From 840d4686164fc68a270fd338969020f5f64fafab Mon Sep 17 00:00:00 2001 From: kjkrishnan Date: Tue, 25 Jun 2024 14:24:41 -0400 Subject: [PATCH] updating links for reference file and fixing versioning --- R/import_fxns.R | 3 +- README.md | 7 ++- docs/articles/domino_object_vignette.html | 52 ++++++++++++++++++++++- docs/pkgdown.yml | 2 +- docs/reference/print-domino-method.html | 4 ++ docs/search.json | 2 +- inst/CITATION | 2 +- inst/pkgdown.yml | 2 +- vignettes/domino_object_vignette.Rmd | 28 +++++++++++- 9 files changed, 90 insertions(+), 12 deletions(-) diff --git a/R/import_fxns.R b/R/import_fxns.R index 5ac636d4..7b13f7b5 100644 --- a/R/import_fxns.R +++ b/R/import_fxns.R @@ -666,7 +666,8 @@ add_rl_column <- function(map, map_ref, conv, new_name) { #' #' Creates a data frame of mean ligand expression for use in plotting a circos #' plot of ligand expression and saving tables of mean expression. -#' +#'us + #' @param x Gene by cell expression matrix #' @param ligands Character vector of ligand genes to be quantified #' @param cell_ident Vector of cell type (identity) names for which to calculate mean ligand gene expression diff --git a/README.md b/README.md index 684583e9..3493e960 100644 --- a/README.md +++ b/README.md @@ -6,9 +6,7 @@ dominoSignal is an updated version of the original [domino](https://github.com/E ### Installation -dominoSignal is undergoing active development where aspects of how data is used, analyzed, and interpreted is subject to change as new features and fixes are implemented. **v0.99.1** of dominoSignal serves as the first stable development version during these active updates for reproducible usage. - -dominoSignal is the continuation of Domino software hosted on the [Elisseeff-Lab GitHub](https://github.com/Elisseeff-Lab/domino). The most up to date stable version is on the [FertigLab GitHub](https://github.com/FertigLab). This version of dominoSignal can be installed using the remotes package. +dominoSignal is the continuation of Domino software hosted on the [Elisseeff-Lab GitHub](https://github.com/Elisseeff-Lab/domino). dominoSignal is undergoing active development where aspects of how data is used, analyzed, and interpreted is subject to change as new features and fixes are implemented. The most up to date stable version is on the [FertigLab GitHub](https://github.com/FertigLab). This version of dominoSignal can be installed using the remotes package. ```r if(!require(remotes)){ @@ -35,7 +33,8 @@ If you use our package in your analysis, please cite us: > Cherry C, Maestas DR, Han J, Andorko JI, Cahan P, Fertig EJ, Garmire LX, Elisseeff JH. Computational reconstruction of the signalling networks surrounding implanted biomaterials from single-cell transcriptomics. Nat Biomed Eng. 2021 Oct;5(10):1228-1238. doi: 10.1038/s41551-021-00770-5. Epub 2021 Aug 2. PMID: 34341534; PMCID: PMC9894531. -> Cherry C, Mitchell J, Nagaraj S, Krishnan K, Lvovs D, Fertig E, Elisseeff J (2024). dominoSignal: Cell Communication Analysis for Single Cell RNA Sequencing. R package version 0.99.1. +> Cherry C, Mitchell J, Nagaraj S, Krishnan K, Lvovs D, Fertig E, Elisseeff J (2024). dominoSignal: Cell Communication Analysis for Single Cell RNA Sequencing. R package version 0.99.2. ### Contact Us If you find any bugs or have questions, please let us know [here](https://github.com/FertigLab/dominoSignal/issues). +tat \ No newline at end of file diff --git a/docs/articles/domino_object_vignette.html b/docs/articles/domino_object_vignette.html index c8d79a2a..7fca553e 100644 --- a/docs/articles/domino_object_vignette.html +++ b/docs/articles/domino_object_vignette.html @@ -116,7 +116,55 @@
Releases

Object contents

-

There is a great deal of information stored with the domino object class: - Input Data - Information about the database used to construct the rl_map - Inputted counts matrix - Inputted z-scored counts matrix - Inputted cluster labels - Inputted transcription factor activation scores - Calculated values - Differential expression p-values of transcription factors in each cluster - Correlation values between ligands and receptors - Median correlation between components of receptor complexes - Linkages - Complexes show the component genes of any complexes in the rl map - Receptor - ligand linkages as determined from the rl map - Transcription factor - target linkages as determined from the SCENIC analysis (or other regulon inference method) - Transcription factors that are differentially expressed in each cluster - Transcription factors that are correlated with receptors - Transcription factors that are correlated with receptors in each cluster - Receptors which are active in each cluster - Ligands that may activate a receptor in a given cluster (so-called incoming ligands; these may include ligands from outside the data set) - Signaling matrices - For each cluster, incoming ligands and the clusters within the data set that they are coming from - A summary of signaling between all clusters - Miscellaneous Information - Build information, which includes the parameters used to build the object in the build_domino() functions - The pared down receptor ligand map information used in building the object - The percent expression of receptors within each cluster

+

There is a great deal of information stored with the domino object class. The domino object is an S4 class object that contains a variety of information about the data set used to build the object, the calculated values, and the linkages between receptors, ligands, and transcription factors. The object is structured as follows (with some examples of the information stored within each slot:

+

For commonly accessed information (the number of cells, clusters, and some build information), the show and print methods for domino objects can be used.

 dom
@@ -548,7 +596,7 @@ 
Linkages#> [25] "CXCL12"                "CXCL14"                "CD58"

If, for some reason, you find yourself in need of the entire linkage structure (not recommended), it can be accessed through its slot name; domino objects are S4 objects.

-all_linkages <- slot(dom, "linkages")
+all_linkages <- slot(dom, "linkages")
 # Names of all sub-structures:
 names(all_linkages)
 #> [1] "complexes"          "rec_lig"            "tf_targets"        
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 83f51e23..14165eea 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -7,7 +7,7 @@ articles:
   dominoSignal: dominoSignal.html
   domino_object_vignette: domino_object_vignette.html
   plotting_vignette: plotting_vignette.html
-last_built: 2024-06-25T17:33Z
+last_built: 2024-06-25T18:20Z
 urls:
   reference: https://FertigLab.github.io/dominoSignal/reference
   article: https://FertigLab.github.io/dominoSignal/articles
diff --git a/docs/reference/print-domino-method.html b/docs/reference/print-domino-method.html
index fb350e21..2b3e1625 100644
--- a/docs/reference/print-domino-method.html
+++ b/docs/reference/print-domino-method.html
@@ -80,6 +80,10 @@ 
Arguments
     
Value

diff --git a/docs/search.json b/docs/search.json
index 220c87b8..f3fcd0cc 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":"https://FertigLab.github.io/dominoSignal/articles/cellphonedb_vignette.html","id":"file-downloads-for-cellphonedb","dir":"Articles","previous_headings":"","what":"File Downloads for CellPhoneDB:","title":"Using the CellPhoneDB Database","text":"Database files CellPhoneDB v4.0.0 human scRNAseq data can installed public Github repository Tiechmann Group developed CellPhoneDB. facilitate use files format used dominoSignal, include helper function, create_rl_map_cellphonedb(), automatically parses files CellPhoneDB database arrive rl_map format. information use files dominoSignal pipeline, please see Getting Started page. learn use SCENIC TF activation scoring, please see SCENIC TF Activation Scoring tutorial. Vignette Build Information Date last built session information:","code":"# URL for desired version of CellPhoneDB cellphone_url <- \"https://github.com/ventolab/cellphonedb-data/archive/refs/tags/v4.0.0.tar.gz\"  # download compressed database cellphone_tar <- paste0(temp_dir, \"/cellphoneDB_v4.tar.gz\") download.file(url = cellphone_url, destfile = cellphone_tar)  # move contents of the compressed file to a new directory untar(tarfile = cellphone_tar, exdir = cellphone_dir) cellphone_data <- paste0(cellphone_dir, \"/cellphonedb-data-4.0.0/data\") Sys.Date() #> [1] \"2024-06-25\" sessionInfo() #> R version 4.2.1 (2022-06-23) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #>  #> Matrix products: default #> BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 #> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 #>  #> locale: #>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               #>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     #>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    #>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  #>  [9] LC_ADDRESS=C               LC_TELEPHONE=C             #> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C        #>  #> attached base packages: #> [1] stats     graphics  grDevices utils     datasets  methods   base      #>  #> loaded via a namespace (and not attached): #>  [1] digest_0.6.35     R6_2.5.1          lifecycle_1.0.4   jsonlite_1.8.8    #>  [5] formatR_1.14      magrittr_2.0.3    evaluate_0.23     rlang_1.1.3       #>  [9] cachem_1.0.8      cli_3.6.2         fs_1.6.3          jquerylib_0.1.4   #> [13] bslib_0.6.1       ragg_1.3.0        vctrs_0.6.5       rmarkdown_2.26    #> [17] pkgdown_2.0.7     textshaping_0.3.7 desc_1.4.3        tools_4.2.1       #> [21] purrr_1.0.2       yaml_2.3.8        xfun_0.42         fastmap_1.1.1     #> [25] compiler_4.2.1    systemfonts_1.0.6 memoise_2.0.1     htmltools_0.5.7   #> [29] knitr_1.45        sass_0.4.8"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"options-and-setup","dir":"Articles","previous_headings":"","what":"Options and Setup","title":"Get Started with dominoSignal","text":"Libraries set Data used vignettes can downloaded Zenodo","code":"set.seed(42)  library(dominoSignal) library(SingleCellExperiment) library(plyr) library(circlize) library(ComplexHeatmap) library(knitr) # BiocFileCache helps with managing files across sessions bfc <- BiocFileCache::BiocFileCache(ask = FALSE) data_url <- \"https://zenodo.org/records/10951634/files\"  # download the reduced pbmc files preprocessed PBMC 3K scRNAseq data set as a # Seurat object pbmc_url <- paste0(data_url, \"/pbmc3k_sce.rds\") pbmc <- BiocFileCache::bfcrpath(bfc, pbmc_url)  # download scenic results scenic_auc_url <- paste0(data_url, \"/auc_pbmc_3k.csv\") scenic_auc <- BiocFileCache::bfcrpath(bfc, scenic_auc_url)  scenic_regulon_url <- paste0(data_url, \"/regulons_pbmc_3k.csv\") scenic_regulon <- BiocFileCache::bfcrpath(bfc, scenic_regulon_url)  # download CellPhoneDB files cellphone_url <- \"https://github.com/ventolab/cellphonedb-data/archive/refs/tags/v4.0.0.tar.gz\" cellphone_tar <- BiocFileCache::bfcrpath(bfc, cellphone_url) cellphone_dir <- paste0(tempdir(), \"/cellphone\") untar(tarfile = cellphone_tar, exdir = cellphone_dir) cellphone_data <- paste0(cellphone_dir, \"/cellphonedb-data-4.0.0/data\")   # directory for created inputs to pySCENIC and dominoSignal input_dir <- paste0(tempdir(), \"/inputs\") if (!dir.exists(input_dir)) {     dir.create(input_dir) }"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"data-preparation","dir":"Articles","previous_headings":"","what":"Data preparation","title":"Get Started with dominoSignal","text":"Analysis cell-cell communication dominoSignal often follows initial processing annotation scRNAseq data. used OSCA workflow prepare data set analysis dominoSignal. complete processing script available data-raw directory dominoSignal package. processed data can downloaded Zenodo.","code":"pbmc <- readRDS(pbmc)"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Get Started with dominoSignal","text":"Installation dominoSignal Github can achieved using remotes package.","code":"if (!require(remotes)) {     install.packages(\"remotes\") } remotes::install_github(\"FertigLab/dominoSignal\")"},{"path":[]},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"loading-scenic-results","dir":"Articles","previous_headings":"Loading TF and R - L data","what":"Loading SCENIC Results","title":"Get Started with dominoSignal","text":"TF activities required input dominoSignal, regulons learned SCENIC important optional input needed annotate TF-target interactions prune TF-receptor linkages receptor target TF. prevents distinction receptor expression driving TF activity TF inducing receptor’s expression. initial regulons data frame read R ctx function two rows column names need replaced one succinct description. dominoSignal changed input format TF regulons list storing vectors target genes regulon, names list TF genes. facilitates use alternative methods TF activity quantification. provide helper function, create_regulon_list_scenic() easy retrieval TF regulons output pySCENIC ctx function. Users aware AUC matrix SCENIC loaded cell x TF orientation transposed TF x cell orientation. pySCENIC also appends “(+)” TF names converted “…” upon loading R. characters can included without affecting results dominoSignal analysis can confusing querying TF features data. recommend comprehensive removal “…” characters using gsub() function.","code":"regulons <- read.csv(scenic_regulon) auc <- read.table(scenic_auc, header = TRUE, row.names = 1, stringsAsFactors = FALSE,     sep = \",\") regulons <- regulons[-1:-2, ] colnames(regulons) <- c(\"TF\", \"MotifID\", \"AUC\", \"NES\", \"MotifSimilarityQvalue\", \"OrthologousIdentity\",     \"Annotation\", \"Context\", \"TargetGenes\", \"RankAtMax\") regulon_list <- create_regulon_list_scenic(regulons = regulons) auc_in <- as.data.frame(t(auc)) # Remove pattern '...' from the end of all rownames: rownames(auc_in) <- gsub(\"\\\\.\\\\.\\\\.$\", \"\", rownames(auc_in))"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"load-cellphonedb-database","dir":"Articles","previous_headings":"Loading TF and R - L data","what":"Load CellPhoneDB Database","title":"Get Started with dominoSignal","text":"dominoSignal updated read ligand - receptor data bases uniform data frame format referred receptor-ligand map (rl_map) enable use alternative updated reference databases addition particular version CellPhoneDB’s database older versions Domino. row corresponds ligand - receptor interaction. Genes participating interaction referred “partner ” “partner B” without requiring fixed ordering whether B ligand vice versa. minimum required columns data frame : int_pair: names interacting ligand receptor separated \" & \" gene_A: gene genes encoding partner gene_B: gene genes encoding partner B type_A: (“L”, “R”) - indicates whether partner ligand (“L”) receptor (“R”) type_B: (“L”, “R”) - indicates whether partner B ligand (“L”) receptor (“R”) Additional annotation columns can provided name_A name_B ligands receptors whose name interaction database match names encoding genes. formatting also allows consideration ligand receptor complexes comprised heteromeric combination multiple proteins must co-expressed function. cases, “name_*” column shows name protein complex, “gene_*” column shows names genes encoding components complex separated commas “,”. plotting results build domino object, names interacting ligands receptors used based combined expression complex components. facilitate use formatting CellPhoneDB database, include helper function, create_rl_map_cellphonedb(), automatically parses files CellPhoneDB database arrive rl_map format.","code":"complexes <- read.csv(paste0(cellphone_data, \"/complex_input.csv\"), stringsAsFactors = FALSE) genes <- read.csv(paste0(cellphone_data, \"/gene_input.csv\"), stringsAsFactors = FALSE) interactions <- read.csv(paste0(cellphone_data, \"/interaction_input.csv\"), stringsAsFactors = FALSE) proteins <- read.csv(paste0(cellphone_data, \"/protein_input.csv\"), stringsAsFactors = FALSE)  rl_map <- create_rl_map_cellphonedb(genes = genes, proteins = proteins, interactions = interactions,     complexes = complexes, database_name = \"CellPhoneDB_v4.0\"  # database version used )  knitr::kable(head(rl_map))"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"optional-adding-interactions-manually","dir":"Articles","previous_headings":"Loading TF and R - L data > Load CellPhoneDB Database","what":"Optional: Adding interactions manually","title":"Get Started with dominoSignal","text":"change use rl_map formatting also enables users manually append interactions interest included interaction database need . can attained formatting desired interactions data frame column headers rl_map using rbind() function.","code":"# Integrin complexes are not annotated as receptors in CellPhoneDB_v4.0 # collagen-integrin interactions between cells may be missed unless tables from # the CellPhoneDB reference are edited or the interactions are manually added  col_int_df <- data.frame(int_pair = \"a11b1 complex & COLA1_HUMAN\", name_A = \"a11b1 complex\",     uniprot_A = \"P05556,Q9UKX5\", gene_A = \"ITB1,ITA11\", type_A = \"R\", name_B = \"COLA1_HUMAN\",     uniprot_B = \"P02452,P08123\", gene_B = \"COL1A1,COL1A2\", type_B = \"L\", annotation_strategy = \"manual\",     source = \"manual\", database_name = \"manual\") rl_map_append <- rbind(col_int_df, rl_map) knitr::kable(head(rl_map_append))"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"analysis-with-domino-object","dir":"Articles","previous_headings":"","what":"Analysis with Domino object","title":"Get Started with dominoSignal","text":"dominoSignal analysis takes place two steps. create_domino() initializes domino result object assesses differential TF activity across cell clusters Wilcoxon rank-sum test establishes TF-receptor linkages based Spearman correlation TF activities receptor expression across queried data set. build_domino() sets parameters TFs receptors called active within cell cluster aggregates scaled expression ligands capable interacting active receptors assessment ligand type cellular source triggering activation receptor.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"required-inputs-from-data-set","dir":"Articles","previous_headings":"Analysis with Domino object","what":"Required inputs from data set","title":"Get Started with dominoSignal","text":"dominoSignal infers active receipt signals via receptors based correlation receptor’s expression TF activity across data set differential activity TF within cell cluster. Correlations conducted using scaled expression values rather raw counts normalized counts. assessment receptor activity per cell type basis, named vector cell cluster assignments, names cell barcodes matching expression matrix, provided. Assessing signaling based categorical groupings cells can achieved passing groupings “clusters” build_domino() place cell types. dominoSignal accepts matrix counts, matrix scaled counts, named vector cell cluster labels factors. Shown extract elements SingleCellExperiment object. Note since data scaled gene, genes expression cell need removed. Note: Ligand receptor expression can assessed genes included z_scores matrix. Many scRNAseq analysis pipelines recommend storing genes high variance scaled expression slots data objects, thereby missing many genes encoding ligands receptors. Ensure genes interest included rows z_scores matrix. Scaled expression calculated genes PBMC data set removal genes expressed less three cells.","code":"counts = assay(pbmc, \"counts\") logcounts = assay(pbmc, \"logcounts\") logcounts = logcounts[rowSums(logcounts) > 0, ] z_scores = t(scale(t(logcounts))) clusters = factor(pbmc$cell_type) names(clusters) = colnames(pbmc)"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"create-domino-object","dir":"Articles","previous_headings":"Analysis with Domino object","what":"Create Domino object","title":"Get Started with dominoSignal","text":"point, create_domino() function can used make object. Parameters note include “use_clusters” required assess signaling cell types rather linkage TFs receptors broadly across data set. “use_complexes” decides receptors function heteromeric complexes considered testing linkages TFs receptors. TRUE, receptor complex linked TF majority component genes meet Spearman correlation threshold. “remove_rec_dropout” decides whether receptor values zero considered correlation calculations; measure intended reduce effect dropout receptors low expression. run create_domino() matrix vector inputs: information stored domino object access , please see vignette structure domino objects.","code":"pbmc_dom <- create_domino(rl_map = rl_map, features = auc_in, counts = counts, z_scores = z_scores,     clusters = clusters, tf_targets = regulon_list, use_clusters = TRUE, use_complexes = TRUE,     remove_rec_dropout = FALSE) # rl_map: receptor - ligand map data frame features: TF activation scores (AUC # matrix) counts: counts matrix z_scores: scaled expression data clusters: # named vector of cell cluster assignments tf_targets: list of TFs and their # regulons use_clusters: assess receptor activation and ligand expression on a # per-cluster basis use_complexes: include receptors and genes that function as # a complex in results remove_rec_dropout: whether to remove zeroes from # correlation calculations"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"build-domino-network","dir":"Articles","previous_headings":"Analysis with Domino object","what":"Build Domino Network","title":"Get Started with dominoSignal","text":"build_domino() finalizes construction domino object setting parameters identifying TFs differential activation clusters, receptor linkage TFs based magnitude positive correlation, minimum percentage cells within cluster expression receptor receptor called active. also options thresholds number TFs may called active cluster number receptors may linked one TF. thresholds n TFs m receptors, bottom n TFs lowest p-values Wilcoxon rank sum test top m receptors Spearman correlation coefficient chosen. thresholds number receptors TFs can sent infinity (Inf) collect receptors TFs meet statistical significance thresholds.","code":"pbmc_dom <- build_domino(dom = pbmc_dom, min_tf_pval = 0.001, max_tf_per_clust = 25,     max_rec_per_tf = 25, rec_tf_cor_threshold = 0.25, min_rec_percentage = 0.1) # min_tf_pval: Threshold for p-value of DE for TFs rec_tf_cor_threshold: # Minimum correlation between receptor and TF min_rec_percentage: Minimum # percent of cells that must express receptor pbmc_dom_all <- build_domino(dom = pbmc_dom, min_tf_pval = 0.001, max_tf_per_clust = Inf,     max_rec_per_tf = Inf, rec_tf_cor_threshold = 0.25, min_rec_percentage = 0.1)"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"visualization-of-domino-results","dir":"Articles","previous_headings":"","what":"Visualization of Domino Results","title":"Get Started with dominoSignal","text":"Multiple functions available visualize intracellular networks receptors TFs ligand - receptor mediated intercellular networks cell types.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"summarize-tf-activity-and-linkage","dir":"Articles","previous_headings":"Visualization of Domino Results","what":"Summarize TF Activity and Linkage","title":"Get Started with dominoSignal","text":"Enrichment TF activities cell types can visualized feat_heatmap() plots data set-wide TF activity scores heatmap.","code":"feat_heatmap(pbmc_dom, norm = TRUE, bool = FALSE, use_raster = FALSE, row_names_gp = grid::gpar(fontsize = 4))"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"cumulative-signaling-between-cell-types","dir":"Articles","previous_headings":"Visualization of Domino Results","what":"Cumulative signaling between cell types","title":"Get Started with dominoSignal","text":"cumulative degree signaling clusters assessed sum scaled expression ligands targeting active receptors another cluster. can visualized graph format using signaling_network() function. Nodes represent cell cluster edges scale magnitude signaling clusters. color edge corresponds sender cluster signal.  Signaling networks can also drawn edges rendering signals directed towards given cell type signals one cell type directed others. see options use, well plotting functions options, please see plotting vignette.","code":"signaling_network(pbmc_dom, edge_weight = 0.5, max_thresh = 3)"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"specific-signaling-interactions-between-clusters","dir":"Articles","previous_headings":"Visualization of Domino Results","what":"Specific Signaling Interactions between Clusters","title":"Get Started with dominoSignal","text":"Beyond aggregated degree signaling cell types, degrees signaling specific ligand - receptor interactions can assessed. gene_network() provides graph linkages active TFs cluster, linked receptors cluster, possible ligands active receptors.  New dominoSignal, gene_network() can used two clusters determine possible ligands given receptor expressed putative outgoing signaling cluster.  comprehensive assessment ligand expression targeting active receptors given cluster can assessed incoming_signaling_heatmap().  Another form comprehensive ligand expression assessment available individual active receptors form circos plots new dominoSignal. outer arcs correspond clusters domino object inner arcs representing possible ligand plotted receptor. Arcs drawn ligands cell type receptor ligand expressed specified threshold. Arc widths correspond mean express ligand cluster widest arc width scaling maximum expression ligand within data.","code":"gene_network(pbmc_dom, clust = \"dendritic_cell\", layout = \"grid\") gene_network(pbmc_dom, clust = \"dendritic_cell\", OutgoingSignalingClust = \"CD14_monocyte\",     layout = \"grid\") incoming_signaling_heatmap(pbmc_dom, rec_clust = \"dendritic_cell\", max_thresh = 2.5,     use_raster = FALSE) circos_ligand_receptor(pbmc_dom, receptor = \"CD74\")"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"continued-development","dir":"Articles","previous_headings":"","what":"Continued Development","title":"Get Started with dominoSignal","text":"Since dominoSignal package still developed, new functions features implemented future versions. meantime, put together information plotting domino object structure like explore package’s functionality. Additionally, find bugs, questions, want share idea, please let us know . Vignette Build Information Date last built session information:","code":"Sys.Date() #> [1] \"2024-06-25\" sessionInfo() #> R version 4.2.1 (2022-06-23) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #>  #> Matrix products: default #> BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 #> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 #>  #> locale: #>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               #>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     #>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    #>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  #>  [9] LC_ADDRESS=C               LC_TELEPHONE=C             #> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C        #>  #> attached base packages: #> [1] grid      stats4    stats     graphics  grDevices utils     datasets  #> [8] methods   base      #>  #> other attached packages: #>  [1] knitr_1.45                  ComplexHeatmap_2.14.0       #>  [3] circlize_0.4.16             plyr_1.8.9                  #>  [5] SingleCellExperiment_1.20.1 SummarizedExperiment_1.28.0 #>  [7] Biobase_2.58.0              GenomicRanges_1.50.2        #>  [9] GenomeInfoDb_1.34.9         IRanges_2.32.0              #> [11] S4Vectors_0.36.2            BiocGenerics_0.44.0         #> [13] MatrixGenerics_1.10.0       matrixStats_1.2.0           #> [15] dominoSignal_0.99.2         #>  #> loaded via a namespace (and not attached): #>   [1] bitops_1.0-7           fs_1.6.3               bit64_4.0.5            #>   [4] filelock_1.0.3         doParallel_1.0.17      RColorBrewer_1.1-3     #>   [7] progress_1.2.3         httr_1.4.7             backports_1.4.1        #>  [10] tools_4.2.1            bslib_0.6.1            utf8_1.2.4             #>  [13] R6_2.5.1               DBI_1.2.2              colorspace_2.1-0       #>  [16] GetoptLong_1.0.5       withr_3.0.0            tidyselect_1.2.1       #>  [19] prettyunits_1.2.0      bit_4.0.5              curl_5.2.1             #>  [22] compiler_4.2.1         textshaping_0.3.7      cli_3.6.2              #>  [25] formatR_1.14           Cairo_1.6-2            xml2_1.3.6             #>  [28] DelayedArray_0.24.0    desc_1.4.3             sass_0.4.8             #>  [31] scales_1.3.0           rappdirs_0.3.3         pkgdown_2.0.7          #>  [34] systemfonts_1.0.6      stringr_1.5.1          digest_0.6.35          #>  [37] rmarkdown_2.26         XVector_0.38.0         pkgconfig_2.0.3        #>  [40] htmltools_0.5.7        highr_0.10             dbplyr_2.5.0           #>  [43] fastmap_1.1.1          rlang_1.1.3            GlobalOptions_0.1.2    #>  [46] RSQLite_2.3.5          shape_1.4.6.1          jquerylib_0.1.4        #>  [49] generics_0.1.3         jsonlite_1.8.8         car_3.1-2              #>  [52] dplyr_1.1.4            RCurl_1.98-1.14        magrittr_2.0.3         #>  [55] GenomeInfoDbData_1.2.9 Matrix_1.6-5           munsell_0.5.0          #>  [58] Rcpp_1.0.12            fansi_1.0.6            abind_1.4-5            #>  [61] lifecycle_1.0.4        stringi_1.8.3          yaml_2.3.8             #>  [64] carData_3.0-5          zlibbioc_1.44.0        BiocFileCache_2.11.2   #>  [67] blob_1.2.4             parallel_4.2.1         crayon_1.5.2           #>  [70] lattice_0.22-5         Biostrings_2.66.0      hms_1.1.3              #>  [73] KEGGREST_1.38.0        magick_2.8.3           pillar_1.9.0           #>  [76] igraph_2.0.3           ggpubr_0.6.0           rjson_0.2.21           #>  [79] ggsignif_0.6.4         codetools_0.2-19       biomaRt_2.54.1         #>  [82] XML_3.99-0.16.1        glue_1.7.0             evaluate_0.23          #>  [85] png_0.1-8              vctrs_0.6.5            foreach_1.5.2          #>  [88] tidyr_1.3.1            gtable_0.3.4           purrr_1.0.2            #>  [91] clue_0.3-65            ggplot2_3.5.0          cachem_1.0.8           #>  [94] xfun_0.42              broom_1.0.5            rstatix_0.7.2          #>  [97] ragg_1.3.0             tibble_3.2.1           iterators_1.0.14       #> [100] AnnotationDbi_1.60.2   memoise_2.0.1          cluster_2.1.6"},{"path":"https://FertigLab.github.io/dominoSignal/articles/domino_object_vignette.html","id":"object-contents","dir":"Articles","previous_headings":"","what":"Object contents","title":"Interacting with domino Objects","text":"great deal information stored domino object class: - Input Data - Information database used construct rl_map - Inputted counts matrix - Inputted z-scored counts matrix - Inputted cluster labels - Inputted transcription factor activation scores - Calculated values - Differential expression p-values transcription factors cluster - Correlation values ligands receptors - Median correlation components receptor complexes - Linkages - Complexes show component genes complexes rl map - Receptor - ligand linkages determined rl map - Transcription factor - target linkages determined SCENIC analysis (regulon inference method) - Transcription factors differentially expressed cluster - Transcription factors correlated receptors - Transcription factors correlated receptors cluster - Receptors active cluster - Ligands may activate receptor given cluster (-called incoming ligands; may include ligands outside data set) - Signaling matrices - cluster, incoming ligands clusters within data set coming - summary signaling clusters - Miscellaneous Information - Build information, includes parameters used build object build_domino() functions - pared receptor ligand map information used building object - percent expression receptors within cluster commonly accessed information (number cells, clusters, build information), show print methods domino objects can used.","code":"dom #> A domino object of 2607 cells #> Built with signaling between 9 clusters print(dom)"},{"path":"https://FertigLab.github.io/dominoSignal/articles/domino_object_vignette.html","id":"access-functions","dir":"Articles","previous_headings":"","what":"Access functions","title":"Interacting with domino Objects","text":"facilitate access information stored domino object, provided collection functions retrieve specific items. functions begin “dom_” can listed using ls().","code":"ls(\"package:dominoSignal\", pattern = \"^dom_\") #>  [1] \"dom_clusters\"      \"dom_correlations\"  \"dom_counts\"        #>  [4] \"dom_database\"      \"dom_de\"            \"dom_info\"          #>  [7] \"dom_linkages\"      \"dom_network_items\" \"dom_signaling\"     #> [10] \"dom_tf_activation\" \"dom_zscores\""},{"path":"https://FertigLab.github.io/dominoSignal/articles/domino_object_vignette.html","id":"input-data","dir":"Articles","previous_headings":"Access functions","what":"Input data","title":"Interacting with domino Objects","text":"creating domino object create_domino() function, several inputs required stored domino object . include cluster labels, counts matrix, z-scored counts matrix, transcription factor activation scores, R-L database used create_rl_map_cellphonedb(). example, access cluster names domino object: Setting argument labels = TRUE return vector cluster labels cell rather unique cluster names. access counts: z-scored counts: transcription factor activation scores can similarly accessed: Information database referenced ligand - receptor pairs composition protein complexes can extracted dom_database() function. default, function returns name(s) database(s) used: like view entire ligand - receptor map, set name_only = FALSE:","code":"dom_clusters(dom) #> [1] \"B_cell\"            \"CD14_monocyte\"     \"CD16_monocyte\"     #> [4] \"CD8_T_cell\"        \"dendritic_cell\"    \"memory_CD4_T_cell\" #> [7] \"naive_CD4_T_cell\"  \"NK_cell\"           \"Platelet\" count_matrix <- dom_counts(dom) knitr::kable(count_matrix[1:5, 1:5]) z_matrix <- dom_zscores(dom) knitr::kable(z_matrix[1:5, 1:5]) activation_matrix <- dom_tf_activation(dom) knitr::kable(activation_matrix[1:5, 1:5]) dom_database(dom) #> [1] \"CellPhoneDB_v4.0\" db_matrix <- dom_database(dom, name_only = FALSE) knitr::kable(db_matrix[1:5, 1:5])"},{"path":"https://FertigLab.github.io/dominoSignal/articles/domino_object_vignette.html","id":"calculations","dir":"Articles","previous_headings":"Access functions","what":"Calculations","title":"Interacting with domino Objects","text":"Active transcription factors cluster determined conducting Wilcoxon rank sum tests transcription factor transcription factor activity scores amongst cells cluster tested activity scores cells outside cluster. p-values one-sided test greater activity within cluster compared cells can accessed dom_de() function. Linkage receptors transcription factors assessed Spearman correlation transcription factor activity scores scaled expression receptor-encoding genes across cells data set. Spearman coefficients can accessed dom_correlations() function. Setting type “complex” return median correlation components receptor complexes; default (“rl”) return receptor - ligand correlations.","code":"de_matrix <- dom_de(dom) knitr::kable(de_matrix[1:5, 1:5]) cor_matrix <- dom_correlations(dom) knitr::kable(cor_matrix[1:5, 1:5])"},{"path":"https://FertigLab.github.io/dominoSignal/articles/domino_object_vignette.html","id":"linkages","dir":"Articles","previous_headings":"Access functions","what":"Linkages","title":"Interacting with domino Objects","text":"Linkages ligands, receptors, transcription factors can accessed several different ways, depending specific link scope desired. dom_linkages() function three arguments - first, like access functions, domino object. second, link_type, used specify linkages desired (options complexes, receptor - ligand, tf - target, tf - receptor). third argument, by_cluster, determines whether linkages returned arranged cluster (though change available linkage types tf - receptor, receptor, incoming-ligand). example, access complexes used across dataset: view incoming ligands cluster: , reason, find need entire linkage structure (recommended), can accessed slot name; domino objects S4 objects. Alternately, obtain simplified list receptors, ligands, /features domino object, use dom_network_items() function. pull transcription factors associated dendritic cell cluster:","code":"complex_links <- dom_linkages(dom, link_type = \"complexes\") # Look for components of NODAL receptor complex complex_links$NODAL_receptor #> NULL incoming_links <- dom_linkages(dom, link_type = \"incoming-ligand\", by_cluster = TRUE) # Check incoming signals to dendritic cells incoming_links$dendritic_cell #>  [1] \"COPA\"                  \"MIF\"                   \"APP\"                   #>  [4] \"FAM19A4\"               \"TAFA4\"                 \"ANXA1\"                 #>  [7] \"CD99\"                  \"integrin_aVb3_complex\" \"integrin_a4b1_complex\" #> [10] \"BMP8B\"                 \"PLAU\"                  \"CSF3\"                  #> [13] \"CXCL9\"                 \"HLA-F\"                 \"CD1D\"                  #> [16] \"INS\"                   \"IL34\"                  \"CSF1\"                  #> [19] \"CSF2\"                  \"CTLA4\"                 \"CD28\"                  #> [22] \"GRN\"                   \"TNF\"                   \"LTA\"                   #> [25] \"CXCL12\"                \"CXCL14\"                \"CD58\" all_linkages <- slot(dom, \"linkages\") # Names of all sub-structures: names(all_linkages) #> [1] \"complexes\"          \"rec_lig\"            \"tf_targets\"         #> [4] \"clust_tf\"           \"tf_rec\"             \"clust_tf_rec\"       #> [7] \"clust_rec\"          \"clust_incoming_lig\" dc_tfs <- dom_network_items(dom, \"dendritic_cell\", return = \"features\") head(dc_tfs) #> [1] \"ATF3\"  \"CEBPD\" \"FOSB\"  \"STAT6\" \"KLF4\"  \"CEBPA\""},{"path":"https://FertigLab.github.io/dominoSignal/articles/domino_object_vignette.html","id":"signaling-matrices","dir":"Articles","previous_headings":"Access functions","what":"Signaling Matrices","title":"Interacting with domino Objects","text":"averaged z-scored expression ligands receptors different clusters can accessed matrix form. view signaling specific cluster clusters, set cluster argument cluster name.","code":"signal_matrix <- dom_signaling(dom) knitr::kable(signal_matrix) dc_matrix <- dom_signaling(dom, \"dendritic_cell\") knitr::kable(dc_matrix)"},{"path":"https://FertigLab.github.io/dominoSignal/articles/domino_object_vignette.html","id":"build-information","dir":"Articles","previous_headings":"Access functions","what":"Build information","title":"Interacting with domino Objects","text":"keep track options set running build_domino(), stored within domino object . view options, use dom_info() function.","code":"dom_info(dom) #> $create #> [1] TRUE #>  #> $build #> [1] TRUE #>  #> $build_variables #>     max_tf_per_clust          min_tf_pval       max_rec_per_tf  #>               25.000                0.001               25.000  #> rec_tf_cor_threshold   min_rec_percentage  #>                0.250                0.100"},{"path":"https://FertigLab.github.io/dominoSignal/articles/domino_object_vignette.html","id":"continued-development","dir":"Articles","previous_headings":"","what":"Continued Development","title":"Interacting with domino Objects","text":"Since dominoSignal package still developed, new functions features implemented future versions. meantime, put together information plotting example analysis can viewed Getting Started page. Additionally, find bugs, questions, want share idea, please let us know . Vignette Build Information Date last built session information:","code":"Sys.Date() #> [1] \"2024-06-25\" sessionInfo() #> R version 4.2.1 (2022-06-23) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #>  #> Matrix products: default #> BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 #> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 #>  #> locale: #>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               #>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     #>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    #>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  #>  [9] LC_ADDRESS=C               LC_TELEPHONE=C             #> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C        #>  #> attached base packages: #> [1] stats     graphics  grDevices utils     datasets  methods   base      #>  #> other attached packages: #> [1] dominoSignal_0.99.2 #>  #> loaded via a namespace (and not attached): #>   [1] bitops_1.0-7           matrixStats_1.2.0      fs_1.6.3               #>   [4] bit64_4.0.5            filelock_1.0.3         doParallel_1.0.17      #>   [7] RColorBrewer_1.1-3     progress_1.2.3         httr_1.4.7             #>  [10] GenomeInfoDb_1.34.9    backports_1.4.1        tools_4.2.1            #>  [13] bslib_0.6.1            utf8_1.2.4             R6_2.5.1               #>  [16] DBI_1.2.2              BiocGenerics_0.44.0    colorspace_2.1-0       #>  [19] GetoptLong_1.0.5       withr_3.0.0            tidyselect_1.2.1       #>  [22] prettyunits_1.2.0      bit_4.0.5              curl_5.2.1             #>  [25] compiler_4.2.1         textshaping_0.3.7      cli_3.6.2              #>  [28] Biobase_2.58.0         formatR_1.14           xml2_1.3.6             #>  [31] desc_1.4.3             sass_0.4.8             scales_1.3.0           #>  [34] rappdirs_0.3.3         pkgdown_2.0.7          systemfonts_1.0.6      #>  [37] stringr_1.5.1          digest_0.6.35          rmarkdown_2.26         #>  [40] XVector_0.38.0         pkgconfig_2.0.3        htmltools_0.5.7        #>  [43] dbplyr_2.5.0           fastmap_1.1.1          rlang_1.1.3            #>  [46] GlobalOptions_0.1.2    RSQLite_2.3.5          shape_1.4.6.1          #>  [49] jquerylib_0.1.4        generics_0.1.3         jsonlite_1.8.8         #>  [52] car_3.1-2              dplyr_1.1.4            RCurl_1.98-1.14        #>  [55] magrittr_2.0.3         GenomeInfoDbData_1.2.9 Matrix_1.6-5           #>  [58] munsell_0.5.0          Rcpp_1.0.12            S4Vectors_0.36.2       #>  [61] fansi_1.0.6            abind_1.4-5            lifecycle_1.0.4        #>  [64] stringi_1.8.3          yaml_2.3.8             carData_3.0-5          #>  [67] zlibbioc_1.44.0        plyr_1.8.9             BiocFileCache_2.11.2   #>  [70] grid_4.2.1             blob_1.2.4             parallel_4.2.1         #>  [73] crayon_1.5.2           lattice_0.22-5         Biostrings_2.66.0      #>  [76] circlize_0.4.16        hms_1.1.3              KEGGREST_1.38.0        #>  [79] knitr_1.45             ComplexHeatmap_2.14.0  pillar_1.9.0           #>  [82] igraph_2.0.3           ggpubr_0.6.0           rjson_0.2.21           #>  [85] ggsignif_0.6.4         codetools_0.2-19       biomaRt_2.54.1         #>  [88] stats4_4.2.1           XML_3.99-0.16.1        glue_1.7.0             #>  [91] evaluate_0.23          png_0.1-8              vctrs_0.6.5            #>  [94] foreach_1.5.2          tidyr_1.3.1            gtable_0.3.4           #>  [97] purrr_1.0.2            clue_0.3-65            ggplot2_3.5.0          #> [100] cachem_1.0.8           xfun_0.42              broom_1.0.5            #> [103] rstatix_0.7.2          ragg_1.3.0             tibble_3.2.1           #> [106] iterators_1.0.14       AnnotationDbi_1.60.2   memoise_2.0.1          #> [109] IRanges_2.32.0         cluster_2.1.6"},{"path":"https://FertigLab.github.io/dominoSignal/articles/plotting_vignette.html","id":"setup-and-data-load","dir":"Articles","previous_headings":"","what":"Setup and Data Load","title":"Plotting Functions and Options","text":"tutorial, use domino object built Getting Started page. yet built domino object, can following instructions Getting Started page. Instructions load data","code":"set.seed(42) library(dominoSignal) library(patchwork) # BiocFileCache helps with managing files across sessions bfc <- BiocFileCache::BiocFileCache(ask = FALSE) data_url <- \"https://zenodo.org/records/10951634/files/pbmc_domino_built.rds\" tmp_path <- BiocFileCache::bfcrpath(bfc, data_url) dom <- readRDS(tmp_path)"},{"path":[]},{"path":"https://FertigLab.github.io/dominoSignal/articles/plotting_vignette.html","id":"correlations-between-receptors-and-transcription-factors","dir":"Articles","previous_headings":"Heatmaps","what":"Correlations between receptors and transcription factors","title":"Plotting Functions and Options","text":"cor_heatmap() can used show correlations calculated receptors transcription factors.  addition displaying scores correlations, function can also used identify correlations certain value (using bool bool_thresh arguments) identify combinations receptors transcription factors (TFs) connected (argument mark_connections).  subset receptors transcription factors interest, vector either () can passed function.  heatmap functions dominoSignal based ComplexHeatmap::Heatmap() also accept additional arguments meant function. example, argument clustering rows columns heatmap explicitly stated, can still passed ComplexHeatmap::Heatmap() cor_heatmap().","code":"cor_heatmap(dom, title = \"PBMC R-TF Correlations\", column_names_gp = grid::gpar(fontsize = 8)) cor_heatmap(dom, bool = TRUE, bool_thresh = 0.25) cor_heatmap(dom, bool = FALSE, mark_connections = TRUE) receptors <- c(\"CSF1R\", \"CSF3R\", \"CCR7\", \"FCER2\") tfs <- c(\"PAX5\", \"JUNB\", \"FOXJ3\", \"FOSB\") cor_heatmap(dom, feats = tfs, recs = receptors) cor_heatmap(dom, cluster_rows = FALSE, cluster_columns = FALSE, column_title = \"Heatmap Without Clustering\",     column_names_gp = grid::gpar(fontsize = 4))"},{"path":"https://FertigLab.github.io/dominoSignal/articles/plotting_vignette.html","id":"heatmap-of-transcription-factor-activation-scores","dir":"Articles","previous_headings":"Heatmaps","what":"Heatmap of Transcription Factor Activation Scores","title":"Plotting Functions and Options","text":"feat_heatmap() used show transcription factor activation features signaling network.  functions similarly cor_heatmap(), arguments select specific vector features, use boolean view threshold, pass arguments ComplexHeatmap::Heatmap(). Specific function though arguments setting range values visualized one choose normalize scores max value.","code":"feat_heatmap(dom, use_raster = FALSE, row_names_gp = grid::gpar(fontsize = 4)) feat_heatmap(dom, min_thresh = 0.1, max_thresh = 0.6, norm = TRUE, bool = FALSE,     use_raster = FALSE) feat_heatmap(dom, bool = TRUE, use_raster = FALSE)"},{"path":"https://FertigLab.github.io/dominoSignal/articles/plotting_vignette.html","id":"heatmap-of-incoming-signaling-for-a-cluster","dir":"Articles","previous_headings":"Heatmaps","what":"Heatmap of Incoming Signaling for a Cluster","title":"Plotting Functions and Options","text":"incoming_signaling_heatmap() can used visualize cluster average expression ligands capable activating TFs enriched cluster. example, view incoming signaling CD8 T cells:  can also select specific clusters interest signaling CD8 T cells. interested viewing monocyte signaling:  heatmap functions, options available minimum threshold, maximum threshold, whether scale values thresholding, whether normalize matrix, ability pass arguments ComplexHeatmap::Heatmap().","code":"incoming_signaling_heatmap(dom, \"CD8_T_cell\") incoming_signaling_heatmap(dom, \"CD8_T_cell\", clusts = c(\"CD14_monocyte\", \"CD16_monocyte\"))"},{"path":"https://FertigLab.github.io/dominoSignal/articles/plotting_vignette.html","id":"heatmap-of-signaling-between-clusters","dir":"Articles","previous_headings":"Heatmaps","what":"Heatmap of Signaling Between Clusters","title":"Plotting Functions and Options","text":"signaling_heatmap() makes heatmap showing signaling strength ligands cluster receptors cluster based averaged expression.  functions, specific clusters can selected, thresholds can set, normalization methods can selected well.","code":"signaling_heatmap(dom) signaling_heatmap(dom, scale = \"sqrt\") signaling_heatmap(dom, normalize = \"rec_norm\")"},{"path":[]},{"path":"https://FertigLab.github.io/dominoSignal/articles/plotting_vignette.html","id":"network-showing-l---r---tf-signaling-between-clusters","dir":"Articles","previous_headings":"Network Plots","what":"Network showing L - R - TF signaling between clusters","title":"Plotting Functions and Options","text":"gene_network() makes network plot display signaling selected clusters ligands, receptors features associated clusters displayed nodes edges linkages. look signaling CD16 Monocytes CD14 Monocytes:  Options modify plot include adjusting scaling ligands different layouts (legible others).  Additionally, colors can given select genes (example, highlight specific signaling path).","code":"gene_network(dom, clust = \"CD16_monocyte\", OutgoingSignalingClust = \"CD14_monocyte\") gene_network(dom, clust = \"CD16_monocyte\", OutgoingSignalingClust = \"CD14_monocyte\",     lig_scale = 25, layout = \"sphere\") gene_network(dom, clust = \"CD16_monocyte\", OutgoingSignalingClust = \"CD14_monocyte\",     cols = c(CD1D = \"violet\", LILRB2 = \"violet\", FOSB = \"violet\"), lig_scale = 10)"},{"path":"https://FertigLab.github.io/dominoSignal/articles/plotting_vignette.html","id":"network-showing-interaction-strength-across-data","dir":"Articles","previous_headings":"Network Plots","what":"Network Showing Interaction Strength Across Data","title":"Plotting Functions and Options","text":"signaling_network() can used create network plot nodes clusters edges indicate signaling one cluster another.  addition changes scaling, thresholds, layouts, plot can modified selecting incoming outgoing clusters (!). example view signaling CD14 Monocytes clusters:","code":"signaling_network(dom) signaling_network(dom, showOutgoingSignalingClusts = \"CD14_monocyte\", scale = \"none\",     norm = \"none\", layout = \"fr\", scale_by = \"none\", edge_weight = 2, vert_scale = 10)"},{"path":[]},{"path":"https://FertigLab.github.io/dominoSignal/articles/plotting_vignette.html","id":"chord-diagrams-connecting-ligands-and-receptors","dir":"Articles","previous_headings":"Other Types of Plots","what":"Chord Diagrams Connecting Ligands and Receptors","title":"Plotting Functions and Options","text":"new addition dominoSignal, circos_ligand_receptor() creates chord plot showing ligands can activate specific receptor, displaying mean cluster expression ligand width chord.  function can given cluster colors match plots may make data. addition, plot can adjusted changing threshold ligand expression required linkage visualized selecting clusters interest.","code":"circos_ligand_receptor(dom, receptor = \"CD74\") cols <- c(\"red\", \"orange\", \"green\", \"blue\", \"pink\", \"purple\", \"slategrey\", \"firebrick\",     \"hotpink\") names(cols) <- dom_clusters(dom, labels = FALSE) circos_ligand_receptor(dom, receptor = \"CD74\", cell_colors = cols)"},{"path":"https://FertigLab.github.io/dominoSignal/articles/plotting_vignette.html","id":"scatter-plot-to-visualize-correlation","dir":"Articles","previous_headings":"Other Types of Plots","what":"Scatter Plot to Visualize Correlation","title":"Plotting Functions and Options","text":"cor_scatter() can used plot cell based activation selected TF expression receptor. produces scatter plot well line best fit look receptor - TF correlation.  keep mind argument remove_rec_dropout match parameter used domino object built. case, use build parameter, leave value argument default value FALSE.","code":"cor_scatter(dom, \"FOSB\", \"CD74\")"},{"path":"https://FertigLab.github.io/dominoSignal/articles/plotting_vignette.html","id":"continued-development","dir":"Articles","previous_headings":"","what":"Continued Development","title":"Plotting Functions and Options","text":"Since dominoSignal package still developed, new functions features implemented future versions. meantime, view example analysis, see Getting Started page, see domino object structure page get familiar object structure. Additionally, find bugs, questions, want share idea, please let us know . Vignette Build Information Date last built session information:","code":"Sys.Date() #> [1] \"2024-06-25\" sessionInfo() #> R version 4.2.1 (2022-06-23) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #>  #> Matrix products: default #> BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 #> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 #>  #> locale: #>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               #>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     #>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    #>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  #>  [9] LC_ADDRESS=C               LC_TELEPHONE=C             #> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C        #>  #> attached base packages: #> [1] stats     graphics  grDevices utils     datasets  methods   base      #>  #> other attached packages: #> [1] patchwork_1.2.0.9000 dominoSignal_0.99.2  #>  #> loaded via a namespace (and not attached): #>   [1] nlme_3.1-164           bitops_1.0-7           matrixStats_1.2.0      #>   [4] fs_1.6.3               bit64_4.0.5            filelock_1.0.3         #>   [7] doParallel_1.0.17      RColorBrewer_1.1-3     progress_1.2.3         #>  [10] httr_1.4.7             GenomeInfoDb_1.34.9    backports_1.4.1        #>  [13] tools_4.2.1            bslib_0.6.1            utf8_1.2.4             #>  [16] R6_2.5.1               mgcv_1.9-1             DBI_1.2.2              #>  [19] BiocGenerics_0.44.0    colorspace_2.1-0       GetoptLong_1.0.5       #>  [22] withr_3.0.0            tidyselect_1.2.1       prettyunits_1.2.0      #>  [25] bit_4.0.5              curl_5.2.1             compiler_4.2.1         #>  [28] textshaping_0.3.7      cli_3.6.2              Biobase_2.58.0         #>  [31] formatR_1.14           Cairo_1.6-2            xml2_1.3.6             #>  [34] desc_1.4.3             labeling_0.4.3         sass_0.4.8             #>  [37] scales_1.3.0           rappdirs_0.3.3         pkgdown_2.0.7          #>  [40] systemfonts_1.0.6      stringr_1.5.1          digest_0.6.35          #>  [43] rmarkdown_2.26         XVector_0.38.0         pkgconfig_2.0.3        #>  [46] htmltools_0.5.7        highr_0.10             dbplyr_2.5.0           #>  [49] fastmap_1.1.1          rlang_1.1.3            GlobalOptions_0.1.2    #>  [52] RSQLite_2.3.5          farver_2.1.1           shape_1.4.6.1          #>  [55] jquerylib_0.1.4        generics_0.1.3         jsonlite_1.8.8         #>  [58] car_3.1-2              dplyr_1.1.4            RCurl_1.98-1.14        #>  [61] magrittr_2.0.3         GenomeInfoDbData_1.2.9 Matrix_1.6-5           #>  [64] munsell_0.5.0          Rcpp_1.0.12            S4Vectors_0.36.2       #>  [67] fansi_1.0.6            abind_1.4-5            lifecycle_1.0.4        #>  [70] stringi_1.8.3          yaml_2.3.8             carData_3.0-5          #>  [73] zlibbioc_1.44.0        plyr_1.8.9             BiocFileCache_2.11.2   #>  [76] grid_4.2.1             blob_1.2.4             parallel_4.2.1         #>  [79] crayon_1.5.2           lattice_0.22-5         splines_4.2.1          #>  [82] Biostrings_2.66.0      circlize_0.4.16        hms_1.1.3              #>  [85] KEGGREST_1.38.0        magick_2.8.3           knitr_1.45             #>  [88] ComplexHeatmap_2.14.0  pillar_1.9.0           igraph_2.0.3           #>  [91] ggpubr_0.6.0           rjson_0.2.21           ggsignif_0.6.4         #>  [94] codetools_0.2-19       biomaRt_2.54.1         stats4_4.2.1           #>  [97] XML_3.99-0.16.1        glue_1.7.0             evaluate_0.23          #> [100] png_0.1-8              vctrs_0.6.5            foreach_1.5.2          #> [103] tidyr_1.3.1            gtable_0.3.4           purrr_1.0.2            #> [106] clue_0.3-65            ggplot2_3.5.0          cachem_1.0.8           #> [109] xfun_0.42              broom_1.0.5            rstatix_0.7.2          #> [112] ragg_1.3.0             tibble_3.2.1           iterators_1.0.14       #> [115] AnnotationDbi_1.60.2   memoise_2.0.1          IRanges_2.32.0         #> [118] cluster_2.1.6"},{"path":"https://FertigLab.github.io/dominoSignal/articles/tf_scenic_vignette.html","id":"file-downloads-for-scenic","dir":"Articles","previous_headings":"","what":"File Downloads for SCENIC","title":"SCENIC for TF Activation Scoring","text":"tutorial, pySCENIC used method TF activity inference. requires downloading files prior use. , show download files way run pySCENIC generate necessary files use dominoSignal analyis. singularity image SCENIC v0.12.1 can installed DockerHub image source. SCENIC requires list TFs, motif annotations, cisTarget motifs available authors SCENIC human (HGNC), mouse (MGI), fly. following download everything necessary analysis data set HGNC gene labels hg38 genome.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/articles/tf_scenic_vignette.html","id":"reference-files","dir":"Articles","previous_headings":"File Downloads for SCENIC","what":"Reference files","title":"SCENIC for TF Activation Scoring","text":"First set temporary directory store files. build singularity image download reference files temporary directory.","code":"temp_dir <- tempdir() SCENIC_DIR=\"'temp_dir'/scenic\" mkdir ${SCENIC_DIR}  # Build singularity image singularity build \"${SCENIC_DIR}/aertslab-pyscenic-0.12.1.sif\" docker://aertslab/pyscenic:0.12.1  # Matrix containing motifs as rows and genes as columns and ranking position for each gene and motif (based on CRM scores) as values. URLs provided link to v2 feather files required for the 0.12.1 version of pySENIC.  curl \"https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg38/refseq_r80/mc_v10_clust/gene_based/hg38_10kbp_up_10kbp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather\" \\   -o \"${SCENIC_DIR}/hg38_10kbp_up_10kbp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather\"  curl \"https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg38/refseq_r80/mc_v10_clust/gene_based/hg38_500bp_up_100bp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather\" \\   -o \"${SCENIC_DIR}/hg38_500bp_up_100bp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather\"  # List of genes encoding TFs in the HG38 reference genome curl \"https://resources.aertslab.org/cistarget/tf_lists/allTFs_hg38.txt\" \\   -o \"${SCENIC_DIR}/allTFs_hg38.txt\"  # Motif annotations based on the 2017 cisTarget motif collection. Use these files if you are using the mc9nr databases. curl \"https://resources.aertslab.org/cistarget/motif2tf/motifs-v9-nr.hgnc-m0.001-o0.0.tbl\" \\   -o \"${SCENIC_DIR}/motifs-v9-nr.hgnc-m0.001-o0.0.tbl\""},{"path":"https://FertigLab.github.io/dominoSignal/articles/tf_scenic_vignette.html","id":"preparing-preprocessed-data","dir":"Articles","previous_headings":"File Downloads for SCENIC","what":"Preparing Preprocessed Data:","title":"SCENIC for TF Activation Scoring","text":"dominoSignal designed compatible single cell workflows, accepting gene cell matrices counts scaled counts. However, pySCENIC used TF activation inference, requires input cell gene matrix. opposite orientation many R based single cell packages (Seurat SingleCellExperiment) default Python based tools scanpy. RNA counts matrix can saved tab-seperated value (.tsv) comma-sperated value (.csv) file transposing matrix cell gene orientation. example extracting counts SingleCellExperiment object saving .tsv file. tsv csv files inefficient storing large matrices. alternative, counts matrix can saved loom file directly passed pySCENIC format. Generating loom file R requires use loomR package. package automatically handles transposition counts matrix cell gene orientation well. recommend approach passing counts matrix pySCENIC. However, users aware loomR hosted CRAN Bioconductor time vignette’s creation. use pbmc3k\\_counts.loom file tutorial.","code":"pbmc_counts <- assay(pbmc, \"counts\") write.table(t(as.matrix(pbmc_counts)), paste0(input_dir, \"/pbmc3k_counts.tsv\"), sep = \"\\t\",     col.names = NA) library(loomR)  # save loom counts matrix pbmc_counts <- assay(pbmc, \"counts\") pbmc_loom <- loomR::create(filename = paste0(input_dir, \"/pbmc3k_counts.loom\"), data = pbmc_counts) # connection to the loom file must be closed to complete file creation pbmc_loom$close_all()"},{"path":"https://FertigLab.github.io/dominoSignal/articles/tf_scenic_vignette.html","id":"running-scenic","dir":"Articles","previous_headings":"","what":"Running SCENIC","title":"SCENIC for TF Activation Scoring","text":"pySCENIC initiated using bash scripting terminal. analysis consists three steps score genes TF motif enrichment, construct TF regulons consisting genes targeted TFs, arrive AUC scores enrichment regulon gene transcription within cell.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/articles/tf_scenic_vignette.html","id":"grn-construct-tf-modules","dir":"Articles","previous_headings":"Running SCENIC","what":"grn: construct TF-modules","title":"SCENIC for TF Activation Scoring","text":"Co-expression modules used quantify gene-TF adjacencies.","code":"singularity exec \"${SCENIC_DIR}/aertslab-pyscenic-0.12.1.sif\" pyscenic grn \\     \"pbmc3k_counts.loom\" \\     \"${SCENIC_DIR}/allTFs_hg38.txt\" \\     -o \"${SCENIC_DIR}/pbmc_adj_3k.tsv\" \\     --num_workers 6 \\     --seed 123  # Arguments:     # path to the loom file     # list of TFs     # output directory for the adjacency matrix     # number of CPUs to use if multi-core processing is available     # specify a random seed for reproducibility"},{"path":"https://FertigLab.github.io/dominoSignal/articles/tf_scenic_vignette.html","id":"ctx-construct-tf-regulons-with-pruning-based-on-tf-motif-enrichment","dir":"Articles","previous_headings":"Running SCENIC","what":"ctx: construct TF regulons with pruning based on TF motif enrichment","title":"SCENIC for TF Activation Scoring","text":"rankings genes based enrichment TF motifs transcription start site (TSS) considered construction regulons, target genes TF-modules removed lack motifs proximal TSS TF may bind.","code":"singularity exec \"${SCENIC_DIR}/aertslab-pyscenic-0.12.1.sif\" pyscenic ctx \\     \"${SCENIC_DIR}/pbmc_adj.tsv\" \\     \"${SCENIC_DIR}/hg38_10kbp_up_10kbp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather\" \\     \"${SCENIC_DIR}/hg38_500bp_up_100bp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather\" \\     --annotations_fname \"${SCENIC_DIR}/motifs-v9-nr.hgnc-m0.001-o0.0.tbl\" \\     --expression_mtx_fname \"pbmc3k_counts.loom\" \\     --mode \"dask_multiprocessing\" \\     --output \"${SCENIC_DIR}/regulons_pbmc_3k.csv\" \\     --num_workers 1                                                            # Arguments:     # adjacency matrix output from grn     # target rankings of motif enrichment within 10 kb of TSS     # target rankings of motif enrichment within 500 bp upstream and 100 bp downstream of TSS     # TF motif features     # counts matrix loom file     # enable multi-core processing     # output file of learned TF regulons     # number of CPU cores"},{"path":"https://FertigLab.github.io/dominoSignal/articles/tf_scenic_vignette.html","id":"aucell-calculate-tf-activity-scores","dir":"Articles","previous_headings":"Running SCENIC","what":"aucell: calculate TF activity scores","title":"SCENIC for TF Activation Scoring","text":"Enrichment regulon measured Area recovery Curve (AUC) genes define regulon. csv files created analysis can used TF activation inputs dominoSignal analysis. Please see Getting Started page information perform analysis dominoSignal. brief tutorial download necessary files using CellPhoneDB, please see Using CellPhoneDB database vignette. Vignette Build Information Date last built session information:","code":"singularity exec \"${SCENIC_DIR}/aertslab-pyscenic-0.12.1.sif\" pyscenic aucell \\     \"pbmc3k_counts.loom\" \\     \"${SCENIC_DIR}/regulons_pbmc_3k.csv\" \\     -o \"${SCENIC_DIR}/auc_pbmc_3k.csv\"      # Arguments:     # counts matrix loom file     # regulon table output from ctx     # cell x TF matrix of TF enrichment AUC values Sys.Date() #> [1] \"2024-06-25\" sessionInfo() #> R version 4.2.1 (2022-06-23) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #>  #> Matrix products: default #> BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 #> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 #>  #> locale: #>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               #>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     #>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    #>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  #>  [9] LC_ADDRESS=C               LC_TELEPHONE=C             #> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C        #>  #> attached base packages: #> [1] stats     graphics  grDevices utils     datasets  methods   base      #>  #> loaded via a namespace (and not attached): #>  [1] digest_0.6.35     R6_2.5.1          lifecycle_1.0.4   jsonlite_1.8.8    #>  [5] formatR_1.14      magrittr_2.0.3    evaluate_0.23     rlang_1.1.3       #>  [9] cachem_1.0.8      cli_3.6.2         fs_1.6.3          jquerylib_0.1.4   #> [13] bslib_0.6.1       ragg_1.3.0        vctrs_0.6.5       rmarkdown_2.26    #> [17] pkgdown_2.0.7     textshaping_0.3.7 desc_1.4.3        tools_4.2.1       #> [21] purrr_1.0.2       yaml_2.3.8        xfun_0.42         fastmap_1.1.1     #> [25] compiler_4.2.1    systemfonts_1.0.6 memoise_2.0.1     htmltools_0.5.7   #> [29] knitr_1.45        sass_0.4.8"},{"path":"https://FertigLab.github.io/dominoSignal/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Christopher Cherry. Author. Jacob T Mitchell. Author, maintainer. Sushma Nagaraj. Author. Kavita Krishnan. Author. Dmitrijs Lvovs. Author. Elana Fertig. Contributor. Jennifer Elisseeff. Contributor.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Cherry C, Maestas D, Han J, Andorko J, Cahan P, Fertig E, Garmire L, Elisseeff J (2021). “Computational reconstruction signalling networks surrounding implanted biomaterials single-cell transcriptomics.” Nature Biomedical Engineering, 5(10), 1228-1238. doi:10.1038/s41551-021-00770-5, https://doi.org/10.1038%2Fs41551-021-00770-5. Cherry C, Mitchell J, Nagaraj S, Krishnan K, Fertig E, Elisseeff J (2024). dominoSignal: Cell Communication Analysis Single Cell RNA Sequencing. R package version 0.99.1.","code":"@Article{,   title = {Computational reconstruction of the signalling networks surrounding implanted biomaterials from single-cell transcriptomics},   author = {Christopher Cherry and David R. Maestas and Jin Han and James I. Andorko and Patrick Cahan and Elana J. Fertig and Lana X. Garmire and Jennifer H. Elisseeff},   journal = {Nature Biomedical Engineering},   year = {2021},   volume = {5},   number = {10},   pages = {1228-1238},   doi = {10.1038/s41551-021-00770-5},   url = {https://doi.org/10.1038%2Fs41551-021-00770-5}, } @Manual{,   title = {dominoSignal: Cell Communication Analysis for Single Cell RNA Sequencing},   author = {Christopher Cherry and Jacob T Mitchell and Sushma Nagaraj and Kavita Krishnan and Elana Fertig and Jennifer Elisseeff},   year = {2024},   note = {R package version 0.99.1}, }"},{"path":"https://FertigLab.github.io/dominoSignal/index.html","id":"introducing-dominosignal-","dir":"","previous_headings":"","what":"Introducing dominoSignal","title":"Cell Communication from Single Cell RNA Sequencing Data","text":"dominoSignal updated version original domino R package published Nature Biomedical Engineering Computational reconstruction signalling networks surrounding implanted biomaterials single-cell transcriptomics. dominoSignal tool analysis intra- intercellular signaling single cell RNA sequencing data based transcription factor activation receptor ligand linkages.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Cell Communication from Single Cell RNA Sequencing Data","text":"version currently hosted FertigLab GitHub dominoSignal repository forked primary repository hosted Elisseeff-Lab GitHub, can installed using remotes package. current stable version active updates reproducible usage (see changelog information changes).","code":"if (!require(remotes)) {     install.packages(\"remotes\") } remotes::install_github(\"FertigLab/dominoSignal\")"},{"path":"https://FertigLab.github.io/dominoSignal/index.html","id":"usage-overview","dir":"","previous_headings":"","what":"Usage Overview","title":"Cell Communication from Single Cell RNA Sequencing Data","text":"overview dominoSignal might used analysis single cell RNA sequencing data set: Transcription factor activation scores calculated (recommend using pySCENIC, methods can used well). information use SCENIC, please see Using SCENIC TF Activation page. ligand-receptor database used map linkages ligands receptors (recommend using cellphoneDB, methods can used well). information downloading necessary files cellphoneDB, please see Using cellphoneDB Database page. domino object created using counts, z-scored counts, clustering information, data steps 1 2. Parameters maximum number transcription factors receptors minimum correlation threshold (among others) used make cell communication network Communication networks can extracted within domino object visualized using variety plotting functions Please see Getting Started page example analysis includes steps dominoSignal analysis detail, creating domino object parameters building network visualizing domino results. articles include details plotting functions structure domino object.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/add_rl_column.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a column to the RL signaling data frame. — add_rl_column","title":"Adds a column to the RL signaling data frame. — add_rl_column","text":"function adds column internal rl 'map' used map receptor receptor complexes ligand ligand complexes.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/add_rl_column.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a column to the RL signaling data frame. — add_rl_column","text":"","code":"add_rl_column(map, map_ref, conv, new_name)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/add_rl_column.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a column to the RL signaling data frame. — add_rl_column","text":"map RL signaling data frame. map_ref Name column match new data conv Data frame matching current data map new data. new_name Name new column created RL map","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/add_rl_column.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a column to the RL signaling data frame. — add_rl_column","text":"updated RL signaling data frame","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/add_rl_column.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a column to the RL signaling data frame. — add_rl_column","text":"","code":"lr_name <- data.frame(\"abbrev\" = c(\"L\", \"R\"), \"full\" = c(\"Ligand\", \"Receptor\")) rl_map_expanded <- add_rl_column(map = dominoSignal:::rl_map_tiny, map_ref = \"type_A\",  conv = lr_name, new_name = \"type_A_full\")"},{"path":"https://FertigLab.github.io/dominoSignal/reference/build_domino.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate a signaling network for a domino object — build_domino","title":"Calculate a signaling network for a domino object — build_domino","text":"function calculates signaling network. requires domino object preprocessed create_domino returns domino object prepared plotting various plotting functions package.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/build_domino.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate a signaling network for a domino object — build_domino","text":"","code":"build_domino(   dom,   max_tf_per_clust = 5,   min_tf_pval = 0.01,   max_rec_per_tf = 5,   rec_tf_cor_threshold = 0.15,   min_rec_percentage = 0.1 )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/build_domino.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate a signaling network for a domino object — build_domino","text":"dom Domino object create_domino(). max_tf_per_clust Maximum number transcription factors called active cluster. min_tf_pval Minimum p-value differential feature score test call transcription factor active cluster. max_rec_per_tf Maximum number receptors link transcription factor. rec_tf_cor_threshold Minimum Spearman correlation used consider receptor linked transcription factor. Increasing decrease number receptors linked transcription factor. min_rec_percentage Minimum percentage cells cluster expressing receptor receptor linked trancription factors cluster.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/build_domino.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate a signaling network for a domino object — build_domino","text":"domino object signaling network built","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/build_domino.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate a signaling network for a domino object — build_domino","text":"","code":"pbmc_dom_tiny_built <- build_domino(  dom = dominoSignal:::pbmc_dom_tiny, min_tf_pval = .001, max_tf_per_clust = 25,  max_rec_per_tf = 25, rec_tf_cor_threshold = .25, min_rec_percentage = 0.1 )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/check_arg.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input arguments — check_arg","title":"Check input arguments — check_arg","text":"Accepts object rules check ; stops requirements met","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/check_arg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input arguments — check_arg","text":"","code":"check_arg(   arg,   allow_class = NULL,   allow_len = NULL,   allow_range = NULL,   allow_values = NULL,   need_vars = c(NULL),   need_colnames = FALSE,   need_rownames = FALSE,   need_names = FALSE )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/check_arg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input arguments — check_arg","text":"arg argument check allow_class vector allowed classes allow_len vector allowed lengths allow_range range minimum maximum values .e. c(1, 5) allow_values vector allowed values need_vars vector required variables need_colnames vogical whether colnames required need_rownames logical whether rownames required need_names logical whether names required","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/check_arg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input arguments — check_arg","text":"Logical indicating whether argument meets requirements","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/circos_ligand_receptor.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot expression of a receptor's ligands by other cell types as a chord plot — circos_ligand_receptor","title":"Plot expression of a receptor's ligands by other cell types as a chord plot — circos_ligand_receptor","text":"Creates chord plot expression ligands can activate specified receptor chord widths correspond mean ligand expression cluster.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/circos_ligand_receptor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot expression of a receptor's ligands by other cell types as a chord plot — circos_ligand_receptor","text":"","code":"circos_ligand_receptor(   dom,   receptor,   ligand_expression_threshold = 0.01,   cell_idents = NULL,   cell_colors = NULL )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/circos_ligand_receptor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot expression of a receptor's ligands by other cell types as a chord plot — circos_ligand_receptor","text":"dom Domino object undergone network building build_domino() receptor Name receptor active least one cell type domino object ligand_expression_threshold Minimum mean expression value ligand cell type chord rendered cell type receptor cell_idents Vector cell types cluster assignments domino object included plot. cell_colors Named vector color names hex codes names correspond plotted cell types color values","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/circos_ligand_receptor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot expression of a receptor's ligands by other cell types as a chord plot — circos_ligand_receptor","text":"Renders circos plot active graphics device","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/circos_ligand_receptor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot expression of a receptor's ligands by other cell types as a chord plot — circos_ligand_receptor","text":"","code":"#basic usage circos_ligand_receptor(dominoSignal:::pbmc_dom_built_tiny, receptor = \"CXCR3\") #> There are more than one numeric columns in the data frame. Take the #> first two numeric columns and draw the link ends with unequal width. #>  #> Type `circos.par$message = FALSE` to suppress the message.  #specify colors cols = c(\"red\", \"orange\", \"green\", \"blue\", \"pink\", \"purple\", \"slategrey\", \"firebrick\", \"hotpink\") names(cols) = levels(dom_clusters(dominoSignal:::pbmc_dom_built_tiny)) circos_ligand_receptor(dominoSignal:::pbmc_dom_built_tiny, receptor = \"CXCR3\", cell_colors = cols) #> There are more than one numeric columns in the data frame. Take the #> first two numeric columns and draw the link ends with unequal width. #>  #> Type `circos.par$message = FALSE` to suppress the message."},{"path":"https://FertigLab.github.io/dominoSignal/reference/conv_py_bools.html","id":null,"dir":"Reference","previous_headings":"","what":"Change cases of True/False syntax from Python to TRUE/FALSE R syntax — conv_py_bools","title":"Change cases of True/False syntax from Python to TRUE/FALSE R syntax — conv_py_bools","text":"Change cases True/False syntax Python TRUE/FALSE R syntax","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/conv_py_bools.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Change cases of True/False syntax from Python to TRUE/FALSE R syntax — conv_py_bools","text":"","code":"conv_py_bools(obj)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/conv_py_bools.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Change cases of True/False syntax from Python to TRUE/FALSE R syntax — conv_py_bools","text":"obj object converted","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/conv_py_bools.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Change cases of True/False syntax from Python to TRUE/FALSE R syntax — conv_py_bools","text":"converted object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/convert_genes.html","id":null,"dir":"Reference","previous_headings":"","what":"Use biomaRt to convert genes — convert_genes","title":"Use biomaRt to convert genes — convert_genes","text":"function reads vector genes converts genes specified symbol type","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/convert_genes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Use biomaRt to convert genes — convert_genes","text":"","code":"convert_genes(   genes,   from = c(\"ENSMUSG\", \"ENSG\", \"MGI\", \"HGNC\"),   to = c(\"MGI\", \"HGNC\"),   host = \"https://www.ensembl.org\" )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/convert_genes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Use biomaRt to convert genes — convert_genes","text":"genes Vector genes convert. Format gene input (ENSMUSG, ENSG, MGI, HGNC) Format gene output (MGI HGNC) host Host connect . Defaults https://www.ensembl.org following useMart default, can changed archived hosts useMart fails connect.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/convert_genes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Use biomaRt to convert genes — convert_genes","text":"data frame input genes column 1 converted genes column 2","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/cor_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a heatmap of correlation between receptors and transcription factors — cor_heatmap","title":"Create a heatmap of correlation between receptors and transcription factors — cor_heatmap","text":"Creates heatmap correlation values receptors transcription factors either boolean threshold continuous values displayed","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/cor_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a heatmap of correlation between receptors and transcription factors — cor_heatmap","text":"","code":"cor_heatmap(   dom,   bool = FALSE,   bool_thresh = 0.15,   title = TRUE,   feats = NULL,   recs = NULL,   mark_connections = FALSE,   ... )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/cor_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a heatmap of correlation between receptors and transcription factors — cor_heatmap","text":"dom Domino object network built (build_domino()) bool Boolean indicating whether heatmap continuous boolean. boolean bool_thresh used determine define activity positive negative. bool_thresh Numeric indicating threshold separating '' '' feature activity making boolean heatmap. title Either string use title boolean describing whether include title. order pass 'main' parameter  ComplexHeatmap::Heatmap()  must set title FALSE. feats Either vector features include heatmap '' features. left NULL features selected signaling network shown. recs Either vector receptors include heatmap '' receptors. left NULL receptors selected signaling network connected features plotted shown. mark_connections Boolean indicating whether add 'x' cells connected receptor TF. Default FALSE. ... parameters pass  ComplexHeatmap::Heatmap() . Note use 'main' parameter  ComplexHeatmap::Heatmap()  must set title = FALSE use 'annCol' 'annColors' ann_cols must FALSE.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/cor_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a heatmap of correlation between receptors and transcription factors — cor_heatmap","text":"heatmap rendered active graphics device","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/cor_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a heatmap of correlation between receptors and transcription factors — cor_heatmap","text":"","code":"# basic usage cor_heatmap(dominoSignal:::pbmc_dom_built_tiny, title = \"PBMC R-TF Correlations\")  # show correlations above a specific value cor_heatmap(dominoSignal:::pbmc_dom_built_tiny, bool = TRUE, bool_thresh = 0.25)  # identify combinations that are connected cor_heatmap(dominoSignal:::pbmc_dom_built_tiny, bool = FALSE, mark_connections = TRUE)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/cor_scatter.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a correlation plot between TF and receptor — cor_scatter","title":"Create a correlation plot between TF and receptor — cor_scatter","text":"Create correlation plot transcription factor activation score receptor expression","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/cor_scatter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a correlation plot between TF and receptor — cor_scatter","text":"","code":"cor_scatter(dom, tf, rec, remove_rec_dropout = TRUE, ...)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/cor_scatter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a correlation plot between TF and receptor — cor_scatter","text":"dom Domino object network built (build_domino()) tf Target TF plottting AUC score rec Target receptor plotting expression remove_rec_dropout Whether remove cells zero expression plot. match setting build_domino(). ... parameters pass ggpubr::ggscatter().","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/cor_scatter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a correlation plot between TF and receptor — cor_scatter","text":"ggplot scatter plot rendered active graphics device","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/cor_scatter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a correlation plot between TF and receptor — cor_scatter","text":"","code":"cor_scatter(dominoSignal:::pbmc_dom_built_tiny, \"FLI1\",\"CXCR3\")"},{"path":"https://FertigLab.github.io/dominoSignal/reference/count_linkage.html","id":null,"dir":"Reference","previous_headings":"","what":"Count occurrences of linkages across multiple domino results from a linkage summary — count_linkage","title":"Count occurrences of linkages across multiple domino results from a linkage summary — count_linkage","text":"Count occurrences linkages across multiple domino results linkage summary","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/count_linkage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count occurrences of linkages across multiple domino results from a linkage summary — count_linkage","text":"","code":"count_linkage(   linkage_summary,   cluster,   group.by = NULL,   linkage = \"rec_lig\",   subject_names = NULL )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/count_linkage.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count occurrences of linkages across multiple domino results from a linkage summary — count_linkage","text":"linkage_summary linkage_summary() object cluster name cell cluster compared across multiple domino results group.name column linkage_summary@subject_meta group subjects counting. NULL, total counts linkages linkages cluster across subjects given. linkage stored linkage domino object. Can compare 'tfs', 'rec', 'incoming_lig', 'tfs_rec', 'rec_lig' subject_names vector subject_names linkage_summary compared. NULL, subject_names linkage summary included counting.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/count_linkage.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count occurrences of linkages across multiple domino results from a linkage summary — count_linkage","text":"data frame columns unique linkage features counts many times linkage occured across compared domino results. group.used, counts linkages also provided columns named unique values group.variable.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/count_linkage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count occurrences of linkages across multiple domino results from a linkage summary — count_linkage","text":"","code":"count_linkage(   linkage_summary = dominoSignal:::linkage_sum_tiny, cluster = \"C1\",    group.by = \"group\", linkage = \"rec\") #>   feature total_count G1 G2 #> 1      R1           3  3  0 #> 2      R2           4  3  1 #> 3      R3           3  1  2 #> 4      R4           2  1  1"},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_domino.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a domino object and prepare it for network construction — create_domino","title":"Create a domino object and prepare it for network construction — create_domino","text":"function reads receptor ligand signaling database, cell level features kind (ie. output pySCENIC), z-scored single cell data, cluster id single cell data, calculates correlation matrix receptors features (transcription factor module scores using pySCENIC), finds features enriched cluster. return domino object prepared build_domino(), calculate signaling network.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_domino.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a domino object and prepare it for network construction — create_domino","text":"","code":"create_domino(   rl_map,   features,   counts = NULL,   z_scores = NULL,   clusters = NULL,   use_clusters = TRUE,   tf_targets = NULL,   verbose = TRUE,   use_complexes = TRUE,   rec_min_thresh = 0.025,   remove_rec_dropout = TRUE,   tf_selection_method = \"clusters\",   tf_variance_quantile = 0.5 )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_domino.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a domino object and prepare it for network construction — create_domino","text":"rl_map Data frame row describes receptor-ligand interaction required columns gene_A & gene_B including gene names receptor ligand type_A & type_B annotating genes B ligand (L) receptor (R) features Either path csv containing cell level features interest (ie. auc matrix pySCENIC) named matrix cells columns features rows. counts Counts matrix data. used threshold receptors dropout. z_scores Matrix containing z-scored expression data cells cells columns features rows. clusters Named factor containing cell cluster names cells. use_clusters Boolean indicating whether use clusters. tf_targets Optional. list names transcription factors stored values character vectors genes transcription factor's regulon. verbose Boolean indicating whether print progress computation. use_complexes Boolean indicating whether wish use receptor/ligand complexes receptor ligand signaling database. FALSE, receptor/ligand pairs either functions protein complex considered constructing signaling network. rec_min_thresh Minimum expression level receptors cell. Default 0.025 2.5 percent cells data set. important calculating correlation connect receptors transcription activation. threshold low correlation calculations proceed cells non-zero expression. remove_rec_dropout Whether remove receptors 0 expression counts calculating correlations. can reduce false positive correlation calculations receptors high dropout rates. tf_selection_method Selection method target transcription factors. 'clusters' differential expression clusters calculated. 'variable' variable transcription factors selected. '' transcription factors feature matrix used. Default 'clusters'. Note wish use clusters intercellular signaling downstream MUST choose clusters. tf_variance_quantile proportion variable features take using variance threshold features. Default 0.5. Higher numbers keep features. Ignored tf_selection_method 'variable'","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_domino.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a domino object and prepare it for network construction — create_domino","text":"domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_domino.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a domino object and prepare it for network construction — create_domino","text":"","code":"pbmc_dom_tiny_all <- create_domino(  rl_map = dominoSignal:::rl_map_tiny, features = dominoSignal:::auc_tiny,   counts = dominoSignal:::RNA_count_tiny, z_scores = dominoSignal:::RNA_zscore_tiny,  clusters = dominoSignal:::clusters_tiny, tf_targets = dominoSignal:::regulon_list_tiny,   use_clusters = FALSE, use_complexes = FALSE,   rec_min_thresh = 0.1, remove_rec_dropout = TRUE,  tf_selection_method = \"all\") #> Reading in and processing signaling database #> Database provided from source: CellPhoneDB #> Getting z_scores, clusters, and counts #> Calculating correlations #> 1 of 6 #> 2 of 6 #> 3 of 6 #> 4 of 6 #> 5 of 6 #> 6 of 6  pbmc_dom_tiny_clustered <- create_domino(  rl_map = dominoSignal:::rl_map_tiny, features = dominoSignal:::auc_tiny,   counts = dominoSignal:::RNA_count_tiny, z_scores = dominoSignal:::RNA_zscore_tiny,  clusters = dominoSignal:::clusters_tiny, tf_targets = dominoSignal:::regulon_list_tiny,  use_clusters = TRUE, use_complexes = TRUE, remove_rec_dropout = FALSE) #> Reading in and processing signaling database #> Database provided from source: CellPhoneDB #> Getting z_scores, clusters, and counts #> Calculating feature enrichment by cluster #> 1 of 3 #> 2 of 3 #> 3 of 3 #> Calculating correlations #> 1 of 6 #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> 2 of 6 #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> 3 of 6 #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> 4 of 6 #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> 5 of 6 #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> 6 of 6 #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties"},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_regulon_list_scenic.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a list of genes in regulons inferred by SCENIC — create_regulon_list_scenic","title":"Create a list of genes in regulons inferred by SCENIC — create_regulon_list_scenic","text":"Generates list transcription factors genes targeted transcription factor part regulon inferred pySCENIC","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_regulon_list_scenic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a list of genes in regulons inferred by SCENIC — create_regulon_list_scenic","text":"","code":"create_regulon_list_scenic(regulons)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_regulon_list_scenic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a list of genes in regulons inferred by SCENIC — create_regulon_list_scenic","text":"regulons Data frame file path table output ctx function pySCENIC","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_regulon_list_scenic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a list of genes in regulons inferred by SCENIC — create_regulon_list_scenic","text":"list names transcription factors stored values character vectors genes inferred regulons","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_regulon_list_scenic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a list of genes in regulons inferred by SCENIC — create_regulon_list_scenic","text":"","code":"regulon_list_tiny <- create_regulon_list_scenic(regulons = dominoSignal:::regulons_tiny)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_rl_map_cellphonedb.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a receptor - ligand map from a CellPhoneDB signaling database — create_rl_map_cellphonedb","title":"Create a receptor - ligand map from a CellPhoneDB signaling database — create_rl_map_cellphonedb","text":"Generates data frame ligand-receptor interactions CellPhoneDB database annotating genes encoding interacting ligands receptors queried transcriptomic data.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_rl_map_cellphonedb.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a receptor - ligand map from a CellPhoneDB signaling database — create_rl_map_cellphonedb","text":"","code":"create_rl_map_cellphonedb(   genes,   proteins,   interactions,   complexes = NULL,   database_name = \"CellPhoneDB\",   gene_conv = NULL,   gene_conv_host = \"https://www.ensembl.org\",   alternate_convert = FALSE,   alternate_convert_table = NULL )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_rl_map_cellphonedb.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a receptor - ligand map from a CellPhoneDB signaling database — create_rl_map_cellphonedb","text":"genes data frame file path table gene names uniprot, hgnc_symbol, ensembl format CellPhoneDB database format proteins data frame file path table protein features CellPhoneDB format interactions data frame file path table protein-protein interactions CellPhoneDB format complexes optional: data frame file path table protein complexes CellPhoneDB format database_name name database used, stored output gene_conv tuple (, ) (source, target) gene conversion orthologs desired; options ENSMUSG, ENSG, MGI, HGNC gene_conv_host host conversion; default ensembl, also use mirrors desired alternate_convert boolean like use non-ensembl method conversion (must supply table; recommended, use ensembl ) alternate_convert_table supplied table non-ensembl method conversion","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_rl_map_cellphonedb.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a receptor - ligand map from a CellPhoneDB signaling database — create_rl_map_cellphonedb","text":"Data frame row describes possible receptor-ligand interaction","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_rl_map_cellphonedb.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a receptor - ligand map from a CellPhoneDB signaling database — create_rl_map_cellphonedb","text":"","code":"rl_map_tiny <- create_rl_map_cellphonedb(genes = dominoSignal:::genes_tiny,   proteins = dominoSignal:::proteins_tiny, interactions = dominoSignal:::interactions_tiny,   complexes = dominoSignal:::complexes_tiny)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/do_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalize a matrix to its max value by row or column — do_norm","title":"Normalize a matrix to its max value by row or column — do_norm","text":"Normalizes matrix max value row column","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/do_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalize a matrix to its max value by row or column — do_norm","text":"","code":"do_norm(mat, dir)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/do_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalize a matrix to its max value by row or column — do_norm","text":"mat Matrix normalized dir Direction normalize matrix (either \"row\" row \"col\" column)","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/do_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalize a matrix to its max value by row or column — do_norm","text":"normalized matrix direction specified.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_clusters.html","id":null,"dir":"Reference","previous_headings":"","what":"Access clusters — dom_clusters","title":"Access clusters — dom_clusters","text":"function pull cluster information domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_clusters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access clusters — dom_clusters","text":"","code":"dom_clusters(dom, labels = FALSE)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_clusters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access clusters — dom_clusters","text":"dom domino object created create_domino() labels boolean whether return cluster labels cell clusters used inferring communication","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_clusters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access clusters — dom_clusters","text":"vector containing either names clusters used factors cluster label individual cell","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_clusters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access clusters — dom_clusters","text":"","code":"cluster_names <- dom_clusters(dominoSignal:::pbmc_dom_built_tiny) cell_cluster_label <- dom_clusters(dominoSignal:::pbmc_dom_built_tiny, labels = TRUE)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_correlations.html","id":null,"dir":"Reference","previous_headings":"","what":"Access correlations — dom_correlations","title":"Access correlations — dom_correlations","text":"function pull receptor-transcription factor correlations domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_correlations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access correlations — dom_correlations","text":"","code":"dom_correlations(dom, type = \"rl\")"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_correlations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access correlations — dom_correlations","text":"dom domino object created create_domino() type either \"rl\" \"complex\", select receptor-ligand complex correlation matrix","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_correlations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access correlations — dom_correlations","text":"matrix containing correlation values receptor (row) transcription factor (column)","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_correlations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access correlations — dom_correlations","text":"","code":"cor_matrix <- dom_correlations(dominoSignal:::pbmc_dom_built_tiny, \"rl\")"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_counts.html","id":null,"dir":"Reference","previous_headings":"","what":"Access counts — dom_counts","title":"Access counts — dom_counts","text":"function pull gene expression domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_counts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access counts — dom_counts","text":"","code":"dom_counts(dom)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_counts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access counts — dom_counts","text":"dom domino object created create_domino()","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_counts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access counts — dom_counts","text":"matrix containing gene expression values gene (row) cell (column)","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_counts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access counts — dom_counts","text":"","code":"counts <- dom_counts(dominoSignal:::pbmc_dom_built_tiny)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_database.html","id":null,"dir":"Reference","previous_headings":"","what":"Access database — dom_database","title":"Access database — dom_database","text":"function pull database information domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_database.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access database — dom_database","text":"","code":"dom_database(dom, name_only = TRUE)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_database.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access database — dom_database","text":"dom domino object created name_only boolean whether return name database used entire database stored. Default TRUE.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_database.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access database — dom_database","text":"vector unique databases used building domino object data frame includes database information used domino object creation","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_database.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access database — dom_database","text":"","code":"database_name <- dom_database(dominoSignal:::pbmc_dom_built_tiny) full_database <- dom_database(dominoSignal:::pbmc_dom_built_tiny, name_only = FALSE)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_de.html","id":null,"dir":"Reference","previous_headings":"","what":"Access differential expression — dom_de","title":"Access differential expression — dom_de","text":"function pull differential expression p-values domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_de.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access differential expression — dom_de","text":"","code":"dom_de(dom)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_de.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access differential expression — dom_de","text":"dom domino object created create_domino()","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_de.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access differential expression — dom_de","text":"matrix containing p-values differential expression transcription factors (rows) cluster (columns)","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_de.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access differential expression — dom_de","text":"","code":"de_mat <- dom_de(dominoSignal:::pbmc_dom_built_tiny)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_info.html","id":null,"dir":"Reference","previous_headings":"","what":"Access build information — dom_info","title":"Access build information — dom_info","text":"function pull parameters used running build_domino() domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access build information — dom_info","text":"","code":"dom_info(dom)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access build information — dom_info","text":"dom domino object created create_domino()","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access build information — dom_info","text":"list containing booleans whether object created built list build parameters used build_domino() infer signaling network","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_info.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access build information — dom_info","text":"","code":"build_details <- dom_info(dominoSignal:::pbmc_dom_built_tiny)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_linkages.html","id":null,"dir":"Reference","previous_headings":"","what":"Access linkages — dom_linkages","title":"Access linkages — dom_linkages","text":"function pull linkages domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_linkages.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access linkages — dom_linkages","text":"","code":"dom_linkages(   dom,   link_type = c(\"complexes\", \"receptor-ligand\", \"tf-target\", \"tf-receptor\", \"receptor\",     \"incoming-ligand\"),   by_cluster = FALSE )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_linkages.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access linkages — dom_linkages","text":"dom domino object created create_domino() link_type one value (\"complexes\", \"receptor-ligand\", \"tf-target\", \"tf-receptor\", \"receptor\", \"incoming-ligand\") used select desired type linkage by_cluster boolean indicate whether linkages returned overall cluster","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_linkages.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access linkages — dom_linkages","text":"list containing linkages combination receptors, ligands, transcription factors, clusters","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_linkages.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access linkages — dom_linkages","text":"","code":"complexes <- dom_linkages(dominoSignal:::pbmc_dom_built_tiny, \"complexes\") tf_rec_by_cluster <- dom_linkages(dominoSignal:::pbmc_dom_built_tiny, \"tf-receptor\", TRUE)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_network_items.html","id":null,"dir":"Reference","previous_headings":"","what":"Access all features, receptors, or ligands present in a signaling network. — dom_network_items","title":"Access all features, receptors, or ligands present in a signaling network. — dom_network_items","text":"function collates features, receptors, ligands found signaling network anywhere list clusters. can useful comparing signaling networks across two separate conditions. order run build_domino() must run object previously.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_network_items.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access all features, receptors, or ligands present in a signaling network. — dom_network_items","text":"","code":"dom_network_items(dom, clusters = NULL, return = NULL)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_network_items.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access all features, receptors, or ligands present in a signaling network. — dom_network_items","text":"dom domino object containing signaling network (.e. build_domino() run) clusters vector indicating clusters collate network items . left NULL clusters included. return string indicating whether collate \"features\", \"receptors\", \"ligands\". \"\" list three returned.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_network_items.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access all features, receptors, or ligands present in a signaling network. — dom_network_items","text":"vector containing features, receptors, ligands data set list containing three.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_network_items.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access all features, receptors, or ligands present in a signaling network. — dom_network_items","text":"","code":"monocyte_receptors <- dom_network_items(dominoSignal:::pbmc_dom_built_tiny, \"CD14_monocyte\", \"receptors\") all_tfs <- dom_network_items(dominoSignal:::pbmc_dom_built_tiny, return = \"features\")"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_signaling.html","id":null,"dir":"Reference","previous_headings":"","what":"Access signaling — dom_signaling","title":"Access signaling — dom_signaling","text":"function pull signaling matrices domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_signaling.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access signaling — dom_signaling","text":"","code":"dom_signaling(dom, cluster = NULL)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_signaling.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access signaling — dom_signaling","text":"dom domino object created create_domino() cluster either NULL indicate global signaling specific cluster signaling matrix desired","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_signaling.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access signaling — dom_signaling","text":"data frame containing signaling score ligand (row) cluster (column) data frame containing global summed signaling scores receptors (rows) ligands (columns) cluster","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_signaling.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access signaling — dom_signaling","text":"","code":"monocyte_signaling <- dom_signaling(dominoSignal:::pbmc_dom_built_tiny, cluster = \"CD14_monocyte\")"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_tf_activation.html","id":null,"dir":"Reference","previous_headings":"","what":"Access transcription factor activation — dom_tf_activation","title":"Access transcription factor activation — dom_tf_activation","text":"function pull transcription factor activation scores domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_tf_activation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access transcription factor activation — dom_tf_activation","text":"","code":"dom_tf_activation(dom)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_tf_activation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access transcription factor activation — dom_tf_activation","text":"dom domino object created create_domino()","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_tf_activation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access transcription factor activation — dom_tf_activation","text":"matrix containing transcription factor activation scores TF (row) cell (column)","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_tf_activation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access transcription factor activation — dom_tf_activation","text":"","code":"tf_activation <- dom_tf_activation(dominoSignal:::pbmc_dom_built_tiny)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_zscores.html","id":null,"dir":"Reference","previous_headings":"","what":"Access z-scores — dom_zscores","title":"Access z-scores — dom_zscores","text":"function pull z-scored expression domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_zscores.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access z-scores — dom_zscores","text":"","code":"dom_zscores(dom)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_zscores.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access z-scores — dom_zscores","text":"dom domino object created create_domino()","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_zscores.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access z-scores — dom_zscores","text":"matrix containing z-scored gene expression values gene (row) cell (column)","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_zscores.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access z-scores — dom_zscores","text":"","code":"zscores <- dom_zscores(dominoSignal:::pbmc_dom_built_tiny)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/domino-class.html","id":null,"dir":"Reference","previous_headings":"","what":"The domino class — domino-class","title":"The domino class — domino-class","text":"domino class contains information necessary calculate receptor-ligand signaling. contains z-scored expression, cell cluster labels, feature values, referenced receptor-ligand database formatted receptor-ligand map. Calculated intermediate values also stored.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/domino-class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The domino class — domino-class","text":"instance class domino","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/domino-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"The domino class — domino-class","text":"db_info List data sets ligand - receptor database counts Raw count gene expression data z_scores Matrix z-scored expression data cells columns clusters Named factor cluster identity cell features Matrix features (TFs) correlate receptor - ligand expression . Cells columns features rows. cor Correlation matrix receptor expression features. linkages List lists containing info linking cluster->tf->rec->lig clust_de Data frame containing differential expression results features cluster. misc List miscellaneous info pertaining run parameters etc. cl_signaling_matrices Incoming signaling matrix cluster signaling Signaling matrix clusters.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/feat_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a heatmap of features organized by cluster — feat_heatmap","title":"Create a heatmap of features organized by cluster — feat_heatmap","text":"Creates heatmap transcription factor activation scores cells grouped cluster.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/feat_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a heatmap of features organized by cluster — feat_heatmap","text":"","code":"feat_heatmap(   dom,   feats = NULL,   bool = FALSE,   bool_thresh = 0.2,   title = TRUE,   norm = FALSE,   cols = NULL,   ann_cols = TRUE,   min_thresh = NULL,   max_thresh = NULL,   ... )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/feat_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a heatmap of features organized by cluster — feat_heatmap","text":"dom Domino object network built (build_domino()) feats Either vector features include heatmap '' features. left NULL features selected signaling network shown. bool Boolean indicating whether heatmap continuous boolean. boolean bool_thresh used determine define activity positive negative. bool_thresh Numeric indicating threshold separating '' '' feature activity making boolean heatmap. title Either string use title boolean describing whether include title. order pass 'main' parameter  ComplexHeatmap::Heatmap()  must set title FALSE. norm Boolean indicating whether normalize transcrption factors max value. cols Named vector colors annotate cells cluster color. Values taken colors names cluster. left NULL default ggplot colors generated. ann_cols Boolean indicating whether include cell cluster column annotation. Colors can defined cols. FALSE custom annotations can passed ComplexHeatmap::Heatmap(). min_thresh Minimum threshold color scaling boolean heatmap max_thresh Maximum threshold color scaling boolean heatmap ... parameters pass  ComplexHeatmap::Heatmap() . Note use 'main' parameter  ComplexHeatmap::Heatmap()  must set title = FALSE use 'annCol' 'annColors' ann_cols must FALSE.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/feat_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a heatmap of features organized by cluster — feat_heatmap","text":"heatmap rendered active graphics device","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/feat_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a heatmap of features organized by cluster — feat_heatmap","text":"","code":"# basic usage feat_heatmap(dominoSignal:::pbmc_dom_built_tiny)  # using thresholds feat_heatmap(  dominoSignal:::pbmc_dom_built_tiny, min_thresh = 0.1,    max_thresh = 0.6, norm = TRUE, bool = FALSE) #> Warning: You are using norm with min_thresh and max_thresh. Note that values will be thresholded AFTER normalization."},{"path":"https://FertigLab.github.io/dominoSignal/reference/gene_network.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a gene association network — gene_network","title":"Create a gene association network — gene_network","text":"Create gene association network genes given cluster. selected cluster acts receptor gene association network, ligands, receptors, features associated receptor cluster included plot.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/gene_network.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a gene association network — gene_network","text":"","code":"gene_network(   dom,   clust = NULL,   OutgoingSignalingClust = NULL,   class_cols = c(lig = \"#FF685F\", rec = \"#47a7ff\", feat = \"#39C740\"),   cols = NULL,   lig_scale = 1,   layout = \"grid\",   ... )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/gene_network.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a gene association network — gene_network","text":"dom Domino object network built (build_domino()) clust Receptor cluster create gene association network . vector clusters may provided. OutgoingSignalingClust Vector clusters plot outgoing signaling class_cols Named vector colors used color classes vertices. Values must colors names must classes ('rec', 'lig', 'feat' receptors, ligands, features.). cols Named vector colors individual genes. Genes included vector colored according class_cols. lig_scale FALSE numeric value scale size ligand vertices based z-scored expression data set. layout Type layout use. Options 'grid', 'random', 'sphere', 'circle', 'fr' Fruchterman-Reingold force directed layout, 'kk' Kamada Kawai directed layout. ... parameters pass plot() igraph object. See igraph manual options.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/gene_network.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a gene association network — gene_network","text":"igraph plot rendered active graphics device","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/gene_network.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a gene association network — gene_network","text":"","code":"# basic usage gene_network(  dominoSignal:::pbmc_dom_built_tiny, clust = \"CD8_T_cell\",   OutgoingSignalingClust = \"CD14_monocyte\")"},{"path":"https://FertigLab.github.io/dominoSignal/reference/ggplot_col_gen.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate ggplot colors — ggplot_col_gen","title":"Generate ggplot colors — ggplot_col_gen","text":"Accepts number colors generate generates ggplot color spectrum.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/ggplot_col_gen.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate ggplot colors — ggplot_col_gen","text":"","code":"ggplot_col_gen(n)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/ggplot_col_gen.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate ggplot colors — ggplot_col_gen","text":"n Number colors generate","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/ggplot_col_gen.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate ggplot colors — ggplot_col_gen","text":"vector colors according ggplot color generation.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/incoming_signaling_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a cluster incoming signaling heatmap — incoming_signaling_heatmap","title":"Create a cluster incoming signaling heatmap — incoming_signaling_heatmap","text":"Creates heatmap cluster incoming signaling matrix. cluster list ligands capable activating enriched transcription factors. function creates heatmap cluster average expression ligands. list cluster incoming signaling matrices can found cl_signaling_matrices slot domino option alternative plotting function.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/incoming_signaling_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a cluster incoming signaling heatmap — incoming_signaling_heatmap","text":"","code":"incoming_signaling_heatmap(   dom,   rec_clust,   clusts = NULL,   min_thresh = -Inf,   max_thresh = Inf,   scale = \"none\",   normalize = \"none\",   title = TRUE,   ... )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/incoming_signaling_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a cluster incoming signaling heatmap — incoming_signaling_heatmap","text":"dom Domino object network built (build_domino()) rec_clust cluster select receptor. Must match naming clusters domino object. clusts Vector clusters included. NULL clusters used. min_thresh Minimum signaling threshold plotting. Defaults -Inf threshold. max_thresh Maximum signaling threshold plotting. Defaults Inf threshold. scale scale values (thresholding). Options 'none', 'sqrt' square root, 'log' log10. normalize Options normalize matrix. Accepted inputs 'none' normalization, 'rec_norm' normalize maximum value receptor cluster, 'lig_norm' normalize maximum value within ligand cluster title Either string use title boolean describing whether include title. order pass 'main' parameter  ComplexHeatmap::Heatmap()  must set title FALSE. ... parameters pass  ComplexHeatmap::Heatmap(). Note use 'column_title' parameter  ComplexHeatmap::Heatmap()  must set title = FALSE","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/incoming_signaling_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a cluster incoming signaling heatmap — incoming_signaling_heatmap","text":"Heatmap rendered active graphics device","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/incoming_signaling_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a cluster incoming signaling heatmap — incoming_signaling_heatmap","text":"","code":"#incoming signaling of the CD8  T cells incoming_signaling_heatmap(dominoSignal:::pbmc_dom_built_tiny, \"CD8_T_cell\")"},{"path":"https://FertigLab.github.io/dominoSignal/reference/lc.html","id":null,"dir":"Reference","previous_headings":"","what":"Pulls all items from a list pooled into a single vector — lc","title":"Pulls all items from a list pooled into a single vector — lc","text":"Helper function convert nested series lists single vector.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/lc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pulls all items from a list pooled into a single vector — lc","text":"","code":"lc(list, list_names)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/lc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pulls all items from a list pooled into a single vector — lc","text":"list List pull items list_names Names items list pool","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/lc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pulls all items from a list pooled into a single vector — lc","text":"vector contaning items list list_names","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/linkage_summary-class.html","id":null,"dir":"Reference","previous_headings":"","what":"The domino linkage summary class — linkage_summary-class","title":"The domino linkage summary class — linkage_summary-class","text":"linkage summary class contains linkages established multiple domino objects gene regulatory network inference reference receptor- ligand data bases. data frame summarizing meta features describe domino objects compared linkage summary facilitates comparisons established linkages differential signaling interactions across categorical sample covariates.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/linkage_summary-class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The domino linkage summary class — linkage_summary-class","text":"instance class linkage_summary","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/linkage_summary-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"The domino linkage summary class — linkage_summary-class","text":"subject_names unique names domino result included summary subject_meta data.frame row describing one subject columns describing features subjects draw comparisons signaling networks subject_linkages nested list linkages inferred subject. Lists stored heirarchical structure subject-cluster-linkage linkages include transcription factors (tfs), linkages transcription factors receptors (tfs_rec), active receptors (rec), possible receptor-ligand interactions (rec_lig), incoming ligands (incoming_lig)","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/mean_ligand_expression.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate mean ligand expression as a data frame for plotting in circos plot — mean_ligand_expression","title":"Calculate mean ligand expression as a data frame for plotting in circos plot — mean_ligand_expression","text":"Creates data frame mean ligand expression use plotting circos plot ligand expression saving tables mean expression.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/mean_ligand_expression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate mean ligand expression as a data frame for plotting in circos plot — mean_ligand_expression","text":"","code":"mean_ligand_expression(x, ligands, cell_ident, cell_barcodes, destination)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/mean_ligand_expression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate mean ligand expression as a data frame for plotting in circos plot — mean_ligand_expression","text":"x Gene cell expression matrix ligands Character vector ligand genes quantified cell_ident Vector cell type (identity) names calculate mean ligand gene expression cell_barcodes Vector cell barcodes (colnames x) belonging cell_ident calculate mean expression across destination Name receptor ligand interacts","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/mean_ligand_expression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate mean ligand expression as a data frame for plotting in circos plot — mean_ligand_expression","text":"data frame ligand expression targeting specified receptor","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/mean_ligand_expression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate mean ligand expression as a data frame for plotting in circos plot — mean_ligand_expression","text":"","code":"counts <- dom_counts(dominoSignal:::pbmc_dom_built_tiny) mean_exp <- mean_ligand_expression(counts,  ligands = c(\"PTPRC\", \"FASLG\"), cell_ident = \"CD14_monocyte\",  cell_barcodes = colnames(counts), destination = \"FAS\")"},{"path":"https://FertigLab.github.io/dominoSignal/reference/plot_differential_linkages.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot differential linkages among domino results ranked by a comparative statistic — plot_differential_linkages","title":"Plot differential linkages among domino results ranked by a comparative statistic — plot_differential_linkages","text":"Plot differential linkages among domino results ranked comparative statistic","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/plot_differential_linkages.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot differential linkages among domino results ranked by a comparative statistic — plot_differential_linkages","text":"","code":"plot_differential_linkages(   differential_linkages,   test_statistic,   stat_range = c(0, 1),   stat_ranking = c(\"ascending\", \"descending\"),   group_palette = NULL )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/plot_differential_linkages.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot differential linkages among domino results ranked by a comparative statistic — plot_differential_linkages","text":"differential_linkages data frame output test_differential_linkages() function test_statistic column name differential_linkages test statistic used ranking linkages stored (ex. 'p.value') stat_range two value vector minimum maximum values test_statistic plotting linkage features stat_ranking 'ascending' (lowest value test statisic colored red plotted top) 'descending' (highest value test statistic colored red plotted top). group_palette named vector colors use group compared","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/plot_differential_linkages.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot differential linkages among domino results ranked by a comparative statistic — plot_differential_linkages","text":"heatmap-class object features ranked test_statistic annotated proportion subjects showed active linkage features.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/plot_differential_linkages.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot differential linkages among domino results ranked by a comparative statistic — plot_differential_linkages","text":"","code":"plot_differential_linkages(  differential_linkages = dominoSignal:::tiny_differential_linkage_c1,  test_statistic = \"p.value\",  stat_ranking = \"ascending\" )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/print-domino-method.html","id":null,"dir":"Reference","previous_headings":"","what":"Print domino object — print,domino-method","title":"Print domino object — print,domino-method","text":"Prints summary domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/print-domino-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print domino object — print,domino-method","text":"","code":"# S4 method for domino print(x, ...)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/print-domino-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print domino object — print,domino-method","text":"x domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/print-domino-method.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print domino object — print,domino-method","text":"printed description number cells clusters domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/print-domino-method.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print domino object — print,domino-method","text":"","code":"print(dominoSignal:::pbmc_dom_built_tiny) #> A domino object of 360 cells #>                 Contains signaling between 3 clusters #>                 Built with a maximum of Inf TFs per cluster #>                 and a maximum of Inf receptors per TF"},{"path":"https://FertigLab.github.io/dominoSignal/reference/read_if_char.html","id":null,"dir":"Reference","previous_headings":"","what":"Read in data if an object looks like path to it — read_if_char","title":"Read in data if an object looks like path to it — read_if_char","text":"Read data object looks like path ","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/read_if_char.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read in data if an object looks like path to it — read_if_char","text":"","code":"read_if_char(obj)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/read_if_char.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read in data if an object looks like path to it — read_if_char","text":"obj object read already object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/read_if_char.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read in data if an object looks like path to it — read_if_char","text":"Object data read path","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/rename_clusters.html","id":null,"dir":"Reference","previous_headings":"","what":"Renames clusters in a domino object — rename_clusters","title":"Renames clusters in a domino object — rename_clusters","text":"function renames clusters used build domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/rename_clusters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Renames clusters in a domino object — rename_clusters","text":"","code":"rename_clusters(dom, clust_conv, warning = FALSE)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/rename_clusters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Renames clusters in a domino object — rename_clusters","text":"dom domino object rename clusters clust_conv named vector conversions old new clusters. Values taken new clusters IDs names old cluster IDs. warning logical. TRUE, warn cluster found conversion table. Default FALSE.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/rename_clusters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Renames clusters in a domino object — rename_clusters","text":"domino object clusters renamed applicable slots.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/rename_clusters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Renames clusters in a domino object — rename_clusters","text":"","code":"new_clust <- c(\"CD8_T_cell\" = \"CD8+ T Cells\",  \"CD14_monocyte\" = \"CD14+ Monocytes\", \"B_cell\" = \"B Cells\") pbmc_dom_built_tiny <- rename_clusters(dominoSignal:::pbmc_dom_built_tiny, new_clust)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/show-domino-method.html","id":null,"dir":"Reference","previous_headings":"","what":"Show domino object information — show,domino-method","title":"Show domino object information — show,domino-method","text":"Shows content overview domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/show-domino-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Show domino object information — show,domino-method","text":"","code":"# S4 method for domino show(object)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/show-domino-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Show domino object information — show,domino-method","text":"object domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/show-domino-method.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Show domino object information — show,domino-method","text":"printed description cell numbers clusters object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/show-domino-method.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Show domino object information — show,domino-method","text":"","code":"dominoSignal:::pbmc_dom_built_tiny #> A domino object of 360 cells #> Built with signaling between 3 clusters  show(dominoSignal:::pbmc_dom_built_tiny) #> A domino object of 360 cells #> Built with signaling between 3 clusters"},{"path":"https://FertigLab.github.io/dominoSignal/reference/signaling_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a network heatmap — signaling_heatmap","title":"Create a network heatmap — signaling_heatmap","text":"Creates heatmap signaling network. Alternatively, network matrix can accessed directly signaling slot domino object using dom_signaling() function.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/signaling_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a network heatmap — signaling_heatmap","text":"","code":"signaling_heatmap(   dom,   clusts = NULL,   min_thresh = -Inf,   max_thresh = Inf,   scale = \"none\",   normalize = \"none\",   ... )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/signaling_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a network heatmap — signaling_heatmap","text":"dom domino object network built (build_domino()) clusts vector clusters included. NULL clusters used. min_thresh minimum signaling threshold plotting. Defaults -Inf threshold. max_thresh maximum signaling threshold plotting. Defaults Inf threshold. scale scale values (thresholding). Options 'none', 'sqrt' square root, 'log' log10. normalize options normalize matrix. Normalization done thresholding scaling. Accepted inputs 'none' normalization, 'rec_norm' normalize maximum value receptor cluster, 'lig_norm' normalize maximum value within ligand cluster ... parameters pass  ComplexHeatmap::Heatmap()","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/signaling_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a network heatmap — signaling_heatmap","text":"heatmap rendered active graphics device","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/signaling_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a network heatmap — signaling_heatmap","text":"","code":"# basic usage signaling_heatmap(dominoSignal:::pbmc_dom_built_tiny)  # scale signaling_heatmap(dominoSignal:::pbmc_dom_built_tiny, scale = \"sqrt\")  # normalize signaling_heatmap(dominoSignal:::pbmc_dom_built_tiny, normalize = \"rec_norm\") #> Warning: Some values are NA, replacing with 0s."},{"path":"https://FertigLab.github.io/dominoSignal/reference/signaling_network.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a cluster to cluster signaling network diagram — signaling_network","title":"Create a cluster to cluster signaling network diagram — signaling_network","text":"Creates network diagram signaling clusters. Nodes clusters directed edges indicate signaling one cluster another. Edges colored based color scheme ligand expressing cluster","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/signaling_network.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a cluster to cluster signaling network diagram — signaling_network","text":"","code":"signaling_network(   dom,   cols = NULL,   edge_weight = 0.3,   clusts = NULL,   showOutgoingSignalingClusts = NULL,   showIncomingSignalingClusts = NULL,   min_thresh = -Inf,   max_thresh = Inf,   normalize = \"none\",   scale = \"sq\",   layout = \"circle\",   scale_by = \"rec_sig\",   vert_scale = 3,   plot_title = NULL,   ... )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/signaling_network.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a cluster to cluster signaling network diagram — signaling_network","text":"dom domino object network built (build_domino()) cols named vector indicating colors clusters. Values colors names must match clusters domino object. left NULL ggplot colors generated clusters edge_weight weight determining thickness edges plot. Signaling values multiplied value clusts vector clusters included network plot showOutgoingSignalingClusts vector clusters plot outgoing signaling showIncomingSignalingClusts vector clusters plot incoming signaling min_thresh minimum signaling threshold. Values lower threshold set threshold. Defaults -Inf threshold max_thresh maximum signaling threshold plotting. Values higher threshold set threshold. Defaults Inf threshold normalize options normalize signaling matrix. Accepted inputs 'none' normalization, 'rec_norm' normalize maximum value receptor cluster, 'lig_norm' normalize maximum value within ligand cluster scale scale values (thresholding). Options 'none', 'sqrt' square root, 'log' log10, 'sq' square layout type layout use. Options 'random', 'sphere', 'circle', 'fr' Fruchterman-Reingold force directed layout, 'kk' Kamada Kawai directed layout scale_by size vertices. Options 'lig_sig' summed outgoing signaling, 'rec_sig' summed incoming signaling, 'none'. former two cases values scaled asinh summing incoming outgoing signaling vert_scale integer used scale size vertices without variable scaling size_verts_by. plot_title text plot's title. ... parameters passed plot used igraph object.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/signaling_network.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a cluster to cluster signaling network diagram — signaling_network","text":"igraph plot rendered active graphics device","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/signaling_network.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a cluster to cluster signaling network diagram — signaling_network","text":"","code":"# basic usage signaling_network(dominoSignal:::pbmc_dom_built_tiny)  # scaling, thresholds, layouts, selecting clusters signaling_network(  dominoSignal:::pbmc_dom_built_tiny, showOutgoingSignalingClusts = \"CD14_monocyte\",   scale = \"none\", norm = \"none\", layout = \"fr\", scale_by = \"none\",   vert_scale = 5)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/summarize_linkages.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize linkages from multiple domino objects — summarize_linkages","title":"Summarize linkages from multiple domino objects — summarize_linkages","text":"Creates linkage_summary() object storing linkages learned different domino objects nested lists facilitate comparisons networks learned domino across subject covariates.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/summarize_linkages.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize linkages from multiple domino objects — summarize_linkages","text":"","code":"summarize_linkages(domino_results, subject_meta, subject_names = NULL)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/summarize_linkages.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize linkages from multiple domino objects — summarize_linkages","text":"domino_results list domino result one domino object per subject. Names list must match subject_names subject_meta data frame includes subject features objects grouped. first column must subject names subject_names vector subject names domino_results. NULL, defaults first column subject_meta.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/summarize_linkages.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize linkages from multiple domino objects — summarize_linkages","text":"linkage summary class object consisting nested lists active transcription factors, active receptors, incoming ligands cluster across multiple domino results","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/summarize_linkages.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize linkages from multiple domino objects — summarize_linkages","text":"","code":"dom_ls <- dominoSignal:::dom_ls_tiny meta_df <- data.frame(\"ID\" = c(\"dom1\", \"dom2\"), \"group\" = c(\"A\", \"B\")) summarize_linkages(  domino_results = dom_ls, subject_meta = meta_df,   subject_names = meta_df$ID ) #> An object of class \"linkage_summary\" #> Slot \"subject_names\": #> [1] dom1 dom2 #> Levels: dom1 dom2 #>  #> Slot \"subject_meta\": #>     ID group #> 1 dom1     A #> 2 dom2     B #>  #> Slot \"subject_linkages\": #> $dom1 #> $dom1$B_cell #> $dom1$B_cell$tfs #> character(0) #>  #> $dom1$B_cell$rec #> NULL #>  #> $dom1$B_cell$incoming_lig #> NULL #>  #> $dom1$B_cell$tfs_rec #> character(0) #>  #> $dom1$B_cell$rec_lig #> character(0) #>  #>  #> $dom1$CD14_monocyte #> $dom1$CD14_monocyte$tfs #> [1] \"ZNF324\" \"CREM\"   \"FOSL1\"  #>  #> $dom1$CD14_monocyte$rec #> [1] \"CXCR3\"        \"IL7_receptor\" \"TGFBR3\"       \"NRG1\"         #>  #> $dom1$CD14_monocyte$incoming_lig #> [1] \"CCL20\"                 \"IL7\"                   \"TGFB3\"                 #> [4] \"integrin_a6b4_complex\" #>  #> $dom1$CD14_monocyte$tfs_rec #> [1] \"ZNF324 <- CXCR3\"      \"CREM <- IL7_receptor\" \"CREM <- TGFBR3\"       #> [4] \"FOSL1 <- NRG1\"        #>  #> $dom1$CD14_monocyte$rec_lig #> [1] \"CXCR3 <- CCL20\"                \"IL7_receptor <- IL7\"           #> [3] \"TGFBR3 <- TGFB3\"               \"NRG1 <- integrin_a6b4_complex\" #>  #>  #> $dom1$CD8_T_cell #> $dom1$CD8_T_cell$tfs #> [1] \"FLI1\" #>  #> $dom1$CD8_T_cell$rec #> [1] \"CXCR3\"        \"IL7_receptor\" #>  #> $dom1$CD8_T_cell$incoming_lig #> [1] \"CCL20\" \"IL7\"   #>  #> $dom1$CD8_T_cell$tfs_rec #> [1] \"FLI1 <- CXCR3\"        \"FLI1 <- IL7_receptor\" #>  #> $dom1$CD8_T_cell$rec_lig #> [1] \"CXCR3 <- CCL20\"      \"IL7_receptor <- IL7\" #>  #>  #>  #> $dom2 #> $dom2$B_cell #> $dom2$B_cell$tfs #> character(0) #>  #> $dom2$B_cell$rec #> NULL #>  #> $dom2$B_cell$incoming_lig #> NULL #>  #> $dom2$B_cell$tfs_rec #> character(0) #>  #> $dom2$B_cell$rec_lig #> character(0) #>  #>  #> $dom2$CD14_monocyte #> $dom2$CD14_monocyte$tfs #> [1] \"FLI1\" #>  #> $dom2$CD14_monocyte$rec #> [1] \"CXCR3\"        \"IL7_receptor\" #>  #> $dom2$CD14_monocyte$incoming_lig #> [1] \"CCL20\" \"IL7\"   #>  #> $dom2$CD14_monocyte$tfs_rec #> [1] \"FLI1 <- CXCR3\"        \"FLI1 <- IL7_receptor\" #>  #> $dom2$CD14_monocyte$rec_lig #> [1] \"CXCR3 <- CCL20\"      \"IL7_receptor <- IL7\" #>  #>  #> $dom2$CD8_T_cell #> $dom2$CD8_T_cell$tfs #> [1] \"ZNF324\" \"CREM\"   \"FOSL1\"  #>  #> $dom2$CD8_T_cell$rec #> [1] \"CXCR3\"        \"IL7_receptor\" \"TGFBR3\"       \"NRG1\"         #>  #> $dom2$CD8_T_cell$incoming_lig #> [1] \"CCL20\"                 \"IL7\"                   \"TGFB3\"                 #> [4] \"integrin_a6b4_complex\" #>  #> $dom2$CD8_T_cell$tfs_rec #> [1] \"ZNF324 <- CXCR3\"      \"CREM <- IL7_receptor\" \"CREM <- TGFBR3\"       #> [4] \"FOSL1 <- NRG1\"        #>  #> $dom2$CD8_T_cell$rec_lig #> [1] \"CXCR3 <- CCL20\"                \"IL7_receptor <- IL7\"           #> [3] \"TGFBR3 <- TGFB3\"               \"NRG1 <- integrin_a6b4_complex\" #>  #>  #>  #>"},{"path":"https://FertigLab.github.io/dominoSignal/reference/table_convert_genes.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert genes using a table — table_convert_genes","title":"Convert genes using a table — table_convert_genes","text":"Takes vector gene inputs conversion table returns converted gene table","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/table_convert_genes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert genes using a table — table_convert_genes","text":"","code":"table_convert_genes(genes, from, to, conversion_table)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/table_convert_genes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert genes using a table — table_convert_genes","text":"genes genes convert gene symbol type input (ENSG, ENSMUSG, HGNC, MGI) desired gene symbol type output (HGNC, MGI) conversion_table data frame column names corresponding gene symbol types (mm.ens, hs.ens, mgi, hgnc) rows corresponding gene symbols ","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/table_convert_genes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert genes using a table — table_convert_genes","text":"data frame genes original corresponding converted symbols","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/test_differential_linkages.html","id":null,"dir":"Reference","previous_headings":"","what":"Statistical test for differential linkages across multiple domino results — test_differential_linkages","title":"Statistical test for differential linkages across multiple domino results — test_differential_linkages","text":"Statistical test differential linkages across multiple domino results","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/test_differential_linkages.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Statistical test for differential linkages across multiple domino results — test_differential_linkages","text":"","code":"test_differential_linkages(   linkage_summary,   cluster,   group.by,   linkage = \"rec_lig\",   subject_names = NULL,   test_name = \"fishers.exact\" )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/test_differential_linkages.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Statistical test for differential linkages across multiple domino results — test_differential_linkages","text":"linkage_summary linkage_summary() object cluster name cell cluster compared across multiple domino results group.name column linkage_summary@subject_meta group subjects counting. linkage stored linkage domino object. Can compare 'tfs', 'rec', 'incoming_lig', 'tfs_rec', 'rec_lig' subject_names vector subject_names linkage_summary compared. NULL, subject_names linkage summary included counting. test_name statistical test used comparison. 'fishers.exact' : Fisher's exact test dependence proportion subjects active linkage cluster group subject belongs group.variable. Provides odds ratio, p-value, Benjamini-Hochberg FDR-adjusted p-value (p.adj) linkage tested.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/test_differential_linkages.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Statistical test for differential linkages across multiple domino results — test_differential_linkages","text":"data frame results test differential linkages. Rows correspond linkage tested. Columns correspond : 'cluster' : name cell cluster compared 'linkage' : type linkage compared 'group.' : grouping variable 'test_name' : test used comparison 'feature' : individual linkages compared 'test statistics' : test statistics provided based test method. 'fishers.exact' provides odds ratio, p-value, fdr-adjusted p-value. 'total_count' : total number subjects linkage active 'X_count' : number subjects category group.(X) linkage active 'total_n' : number total subjects compared 'X_n' : total number subjects category group.(X)","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/test_differential_linkages.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Statistical test for differential linkages across multiple domino results — test_differential_linkages","text":"","code":"test_differential_linkages(   linkage_summary = dominoSignal:::linkage_sum_tiny, cluster = \"C1\", group.by = \"group\",    linkage = \"rec\", test_name = \"fishers.exact\" ) #>    cluster linkage group.by     test_name feature odds.ratio p.value p.adj #> R1      C1     rec    group fishers.exact      R1        Inf     0.1   0.4 #> R2      C1     rec    group fishers.exact      R2        Inf     0.4   0.8 #> R3      C1     rec    group fishers.exact      R3  0.3219834     1.0   1.0 #> R4      C1     rec    group fishers.exact      R4  1.0000000     1.0   1.0 #>    total_count G1_count G2_count total_n G1_n G2_n #> R1           3        3        0       6    3    3 #> R2           4        3        1       6    3    3 #> R3           3        1        2       6    3    3 #> R4           2        1        1       6    3    3"},{"path":[]},{"path":"https://FertigLab.github.io/dominoSignal/news/index.html","id":"package-name-0-99-2","dir":"Changelog","previous_headings":"","what":"Package Name","title":"dominoSignal v0.99.2","text":"Update package name “domino2” “dominoSignal”","code":""},{"path":"https://FertigLab.github.io/dominoSignal/news/index.html","id":"bioconductor-standards-0-99-2","dir":"Changelog","previous_headings":"","what":"Bioconductor Standards","title":"dominoSignal v0.99.2","text":"Update vignettes presenting application DominoSignal pipeline data formatted SingleCellExperiment object Implemented caching example data BiocCache meet package size limits Removal deprecated scripts running SCENIC. Tutorials running SCENIC still present vignettes Corrected BiocCheck notes pertaining coding practices including paste conditional statements, functions dontrun examples, usage seq_len seq_along place seq, usage vapply place sapply","code":""},{"path":"https://FertigLab.github.io/dominoSignal/news/index.html","id":"dominosignal-v0991","dir":"Changelog","previous_headings":"","what":"dominoSignal v0.99.1","title":"dominoSignal v0.99.1","text":"Update Bioconductor version numbering conventions package submission","code":""},{"path":[]},{"path":"https://FertigLab.github.io/dominoSignal/news/index.html","id":"linkage-functions-0-2-2","dir":"Changelog","previous_headings":"","what":"Linkage functions","title":"dominoSignal v0.2.2","text":"Addition new class summarize linkages objects Addition helper functions count linkages compare objects Plotting function differential linkages","code":""},{"path":"https://FertigLab.github.io/dominoSignal/news/index.html","id":"package-structure-0-2-2","dir":"Changelog","previous_headings":"","what":"Package structure","title":"dominoSignal v0.2.2","text":"Adjustments made meet Bioconductor standards","code":""},{"path":[]},{"path":"https://FertigLab.github.io/dominoSignal/news/index.html","id":"updates-to-domino-object-construction-0-2-1","dir":"Changelog","previous_headings":"","what":"Updates to domino object construction","title":"dominoSignal v0.2.1","text":"Uniform formats inputs receptor - ligand interaction databases, transcription factor activity features, regulon gene lists operability alternative databases transcription factor activation inference methods Helper functions reformatting pySCENIC outputs CellPhoneDB database files domino-readable uniform formats Assessment transcription factor linkage receptors function heteromeric complex based correlation transcription factor activity receptor component genes Assessment complex ligand expression mean component gene expression plotting functions Minimum threshold percentage cells cluster expressing receptor gene receptor called active within cluster Additional linkage slots active receptors cluster, transcription factor - receptor linkages cluster, incoming ligands active receptors cluster","code":""},{"path":"https://FertigLab.github.io/dominoSignal/news/index.html","id":"plotting-functions-0-2-1","dir":"Changelog","previous_headings":"","what":"Plotting functions","title":"dominoSignal v0.2.1","text":"Chord plot ligand expression targeting specified receptor chord widths correspond quantity ligand expression cell cluster Signaling networks showing outgoing signaling specified cell clusters Gene networks two cell clusters","code":""},{"path":"https://FertigLab.github.io/dominoSignal/news/index.html","id":"bugfixes-0-2-1","dir":"Changelog","previous_headings":"","what":"Bugfixes","title":"dominoSignal v0.2.1","text":"Added host option gene ortholog conversions using {biomaRt} access maintained mirrors Transcription factor - target linkages now properly stored receptors transcription factor’s regulon excluded linkage Ligand nodes sizes gene networks correspond quantity ligand expression create_domino() can run without providing regulon list References host GitHub repository updated Elisseeff-Lab","code":""}]
+[{"path":"https://FertigLab.github.io/dominoSignal/articles/cellphonedb_vignette.html","id":"file-downloads-for-cellphonedb","dir":"Articles","previous_headings":"","what":"File Downloads for CellPhoneDB:","title":"Using the CellPhoneDB Database","text":"Database files CellPhoneDB v4.0.0 human scRNAseq data can installed public Github repository Tiechmann Group developed CellPhoneDB. facilitate use files format used dominoSignal, include helper function, create_rl_map_cellphonedb(), automatically parses files CellPhoneDB database arrive rl_map format. information use files dominoSignal pipeline, please see Getting Started page. learn use SCENIC TF activation scoring, please see SCENIC TF Activation Scoring tutorial. Vignette Build Information Date last built session information:","code":"# URL for desired version of CellPhoneDB cellphone_url <- \"https://github.com/ventolab/cellphonedb-data/archive/refs/tags/v4.0.0.tar.gz\"  # download compressed database cellphone_tar <- paste0(temp_dir, \"/cellphoneDB_v4.tar.gz\") download.file(url = cellphone_url, destfile = cellphone_tar)  # move contents of the compressed file to a new directory untar(tarfile = cellphone_tar, exdir = cellphone_dir) cellphone_data <- paste0(cellphone_dir, \"/cellphonedb-data-4.0.0/data\") Sys.Date() #> [1] \"2024-06-25\" sessionInfo() #> R version 4.2.1 (2022-06-23) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #>  #> Matrix products: default #> BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 #> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 #>  #> locale: #>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               #>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     #>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    #>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  #>  [9] LC_ADDRESS=C               LC_TELEPHONE=C             #> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C        #>  #> attached base packages: #> [1] stats     graphics  grDevices utils     datasets  methods   base      #>  #> loaded via a namespace (and not attached): #>  [1] digest_0.6.35     R6_2.5.1          lifecycle_1.0.4   jsonlite_1.8.8    #>  [5] formatR_1.14      magrittr_2.0.3    evaluate_0.23     rlang_1.1.3       #>  [9] cachem_1.0.8      cli_3.6.2         fs_1.6.3          jquerylib_0.1.4   #> [13] bslib_0.6.1       ragg_1.3.0        vctrs_0.6.5       rmarkdown_2.26    #> [17] pkgdown_2.0.7     textshaping_0.3.7 desc_1.4.3        tools_4.2.1       #> [21] purrr_1.0.2       yaml_2.3.8        xfun_0.42         fastmap_1.1.1     #> [25] compiler_4.2.1    systemfonts_1.0.6 memoise_2.0.1     htmltools_0.5.7   #> [29] knitr_1.45        sass_0.4.8"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"options-and-setup","dir":"Articles","previous_headings":"","what":"Options and Setup","title":"Get Started with dominoSignal","text":"Libraries set Data used vignettes can downloaded Zenodo","code":"set.seed(42)  library(dominoSignal) library(SingleCellExperiment) library(plyr) library(circlize) library(ComplexHeatmap) library(knitr) # BiocFileCache helps with managing files across sessions bfc <- BiocFileCache::BiocFileCache(ask = FALSE) data_url <- \"https://zenodo.org/records/10951634/files\"  # download the reduced pbmc files preprocessed PBMC 3K scRNAseq data set as a # Seurat object pbmc_url <- paste0(data_url, \"/pbmc3k_sce.rds\") pbmc <- BiocFileCache::bfcrpath(bfc, pbmc_url)  # download scenic results scenic_auc_url <- paste0(data_url, \"/auc_pbmc_3k.csv\") scenic_auc <- BiocFileCache::bfcrpath(bfc, scenic_auc_url)  scenic_regulon_url <- paste0(data_url, \"/regulons_pbmc_3k.csv\") scenic_regulon <- BiocFileCache::bfcrpath(bfc, scenic_regulon_url)  # download CellPhoneDB files cellphone_url <- \"https://github.com/ventolab/cellphonedb-data/archive/refs/tags/v4.0.0.tar.gz\" cellphone_tar <- BiocFileCache::bfcrpath(bfc, cellphone_url) cellphone_dir <- paste0(tempdir(), \"/cellphone\") untar(tarfile = cellphone_tar, exdir = cellphone_dir) cellphone_data <- paste0(cellphone_dir, \"/cellphonedb-data-4.0.0/data\")   # directory for created inputs to pySCENIC and dominoSignal input_dir <- paste0(tempdir(), \"/inputs\") if (!dir.exists(input_dir)) {     dir.create(input_dir) }"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"data-preparation","dir":"Articles","previous_headings":"","what":"Data preparation","title":"Get Started with dominoSignal","text":"Analysis cell-cell communication dominoSignal often follows initial processing annotation scRNAseq data. used OSCA workflow prepare data set analysis dominoSignal. complete processing script available data-raw directory dominoSignal package. processed data can downloaded Zenodo.","code":"pbmc <- readRDS(pbmc)"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Get Started with dominoSignal","text":"Installation dominoSignal Github can achieved using remotes package.","code":"if (!require(remotes)) {     install.packages(\"remotes\") } remotes::install_github(\"FertigLab/dominoSignal\")"},{"path":[]},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"loading-scenic-results","dir":"Articles","previous_headings":"Loading TF and R - L data","what":"Loading SCENIC Results","title":"Get Started with dominoSignal","text":"TF activities required input dominoSignal, regulons learned SCENIC important optional input needed annotate TF-target interactions prune TF-receptor linkages receptor target TF. prevents distinction receptor expression driving TF activity TF inducing receptor’s expression. initial regulons data frame read R ctx function two rows column names need replaced one succinct description. dominoSignal changed input format TF regulons list storing vectors target genes regulon, names list TF genes. facilitates use alternative methods TF activity quantification. provide helper function, create_regulon_list_scenic() easy retrieval TF regulons output pySCENIC ctx function. Users aware AUC matrix SCENIC loaded cell x TF orientation transposed TF x cell orientation. pySCENIC also appends “(+)” TF names converted “…” upon loading R. characters can included without affecting results dominoSignal analysis can confusing querying TF features data. recommend comprehensive removal “…” characters using gsub() function.","code":"regulons <- read.csv(scenic_regulon) auc <- read.table(scenic_auc, header = TRUE, row.names = 1, stringsAsFactors = FALSE,     sep = \",\") regulons <- regulons[-1:-2, ] colnames(regulons) <- c(\"TF\", \"MotifID\", \"AUC\", \"NES\", \"MotifSimilarityQvalue\", \"OrthologousIdentity\",     \"Annotation\", \"Context\", \"TargetGenes\", \"RankAtMax\") regulon_list <- create_regulon_list_scenic(regulons = regulons) auc_in <- as.data.frame(t(auc)) # Remove pattern '...' from the end of all rownames: rownames(auc_in) <- gsub(\"\\\\.\\\\.\\\\.$\", \"\", rownames(auc_in))"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"load-cellphonedb-database","dir":"Articles","previous_headings":"Loading TF and R - L data","what":"Load CellPhoneDB Database","title":"Get Started with dominoSignal","text":"dominoSignal updated read ligand - receptor data bases uniform data frame format referred receptor-ligand map (rl_map) enable use alternative updated reference databases addition particular version CellPhoneDB’s database older versions Domino. row corresponds ligand - receptor interaction. Genes participating interaction referred “partner ” “partner B” without requiring fixed ordering whether B ligand vice versa. minimum required columns data frame : int_pair: names interacting ligand receptor separated \" & \" gene_A: gene genes encoding partner gene_B: gene genes encoding partner B type_A: (“L”, “R”) - indicates whether partner ligand (“L”) receptor (“R”) type_B: (“L”, “R”) - indicates whether partner B ligand (“L”) receptor (“R”) Additional annotation columns can provided name_A name_B ligands receptors whose name interaction database match names encoding genes. formatting also allows consideration ligand receptor complexes comprised heteromeric combination multiple proteins must co-expressed function. cases, “name_*” column shows name protein complex, “gene_*” column shows names genes encoding components complex separated commas “,”. plotting results build domino object, names interacting ligands receptors used based combined expression complex components. facilitate use formatting CellPhoneDB database, include helper function, create_rl_map_cellphonedb(), automatically parses files CellPhoneDB database arrive rl_map format.","code":"complexes <- read.csv(paste0(cellphone_data, \"/complex_input.csv\"), stringsAsFactors = FALSE) genes <- read.csv(paste0(cellphone_data, \"/gene_input.csv\"), stringsAsFactors = FALSE) interactions <- read.csv(paste0(cellphone_data, \"/interaction_input.csv\"), stringsAsFactors = FALSE) proteins <- read.csv(paste0(cellphone_data, \"/protein_input.csv\"), stringsAsFactors = FALSE)  rl_map <- create_rl_map_cellphonedb(genes = genes, proteins = proteins, interactions = interactions,     complexes = complexes, database_name = \"CellPhoneDB_v4.0\"  # database version used )  knitr::kable(head(rl_map))"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"optional-adding-interactions-manually","dir":"Articles","previous_headings":"Loading TF and R - L data > Load CellPhoneDB Database","what":"Optional: Adding interactions manually","title":"Get Started with dominoSignal","text":"change use rl_map formatting also enables users manually append interactions interest included interaction database need . can attained formatting desired interactions data frame column headers rl_map using rbind() function.","code":"# Integrin complexes are not annotated as receptors in CellPhoneDB_v4.0 # collagen-integrin interactions between cells may be missed unless tables from # the CellPhoneDB reference are edited or the interactions are manually added  col_int_df <- data.frame(int_pair = \"a11b1 complex & COLA1_HUMAN\", name_A = \"a11b1 complex\",     uniprot_A = \"P05556,Q9UKX5\", gene_A = \"ITB1,ITA11\", type_A = \"R\", name_B = \"COLA1_HUMAN\",     uniprot_B = \"P02452,P08123\", gene_B = \"COL1A1,COL1A2\", type_B = \"L\", annotation_strategy = \"manual\",     source = \"manual\", database_name = \"manual\") rl_map_append <- rbind(col_int_df, rl_map) knitr::kable(head(rl_map_append))"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"analysis-with-domino-object","dir":"Articles","previous_headings":"","what":"Analysis with Domino object","title":"Get Started with dominoSignal","text":"dominoSignal analysis takes place two steps. create_domino() initializes domino result object assesses differential TF activity across cell clusters Wilcoxon rank-sum test establishes TF-receptor linkages based Spearman correlation TF activities receptor expression across queried data set. build_domino() sets parameters TFs receptors called active within cell cluster aggregates scaled expression ligands capable interacting active receptors assessment ligand type cellular source triggering activation receptor.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"required-inputs-from-data-set","dir":"Articles","previous_headings":"Analysis with Domino object","what":"Required inputs from data set","title":"Get Started with dominoSignal","text":"dominoSignal infers active receipt signals via receptors based correlation receptor’s expression TF activity across data set differential activity TF within cell cluster. Correlations conducted using scaled expression values rather raw counts normalized counts. assessment receptor activity per cell type basis, named vector cell cluster assignments, names cell barcodes matching expression matrix, provided. Assessing signaling based categorical groupings cells can achieved passing groupings “clusters” build_domino() place cell types. dominoSignal accepts matrix counts, matrix scaled counts, named vector cell cluster labels factors. Shown extract elements SingleCellExperiment object. Note since data scaled gene, genes expression cell need removed. Note: Ligand receptor expression can assessed genes included z_scores matrix. Many scRNAseq analysis pipelines recommend storing genes high variance scaled expression slots data objects, thereby missing many genes encoding ligands receptors. Ensure genes interest included rows z_scores matrix. Scaled expression calculated genes PBMC data set removal genes expressed less three cells.","code":"counts = assay(pbmc, \"counts\") logcounts = assay(pbmc, \"logcounts\") logcounts = logcounts[rowSums(logcounts) > 0, ] z_scores = t(scale(t(logcounts))) clusters = factor(pbmc$cell_type) names(clusters) = colnames(pbmc)"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"create-domino-object","dir":"Articles","previous_headings":"Analysis with Domino object","what":"Create Domino object","title":"Get Started with dominoSignal","text":"point, create_domino() function can used make object. Parameters note include “use_clusters” required assess signaling cell types rather linkage TFs receptors broadly across data set. “use_complexes” decides receptors function heteromeric complexes considered testing linkages TFs receptors. TRUE, receptor complex linked TF majority component genes meet Spearman correlation threshold. “remove_rec_dropout” decides whether receptor values zero considered correlation calculations; measure intended reduce effect dropout receptors low expression. run create_domino() matrix vector inputs: information stored domino object access , please see vignette structure domino objects.","code":"pbmc_dom <- create_domino(rl_map = rl_map, features = auc_in, counts = counts, z_scores = z_scores,     clusters = clusters, tf_targets = regulon_list, use_clusters = TRUE, use_complexes = TRUE,     remove_rec_dropout = FALSE) # rl_map: receptor - ligand map data frame features: TF activation scores (AUC # matrix) counts: counts matrix z_scores: scaled expression data clusters: # named vector of cell cluster assignments tf_targets: list of TFs and their # regulons use_clusters: assess receptor activation and ligand expression on a # per-cluster basis use_complexes: include receptors and genes that function as # a complex in results remove_rec_dropout: whether to remove zeroes from # correlation calculations"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"build-domino-network","dir":"Articles","previous_headings":"Analysis with Domino object","what":"Build Domino Network","title":"Get Started with dominoSignal","text":"build_domino() finalizes construction domino object setting parameters identifying TFs differential activation clusters, receptor linkage TFs based magnitude positive correlation, minimum percentage cells within cluster expression receptor receptor called active. also options thresholds number TFs may called active cluster number receptors may linked one TF. thresholds n TFs m receptors, bottom n TFs lowest p-values Wilcoxon rank sum test top m receptors Spearman correlation coefficient chosen. thresholds number receptors TFs can sent infinity (Inf) collect receptors TFs meet statistical significance thresholds.","code":"pbmc_dom <- build_domino(dom = pbmc_dom, min_tf_pval = 0.001, max_tf_per_clust = 25,     max_rec_per_tf = 25, rec_tf_cor_threshold = 0.25, min_rec_percentage = 0.1) # min_tf_pval: Threshold for p-value of DE for TFs rec_tf_cor_threshold: # Minimum correlation between receptor and TF min_rec_percentage: Minimum # percent of cells that must express receptor pbmc_dom_all <- build_domino(dom = pbmc_dom, min_tf_pval = 0.001, max_tf_per_clust = Inf,     max_rec_per_tf = Inf, rec_tf_cor_threshold = 0.25, min_rec_percentage = 0.1)"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"visualization-of-domino-results","dir":"Articles","previous_headings":"","what":"Visualization of Domino Results","title":"Get Started with dominoSignal","text":"Multiple functions available visualize intracellular networks receptors TFs ligand - receptor mediated intercellular networks cell types.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"summarize-tf-activity-and-linkage","dir":"Articles","previous_headings":"Visualization of Domino Results","what":"Summarize TF Activity and Linkage","title":"Get Started with dominoSignal","text":"Enrichment TF activities cell types can visualized feat_heatmap() plots data set-wide TF activity scores heatmap.","code":"feat_heatmap(pbmc_dom, norm = TRUE, bool = FALSE, use_raster = FALSE, row_names_gp = grid::gpar(fontsize = 4))"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"cumulative-signaling-between-cell-types","dir":"Articles","previous_headings":"Visualization of Domino Results","what":"Cumulative signaling between cell types","title":"Get Started with dominoSignal","text":"cumulative degree signaling clusters assessed sum scaled expression ligands targeting active receptors another cluster. can visualized graph format using signaling_network() function. Nodes represent cell cluster edges scale magnitude signaling clusters. color edge corresponds sender cluster signal.  Signaling networks can also drawn edges rendering signals directed towards given cell type signals one cell type directed others. see options use, well plotting functions options, please see plotting vignette.","code":"signaling_network(pbmc_dom, edge_weight = 0.5, max_thresh = 3)"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"specific-signaling-interactions-between-clusters","dir":"Articles","previous_headings":"Visualization of Domino Results","what":"Specific Signaling Interactions between Clusters","title":"Get Started with dominoSignal","text":"Beyond aggregated degree signaling cell types, degrees signaling specific ligand - receptor interactions can assessed. gene_network() provides graph linkages active TFs cluster, linked receptors cluster, possible ligands active receptors.  New dominoSignal, gene_network() can used two clusters determine possible ligands given receptor expressed putative outgoing signaling cluster.  comprehensive assessment ligand expression targeting active receptors given cluster can assessed incoming_signaling_heatmap().  Another form comprehensive ligand expression assessment available individual active receptors form circos plots new dominoSignal. outer arcs correspond clusters domino object inner arcs representing possible ligand plotted receptor. Arcs drawn ligands cell type receptor ligand expressed specified threshold. Arc widths correspond mean express ligand cluster widest arc width scaling maximum expression ligand within data.","code":"gene_network(pbmc_dom, clust = \"dendritic_cell\", layout = \"grid\") gene_network(pbmc_dom, clust = \"dendritic_cell\", OutgoingSignalingClust = \"CD14_monocyte\",     layout = \"grid\") incoming_signaling_heatmap(pbmc_dom, rec_clust = \"dendritic_cell\", max_thresh = 2.5,     use_raster = FALSE) circos_ligand_receptor(pbmc_dom, receptor = \"CD74\")"},{"path":"https://FertigLab.github.io/dominoSignal/articles/dominoSignal.html","id":"continued-development","dir":"Articles","previous_headings":"","what":"Continued Development","title":"Get Started with dominoSignal","text":"Since dominoSignal package still developed, new functions features implemented future versions. meantime, put together information plotting domino object structure like explore package’s functionality. Additionally, find bugs, questions, want share idea, please let us know . Vignette Build Information Date last built session information:","code":"Sys.Date() #> [1] \"2024-06-25\" sessionInfo() #> R version 4.2.1 (2022-06-23) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #>  #> Matrix products: default #> BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 #> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 #>  #> locale: #>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               #>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     #>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    #>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  #>  [9] LC_ADDRESS=C               LC_TELEPHONE=C             #> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C        #>  #> attached base packages: #> [1] grid      stats4    stats     graphics  grDevices utils     datasets  #> [8] methods   base      #>  #> other attached packages: #>  [1] knitr_1.45                  ComplexHeatmap_2.14.0       #>  [3] circlize_0.4.16             plyr_1.8.9                  #>  [5] SingleCellExperiment_1.20.1 SummarizedExperiment_1.28.0 #>  [7] Biobase_2.58.0              GenomicRanges_1.50.2        #>  [9] GenomeInfoDb_1.34.9         IRanges_2.32.0              #> [11] S4Vectors_0.36.2            BiocGenerics_0.44.0         #> [13] MatrixGenerics_1.10.0       matrixStats_1.2.0           #> [15] dominoSignal_0.99.2         #>  #> loaded via a namespace (and not attached): #>   [1] bitops_1.0-7           fs_1.6.3               bit64_4.0.5            #>   [4] filelock_1.0.3         doParallel_1.0.17      RColorBrewer_1.1-3     #>   [7] progress_1.2.3         httr_1.4.7             backports_1.4.1        #>  [10] tools_4.2.1            bslib_0.6.1            utf8_1.2.4             #>  [13] R6_2.5.1               DBI_1.2.2              colorspace_2.1-0       #>  [16] GetoptLong_1.0.5       withr_3.0.0            tidyselect_1.2.1       #>  [19] prettyunits_1.2.0      bit_4.0.5              curl_5.2.1             #>  [22] compiler_4.2.1         textshaping_0.3.7      cli_3.6.2              #>  [25] formatR_1.14           Cairo_1.6-2            xml2_1.3.6             #>  [28] DelayedArray_0.24.0    desc_1.4.3             sass_0.4.8             #>  [31] scales_1.3.0           rappdirs_0.3.3         pkgdown_2.0.7          #>  [34] systemfonts_1.0.6      stringr_1.5.1          digest_0.6.35          #>  [37] rmarkdown_2.26         XVector_0.38.0         pkgconfig_2.0.3        #>  [40] htmltools_0.5.7        highr_0.10             dbplyr_2.5.0           #>  [43] fastmap_1.1.1          rlang_1.1.3            GlobalOptions_0.1.2    #>  [46] RSQLite_2.3.5          shape_1.4.6.1          jquerylib_0.1.4        #>  [49] generics_0.1.3         jsonlite_1.8.8         car_3.1-2              #>  [52] dplyr_1.1.4            RCurl_1.98-1.14        magrittr_2.0.3         #>  [55] GenomeInfoDbData_1.2.9 Matrix_1.6-5           munsell_0.5.0          #>  [58] Rcpp_1.0.12            fansi_1.0.6            abind_1.4-5            #>  [61] lifecycle_1.0.4        stringi_1.8.3          yaml_2.3.8             #>  [64] carData_3.0-5          zlibbioc_1.44.0        BiocFileCache_2.11.2   #>  [67] blob_1.2.4             parallel_4.2.1         crayon_1.5.2           #>  [70] lattice_0.22-5         Biostrings_2.66.0      hms_1.1.3              #>  [73] KEGGREST_1.38.0        magick_2.8.3           pillar_1.9.0           #>  [76] igraph_2.0.3           ggpubr_0.6.0           rjson_0.2.21           #>  [79] ggsignif_0.6.4         codetools_0.2-19       biomaRt_2.54.1         #>  [82] XML_3.99-0.16.1        glue_1.7.0             evaluate_0.23          #>  [85] png_0.1-8              vctrs_0.6.5            foreach_1.5.2          #>  [88] tidyr_1.3.1            gtable_0.3.4           purrr_1.0.2            #>  [91] clue_0.3-65            ggplot2_3.5.0          cachem_1.0.8           #>  [94] xfun_0.42              broom_1.0.5            rstatix_0.7.2          #>  [97] ragg_1.3.0             tibble_3.2.1           iterators_1.0.14       #> [100] AnnotationDbi_1.60.2   memoise_2.0.1          cluster_2.1.6"},{"path":"https://FertigLab.github.io/dominoSignal/articles/domino_object_vignette.html","id":"object-contents","dir":"Articles","previous_headings":"","what":"Object contents","title":"Interacting with domino Objects","text":"great deal information stored domino object class. domino object S4 class object contains variety information data set used build object, calculated values, linkages receptors, ligands, transcription factors. object structured follows (examples information stored within slot: Input Data Information database used construct rl_map Inputted counts matrix Inputted z-scored counts matrix Inputted cluster labels Inputted transcription factor activation scores Calculated values Differential expression p-values transcription factors cluster Correlation values ligands receptors Median correlation components receptor complexes Linkages Complexes show component genes complexes rl map Receptor - ligand linkages determined rl map Transcription factor - target linkages determined SCENIC analysis (regulon inference method) Transcription factors differentially expressed cluster Transcription factors correlated receptors Transcription factors correlated receptors cluster Receptors active cluster Ligands may activate receptor given cluster (-called incoming ligands; may include ligands outside data set) Signaling matrices cluster, incoming ligands clusters within data set coming summary signaling clusters Miscellaneous Information Build information, includes parameters used build object build_domino() functions pared receptor ligand map information used building object percent expression receptors within cluster commonly accessed information (number cells, clusters, build information), show print methods domino objects can used.","code":"dom #> A domino object of 2607 cells #> Built with signaling between 9 clusters print(dom)"},{"path":"https://FertigLab.github.io/dominoSignal/articles/domino_object_vignette.html","id":"access-functions","dir":"Articles","previous_headings":"","what":"Access functions","title":"Interacting with domino Objects","text":"facilitate access information stored domino object, provided collection functions retrieve specific items. functions begin “dom_” can listed using ls().","code":"ls(\"package:dominoSignal\", pattern = \"^dom_\") #>  [1] \"dom_clusters\"      \"dom_correlations\"  \"dom_counts\"        #>  [4] \"dom_database\"      \"dom_de\"            \"dom_info\"          #>  [7] \"dom_linkages\"      \"dom_network_items\" \"dom_signaling\"     #> [10] \"dom_tf_activation\" \"dom_zscores\""},{"path":"https://FertigLab.github.io/dominoSignal/articles/domino_object_vignette.html","id":"input-data","dir":"Articles","previous_headings":"Access functions","what":"Input data","title":"Interacting with domino Objects","text":"creating domino object create_domino() function, several inputs required stored domino object . include cluster labels, counts matrix, z-scored counts matrix, transcription factor activation scores, R-L database used create_rl_map_cellphonedb(). example, access cluster names domino object: Setting argument labels = TRUE return vector cluster labels cell rather unique cluster names. access counts: z-scored counts: transcription factor activation scores can similarly accessed: Information database referenced ligand - receptor pairs composition protein complexes can extracted dom_database() function. default, function returns name(s) database(s) used: like view entire ligand - receptor map, set name_only = FALSE:","code":"dom_clusters(dom) #> [1] \"B_cell\"            \"CD14_monocyte\"     \"CD16_monocyte\"     #> [4] \"CD8_T_cell\"        \"dendritic_cell\"    \"memory_CD4_T_cell\" #> [7] \"naive_CD4_T_cell\"  \"NK_cell\"           \"Platelet\" count_matrix <- dom_counts(dom) knitr::kable(count_matrix[1:5, 1:5]) z_matrix <- dom_zscores(dom) knitr::kable(z_matrix[1:5, 1:5]) activation_matrix <- dom_tf_activation(dom) knitr::kable(activation_matrix[1:5, 1:5]) dom_database(dom) #> [1] \"CellPhoneDB_v4.0\" db_matrix <- dom_database(dom, name_only = FALSE) knitr::kable(db_matrix[1:5, 1:5])"},{"path":"https://FertigLab.github.io/dominoSignal/articles/domino_object_vignette.html","id":"calculations","dir":"Articles","previous_headings":"Access functions","what":"Calculations","title":"Interacting with domino Objects","text":"Active transcription factors cluster determined conducting Wilcoxon rank sum tests transcription factor transcription factor activity scores amongst cells cluster tested activity scores cells outside cluster. p-values one-sided test greater activity within cluster compared cells can accessed dom_de() function. Linkage receptors transcription factors assessed Spearman correlation transcription factor activity scores scaled expression receptor-encoding genes across cells data set. Spearman coefficients can accessed dom_correlations() function. Setting type “complex” return median correlation components receptor complexes; default (“rl”) return receptor - ligand correlations.","code":"de_matrix <- dom_de(dom) knitr::kable(de_matrix[1:5, 1:5]) cor_matrix <- dom_correlations(dom) knitr::kable(cor_matrix[1:5, 1:5])"},{"path":"https://FertigLab.github.io/dominoSignal/articles/domino_object_vignette.html","id":"linkages","dir":"Articles","previous_headings":"Access functions","what":"Linkages","title":"Interacting with domino Objects","text":"Linkages ligands, receptors, transcription factors can accessed several different ways, depending specific link scope desired. dom_linkages() function three arguments - first, like access functions, domino object. second, link_type, used specify linkages desired (options complexes, receptor - ligand, tf - target, tf - receptor). third argument, by_cluster, determines whether linkages returned arranged cluster (though change available linkage types tf - receptor, receptor, incoming-ligand). example, access complexes used across dataset: view incoming ligands cluster: , reason, find need entire linkage structure (recommended), can accessed slot name; domino objects S4 objects. Alternately, obtain simplified list receptors, ligands, /features domino object, use dom_network_items() function. pull transcription factors associated dendritic cell cluster:","code":"complex_links <- dom_linkages(dom, link_type = \"complexes\") # Look for components of NODAL receptor complex complex_links$NODAL_receptor #> NULL incoming_links <- dom_linkages(dom, link_type = \"incoming-ligand\", by_cluster = TRUE) # Check incoming signals to dendritic cells incoming_links$dendritic_cell #>  [1] \"COPA\"                  \"MIF\"                   \"APP\"                   #>  [4] \"FAM19A4\"               \"TAFA4\"                 \"ANXA1\"                 #>  [7] \"CD99\"                  \"integrin_aVb3_complex\" \"integrin_a4b1_complex\" #> [10] \"BMP8B\"                 \"PLAU\"                  \"CSF3\"                  #> [13] \"CXCL9\"                 \"HLA-F\"                 \"CD1D\"                  #> [16] \"INS\"                   \"IL34\"                  \"CSF1\"                  #> [19] \"CSF2\"                  \"CTLA4\"                 \"CD28\"                  #> [22] \"GRN\"                   \"TNF\"                   \"LTA\"                   #> [25] \"CXCL12\"                \"CXCL14\"                \"CD58\" all_linkages <- slot(dom, \"linkages\") # Names of all sub-structures: names(all_linkages) #> [1] \"complexes\"          \"rec_lig\"            \"tf_targets\"         #> [4] \"clust_tf\"           \"tf_rec\"             \"clust_tf_rec\"       #> [7] \"clust_rec\"          \"clust_incoming_lig\" dc_tfs <- dom_network_items(dom, \"dendritic_cell\", return = \"features\") head(dc_tfs) #> [1] \"ATF3\"  \"CEBPD\" \"FOSB\"  \"STAT6\" \"KLF4\"  \"CEBPA\""},{"path":"https://FertigLab.github.io/dominoSignal/articles/domino_object_vignette.html","id":"signaling-matrices","dir":"Articles","previous_headings":"Access functions","what":"Signaling Matrices","title":"Interacting with domino Objects","text":"averaged z-scored expression ligands receptors different clusters can accessed matrix form. view signaling specific cluster clusters, set cluster argument cluster name.","code":"signal_matrix <- dom_signaling(dom) knitr::kable(signal_matrix) dc_matrix <- dom_signaling(dom, \"dendritic_cell\") knitr::kable(dc_matrix)"},{"path":"https://FertigLab.github.io/dominoSignal/articles/domino_object_vignette.html","id":"build-information","dir":"Articles","previous_headings":"Access functions","what":"Build information","title":"Interacting with domino Objects","text":"keep track options set running build_domino(), stored within domino object . view options, use dom_info() function.","code":"dom_info(dom) #> $create #> [1] TRUE #>  #> $build #> [1] TRUE #>  #> $build_variables #>     max_tf_per_clust          min_tf_pval       max_rec_per_tf  #>               25.000                0.001               25.000  #> rec_tf_cor_threshold   min_rec_percentage  #>                0.250                0.100"},{"path":"https://FertigLab.github.io/dominoSignal/articles/domino_object_vignette.html","id":"continued-development","dir":"Articles","previous_headings":"","what":"Continued Development","title":"Interacting with domino Objects","text":"Since dominoSignal package still developed, new functions features implemented future versions. meantime, put together information plotting example analysis can viewed Getting Started page. Additionally, find bugs, questions, want share idea, please let us know . Vignette Build Information Date last built session information:","code":"Sys.Date() #> [1] \"2024-06-25\" sessionInfo() #> R version 4.2.1 (2022-06-23) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #>  #> Matrix products: default #> BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 #> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 #>  #> locale: #>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               #>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     #>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    #>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  #>  [9] LC_ADDRESS=C               LC_TELEPHONE=C             #> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C        #>  #> attached base packages: #> [1] stats     graphics  grDevices utils     datasets  methods   base      #>  #> other attached packages: #> [1] dominoSignal_0.99.2 #>  #> loaded via a namespace (and not attached): #>   [1] bitops_1.0-7           matrixStats_1.2.0      fs_1.6.3               #>   [4] bit64_4.0.5            filelock_1.0.3         doParallel_1.0.17      #>   [7] RColorBrewer_1.1-3     progress_1.2.3         httr_1.4.7             #>  [10] GenomeInfoDb_1.34.9    backports_1.4.1        tools_4.2.1            #>  [13] bslib_0.6.1            utf8_1.2.4             R6_2.5.1               #>  [16] DBI_1.2.2              BiocGenerics_0.44.0    colorspace_2.1-0       #>  [19] GetoptLong_1.0.5       withr_3.0.0            tidyselect_1.2.1       #>  [22] prettyunits_1.2.0      bit_4.0.5              curl_5.2.1             #>  [25] compiler_4.2.1         textshaping_0.3.7      cli_3.6.2              #>  [28] Biobase_2.58.0         formatR_1.14           xml2_1.3.6             #>  [31] desc_1.4.3             sass_0.4.8             scales_1.3.0           #>  [34] rappdirs_0.3.3         pkgdown_2.0.7          systemfonts_1.0.6      #>  [37] stringr_1.5.1          digest_0.6.35          rmarkdown_2.26         #>  [40] XVector_0.38.0         pkgconfig_2.0.3        htmltools_0.5.7        #>  [43] dbplyr_2.5.0           fastmap_1.1.1          rlang_1.1.3            #>  [46] GlobalOptions_0.1.2    RSQLite_2.3.5          shape_1.4.6.1          #>  [49] jquerylib_0.1.4        generics_0.1.3         jsonlite_1.8.8         #>  [52] car_3.1-2              dplyr_1.1.4            RCurl_1.98-1.14        #>  [55] magrittr_2.0.3         GenomeInfoDbData_1.2.9 Matrix_1.6-5           #>  [58] munsell_0.5.0          Rcpp_1.0.12            S4Vectors_0.36.2       #>  [61] fansi_1.0.6            abind_1.4-5            lifecycle_1.0.4        #>  [64] stringi_1.8.3          yaml_2.3.8             carData_3.0-5          #>  [67] zlibbioc_1.44.0        plyr_1.8.9             BiocFileCache_2.11.2   #>  [70] grid_4.2.1             blob_1.2.4             parallel_4.2.1         #>  [73] crayon_1.5.2           lattice_0.22-5         Biostrings_2.66.0      #>  [76] circlize_0.4.16        hms_1.1.3              KEGGREST_1.38.0        #>  [79] knitr_1.45             ComplexHeatmap_2.14.0  pillar_1.9.0           #>  [82] igraph_2.0.3           ggpubr_0.6.0           rjson_0.2.21           #>  [85] ggsignif_0.6.4         codetools_0.2-19       biomaRt_2.54.1         #>  [88] stats4_4.2.1           XML_3.99-0.16.1        glue_1.7.0             #>  [91] evaluate_0.23          png_0.1-8              vctrs_0.6.5            #>  [94] foreach_1.5.2          tidyr_1.3.1            gtable_0.3.4           #>  [97] purrr_1.0.2            clue_0.3-65            ggplot2_3.5.0          #> [100] cachem_1.0.8           xfun_0.42              broom_1.0.5            #> [103] rstatix_0.7.2          ragg_1.3.0             tibble_3.2.1           #> [106] iterators_1.0.14       AnnotationDbi_1.60.2   memoise_2.0.1          #> [109] IRanges_2.32.0         cluster_2.1.6"},{"path":"https://FertigLab.github.io/dominoSignal/articles/plotting_vignette.html","id":"setup-and-data-load","dir":"Articles","previous_headings":"","what":"Setup and Data Load","title":"Plotting Functions and Options","text":"tutorial, use domino object built Getting Started page. yet built domino object, can following instructions Getting Started page. Instructions load data","code":"set.seed(42) library(dominoSignal) library(patchwork) # BiocFileCache helps with managing files across sessions bfc <- BiocFileCache::BiocFileCache(ask = FALSE) data_url <- \"https://zenodo.org/records/10951634/files/pbmc_domino_built.rds\" tmp_path <- BiocFileCache::bfcrpath(bfc, data_url) dom <- readRDS(tmp_path)"},{"path":[]},{"path":"https://FertigLab.github.io/dominoSignal/articles/plotting_vignette.html","id":"correlations-between-receptors-and-transcription-factors","dir":"Articles","previous_headings":"Heatmaps","what":"Correlations between receptors and transcription factors","title":"Plotting Functions and Options","text":"cor_heatmap() can used show correlations calculated receptors transcription factors.  addition displaying scores correlations, function can also used identify correlations certain value (using bool bool_thresh arguments) identify combinations receptors transcription factors (TFs) connected (argument mark_connections).  subset receptors transcription factors interest, vector either () can passed function.  heatmap functions dominoSignal based ComplexHeatmap::Heatmap() also accept additional arguments meant function. example, argument clustering rows columns heatmap explicitly stated, can still passed ComplexHeatmap::Heatmap() cor_heatmap().","code":"cor_heatmap(dom, title = \"PBMC R-TF Correlations\", column_names_gp = grid::gpar(fontsize = 8)) cor_heatmap(dom, bool = TRUE, bool_thresh = 0.25) cor_heatmap(dom, bool = FALSE, mark_connections = TRUE) receptors <- c(\"CSF1R\", \"CSF3R\", \"CCR7\", \"FCER2\") tfs <- c(\"PAX5\", \"JUNB\", \"FOXJ3\", \"FOSB\") cor_heatmap(dom, feats = tfs, recs = receptors) cor_heatmap(dom, cluster_rows = FALSE, cluster_columns = FALSE, column_title = \"Heatmap Without Clustering\",     column_names_gp = grid::gpar(fontsize = 4))"},{"path":"https://FertigLab.github.io/dominoSignal/articles/plotting_vignette.html","id":"heatmap-of-transcription-factor-activation-scores","dir":"Articles","previous_headings":"Heatmaps","what":"Heatmap of Transcription Factor Activation Scores","title":"Plotting Functions and Options","text":"feat_heatmap() used show transcription factor activation features signaling network.  functions similarly cor_heatmap(), arguments select specific vector features, use boolean view threshold, pass arguments ComplexHeatmap::Heatmap(). Specific function though arguments setting range values visualized one choose normalize scores max value.","code":"feat_heatmap(dom, use_raster = FALSE, row_names_gp = grid::gpar(fontsize = 4)) feat_heatmap(dom, min_thresh = 0.1, max_thresh = 0.6, norm = TRUE, bool = FALSE,     use_raster = FALSE) feat_heatmap(dom, bool = TRUE, use_raster = FALSE)"},{"path":"https://FertigLab.github.io/dominoSignal/articles/plotting_vignette.html","id":"heatmap-of-incoming-signaling-for-a-cluster","dir":"Articles","previous_headings":"Heatmaps","what":"Heatmap of Incoming Signaling for a Cluster","title":"Plotting Functions and Options","text":"incoming_signaling_heatmap() can used visualize cluster average expression ligands capable activating TFs enriched cluster. example, view incoming signaling CD8 T cells:  can also select specific clusters interest signaling CD8 T cells. interested viewing monocyte signaling:  heatmap functions, options available minimum threshold, maximum threshold, whether scale values thresholding, whether normalize matrix, ability pass arguments ComplexHeatmap::Heatmap().","code":"incoming_signaling_heatmap(dom, \"CD8_T_cell\") incoming_signaling_heatmap(dom, \"CD8_T_cell\", clusts = c(\"CD14_monocyte\", \"CD16_monocyte\"))"},{"path":"https://FertigLab.github.io/dominoSignal/articles/plotting_vignette.html","id":"heatmap-of-signaling-between-clusters","dir":"Articles","previous_headings":"Heatmaps","what":"Heatmap of Signaling Between Clusters","title":"Plotting Functions and Options","text":"signaling_heatmap() makes heatmap showing signaling strength ligands cluster receptors cluster based averaged expression.  functions, specific clusters can selected, thresholds can set, normalization methods can selected well.","code":"signaling_heatmap(dom) signaling_heatmap(dom, scale = \"sqrt\") signaling_heatmap(dom, normalize = \"rec_norm\")"},{"path":[]},{"path":"https://FertigLab.github.io/dominoSignal/articles/plotting_vignette.html","id":"network-showing-l---r---tf-signaling-between-clusters","dir":"Articles","previous_headings":"Network Plots","what":"Network showing L - R - TF signaling between clusters","title":"Plotting Functions and Options","text":"gene_network() makes network plot display signaling selected clusters ligands, receptors features associated clusters displayed nodes edges linkages. look signaling CD16 Monocytes CD14 Monocytes:  Options modify plot include adjusting scaling ligands different layouts (legible others).  Additionally, colors can given select genes (example, highlight specific signaling path).","code":"gene_network(dom, clust = \"CD16_monocyte\", OutgoingSignalingClust = \"CD14_monocyte\") gene_network(dom, clust = \"CD16_monocyte\", OutgoingSignalingClust = \"CD14_monocyte\",     lig_scale = 25, layout = \"sphere\") gene_network(dom, clust = \"CD16_monocyte\", OutgoingSignalingClust = \"CD14_monocyte\",     cols = c(CD1D = \"violet\", LILRB2 = \"violet\", FOSB = \"violet\"), lig_scale = 10)"},{"path":"https://FertigLab.github.io/dominoSignal/articles/plotting_vignette.html","id":"network-showing-interaction-strength-across-data","dir":"Articles","previous_headings":"Network Plots","what":"Network Showing Interaction Strength Across Data","title":"Plotting Functions and Options","text":"signaling_network() can used create network plot nodes clusters edges indicate signaling one cluster another.  addition changes scaling, thresholds, layouts, plot can modified selecting incoming outgoing clusters (!). example view signaling CD14 Monocytes clusters:","code":"signaling_network(dom) signaling_network(dom, showOutgoingSignalingClusts = \"CD14_monocyte\", scale = \"none\",     norm = \"none\", layout = \"fr\", scale_by = \"none\", edge_weight = 2, vert_scale = 10)"},{"path":[]},{"path":"https://FertigLab.github.io/dominoSignal/articles/plotting_vignette.html","id":"chord-diagrams-connecting-ligands-and-receptors","dir":"Articles","previous_headings":"Other Types of Plots","what":"Chord Diagrams Connecting Ligands and Receptors","title":"Plotting Functions and Options","text":"new addition dominoSignal, circos_ligand_receptor() creates chord plot showing ligands can activate specific receptor, displaying mean cluster expression ligand width chord.  function can given cluster colors match plots may make data. addition, plot can adjusted changing threshold ligand expression required linkage visualized selecting clusters interest.","code":"circos_ligand_receptor(dom, receptor = \"CD74\") cols <- c(\"red\", \"orange\", \"green\", \"blue\", \"pink\", \"purple\", \"slategrey\", \"firebrick\",     \"hotpink\") names(cols) <- dom_clusters(dom, labels = FALSE) circos_ligand_receptor(dom, receptor = \"CD74\", cell_colors = cols)"},{"path":"https://FertigLab.github.io/dominoSignal/articles/plotting_vignette.html","id":"scatter-plot-to-visualize-correlation","dir":"Articles","previous_headings":"Other Types of Plots","what":"Scatter Plot to Visualize Correlation","title":"Plotting Functions and Options","text":"cor_scatter() can used plot cell based activation selected TF expression receptor. produces scatter plot well line best fit look receptor - TF correlation.  keep mind argument remove_rec_dropout match parameter used domino object built. case, use build parameter, leave value argument default value FALSE.","code":"cor_scatter(dom, \"FOSB\", \"CD74\")"},{"path":"https://FertigLab.github.io/dominoSignal/articles/plotting_vignette.html","id":"continued-development","dir":"Articles","previous_headings":"","what":"Continued Development","title":"Plotting Functions and Options","text":"Since dominoSignal package still developed, new functions features implemented future versions. meantime, view example analysis, see Getting Started page, see domino object structure page get familiar object structure. Additionally, find bugs, questions, want share idea, please let us know . Vignette Build Information Date last built session information:","code":"Sys.Date() #> [1] \"2024-06-25\" sessionInfo() #> R version 4.2.1 (2022-06-23) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #>  #> Matrix products: default #> BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 #> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 #>  #> locale: #>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               #>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     #>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    #>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  #>  [9] LC_ADDRESS=C               LC_TELEPHONE=C             #> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C        #>  #> attached base packages: #> [1] stats     graphics  grDevices utils     datasets  methods   base      #>  #> other attached packages: #> [1] patchwork_1.2.0.9000 dominoSignal_0.99.2  #>  #> loaded via a namespace (and not attached): #>   [1] nlme_3.1-164           bitops_1.0-7           matrixStats_1.2.0      #>   [4] fs_1.6.3               bit64_4.0.5            filelock_1.0.3         #>   [7] doParallel_1.0.17      RColorBrewer_1.1-3     progress_1.2.3         #>  [10] httr_1.4.7             GenomeInfoDb_1.34.9    backports_1.4.1        #>  [13] tools_4.2.1            bslib_0.6.1            utf8_1.2.4             #>  [16] R6_2.5.1               mgcv_1.9-1             DBI_1.2.2              #>  [19] BiocGenerics_0.44.0    colorspace_2.1-0       GetoptLong_1.0.5       #>  [22] withr_3.0.0            tidyselect_1.2.1       prettyunits_1.2.0      #>  [25] bit_4.0.5              curl_5.2.1             compiler_4.2.1         #>  [28] textshaping_0.3.7      cli_3.6.2              Biobase_2.58.0         #>  [31] formatR_1.14           Cairo_1.6-2            xml2_1.3.6             #>  [34] desc_1.4.3             labeling_0.4.3         sass_0.4.8             #>  [37] scales_1.3.0           rappdirs_0.3.3         pkgdown_2.0.7          #>  [40] systemfonts_1.0.6      stringr_1.5.1          digest_0.6.35          #>  [43] rmarkdown_2.26         XVector_0.38.0         pkgconfig_2.0.3        #>  [46] htmltools_0.5.7        highr_0.10             dbplyr_2.5.0           #>  [49] fastmap_1.1.1          rlang_1.1.3            GlobalOptions_0.1.2    #>  [52] RSQLite_2.3.5          farver_2.1.1           shape_1.4.6.1          #>  [55] jquerylib_0.1.4        generics_0.1.3         jsonlite_1.8.8         #>  [58] car_3.1-2              dplyr_1.1.4            RCurl_1.98-1.14        #>  [61] magrittr_2.0.3         GenomeInfoDbData_1.2.9 Matrix_1.6-5           #>  [64] munsell_0.5.0          Rcpp_1.0.12            S4Vectors_0.36.2       #>  [67] fansi_1.0.6            abind_1.4-5            lifecycle_1.0.4        #>  [70] stringi_1.8.3          yaml_2.3.8             carData_3.0-5          #>  [73] zlibbioc_1.44.0        plyr_1.8.9             BiocFileCache_2.11.2   #>  [76] grid_4.2.1             blob_1.2.4             parallel_4.2.1         #>  [79] crayon_1.5.2           lattice_0.22-5         splines_4.2.1          #>  [82] Biostrings_2.66.0      circlize_0.4.16        hms_1.1.3              #>  [85] KEGGREST_1.38.0        magick_2.8.3           knitr_1.45             #>  [88] ComplexHeatmap_2.14.0  pillar_1.9.0           igraph_2.0.3           #>  [91] ggpubr_0.6.0           rjson_0.2.21           ggsignif_0.6.4         #>  [94] codetools_0.2-19       biomaRt_2.54.1         stats4_4.2.1           #>  [97] XML_3.99-0.16.1        glue_1.7.0             evaluate_0.23          #> [100] png_0.1-8              vctrs_0.6.5            foreach_1.5.2          #> [103] tidyr_1.3.1            gtable_0.3.4           purrr_1.0.2            #> [106] clue_0.3-65            ggplot2_3.5.0          cachem_1.0.8           #> [109] xfun_0.42              broom_1.0.5            rstatix_0.7.2          #> [112] ragg_1.3.0             tibble_3.2.1           iterators_1.0.14       #> [115] AnnotationDbi_1.60.2   memoise_2.0.1          IRanges_2.32.0         #> [118] cluster_2.1.6"},{"path":"https://FertigLab.github.io/dominoSignal/articles/tf_scenic_vignette.html","id":"file-downloads-for-scenic","dir":"Articles","previous_headings":"","what":"File Downloads for SCENIC","title":"SCENIC for TF Activation Scoring","text":"tutorial, pySCENIC used method TF activity inference. requires downloading files prior use. , show download files way run pySCENIC generate necessary files use dominoSignal analyis. singularity image SCENIC v0.12.1 can installed DockerHub image source. SCENIC requires list TFs, motif annotations, cisTarget motifs available authors SCENIC human (HGNC), mouse (MGI), fly. following download everything necessary analysis data set HGNC gene labels hg38 genome.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/articles/tf_scenic_vignette.html","id":"reference-files","dir":"Articles","previous_headings":"File Downloads for SCENIC","what":"Reference files","title":"SCENIC for TF Activation Scoring","text":"First set temporary directory store files. build singularity image download reference files temporary directory.","code":"temp_dir <- tempdir() SCENIC_DIR=\"'temp_dir'/scenic\" mkdir ${SCENIC_DIR}  # Build singularity image singularity build \"${SCENIC_DIR}/aertslab-pyscenic-0.12.1.sif\" docker://aertslab/pyscenic:0.12.1  # Matrix containing motifs as rows and genes as columns and ranking position for each gene and motif (based on CRM scores) as values. URLs provided link to v2 feather files required for the 0.12.1 version of pySENIC.  curl \"https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg38/refseq_r80/mc_v10_clust/gene_based/hg38_10kbp_up_10kbp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather\" \\   -o \"${SCENIC_DIR}/hg38_10kbp_up_10kbp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather\"  curl \"https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg38/refseq_r80/mc_v10_clust/gene_based/hg38_500bp_up_100bp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather\" \\   -o \"${SCENIC_DIR}/hg38_500bp_up_100bp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather\"  # List of genes encoding TFs in the HG38 reference genome curl \"https://resources.aertslab.org/cistarget/tf_lists/allTFs_hg38.txt\" \\   -o \"${SCENIC_DIR}/allTFs_hg38.txt\"  # Motif annotations based on the 2017 cisTarget motif collection. Use these files if you are using the mc9nr databases. curl \"https://resources.aertslab.org/cistarget/motif2tf/motifs-v9-nr.hgnc-m0.001-o0.0.tbl\" \\   -o \"${SCENIC_DIR}/motifs-v9-nr.hgnc-m0.001-o0.0.tbl\""},{"path":"https://FertigLab.github.io/dominoSignal/articles/tf_scenic_vignette.html","id":"preparing-preprocessed-data","dir":"Articles","previous_headings":"File Downloads for SCENIC","what":"Preparing Preprocessed Data:","title":"SCENIC for TF Activation Scoring","text":"dominoSignal designed compatible single cell workflows, accepting gene cell matrices counts scaled counts. However, pySCENIC used TF activation inference, requires input cell gene matrix. opposite orientation many R based single cell packages (Seurat SingleCellExperiment) default Python based tools scanpy. RNA counts matrix can saved tab-seperated value (.tsv) comma-sperated value (.csv) file transposing matrix cell gene orientation. example extracting counts SingleCellExperiment object saving .tsv file. tsv csv files inefficient storing large matrices. alternative, counts matrix can saved loom file directly passed pySCENIC format. Generating loom file R requires use loomR package. package automatically handles transposition counts matrix cell gene orientation well. recommend approach passing counts matrix pySCENIC. However, users aware loomR hosted CRAN Bioconductor time vignette’s creation. use pbmc3k\\_counts.loom file tutorial.","code":"pbmc_counts <- assay(pbmc, \"counts\") write.table(t(as.matrix(pbmc_counts)), paste0(input_dir, \"/pbmc3k_counts.tsv\"), sep = \"\\t\",     col.names = NA) library(loomR)  # save loom counts matrix pbmc_counts <- assay(pbmc, \"counts\") pbmc_loom <- loomR::create(filename = paste0(input_dir, \"/pbmc3k_counts.loom\"), data = pbmc_counts) # connection to the loom file must be closed to complete file creation pbmc_loom$close_all()"},{"path":"https://FertigLab.github.io/dominoSignal/articles/tf_scenic_vignette.html","id":"running-scenic","dir":"Articles","previous_headings":"","what":"Running SCENIC","title":"SCENIC for TF Activation Scoring","text":"pySCENIC initiated using bash scripting terminal. analysis consists three steps score genes TF motif enrichment, construct TF regulons consisting genes targeted TFs, arrive AUC scores enrichment regulon gene transcription within cell.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/articles/tf_scenic_vignette.html","id":"grn-construct-tf-modules","dir":"Articles","previous_headings":"Running SCENIC","what":"grn: construct TF-modules","title":"SCENIC for TF Activation Scoring","text":"Co-expression modules used quantify gene-TF adjacencies.","code":"singularity exec \"${SCENIC_DIR}/aertslab-pyscenic-0.12.1.sif\" pyscenic grn \\     \"pbmc3k_counts.loom\" \\     \"${SCENIC_DIR}/allTFs_hg38.txt\" \\     -o \"${SCENIC_DIR}/pbmc_adj_3k.tsv\" \\     --num_workers 6 \\     --seed 123  # Arguments:     # path to the loom file     # list of TFs     # output directory for the adjacency matrix     # number of CPUs to use if multi-core processing is available     # specify a random seed for reproducibility"},{"path":"https://FertigLab.github.io/dominoSignal/articles/tf_scenic_vignette.html","id":"ctx-construct-tf-regulons-with-pruning-based-on-tf-motif-enrichment","dir":"Articles","previous_headings":"Running SCENIC","what":"ctx: construct TF regulons with pruning based on TF motif enrichment","title":"SCENIC for TF Activation Scoring","text":"rankings genes based enrichment TF motifs transcription start site (TSS) considered construction regulons, target genes TF-modules removed lack motifs proximal TSS TF may bind.","code":"singularity exec \"${SCENIC_DIR}/aertslab-pyscenic-0.12.1.sif\" pyscenic ctx \\     \"${SCENIC_DIR}/pbmc_adj.tsv\" \\     \"${SCENIC_DIR}/hg38_10kbp_up_10kbp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather\" \\     \"${SCENIC_DIR}/hg38_500bp_up_100bp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather\" \\     --annotations_fname \"${SCENIC_DIR}/motifs-v9-nr.hgnc-m0.001-o0.0.tbl\" \\     --expression_mtx_fname \"pbmc3k_counts.loom\" \\     --mode \"dask_multiprocessing\" \\     --output \"${SCENIC_DIR}/regulons_pbmc_3k.csv\" \\     --num_workers 1                                                            # Arguments:     # adjacency matrix output from grn     # target rankings of motif enrichment within 10 kb of TSS     # target rankings of motif enrichment within 500 bp upstream and 100 bp downstream of TSS     # TF motif features     # counts matrix loom file     # enable multi-core processing     # output file of learned TF regulons     # number of CPU cores"},{"path":"https://FertigLab.github.io/dominoSignal/articles/tf_scenic_vignette.html","id":"aucell-calculate-tf-activity-scores","dir":"Articles","previous_headings":"Running SCENIC","what":"aucell: calculate TF activity scores","title":"SCENIC for TF Activation Scoring","text":"Enrichment regulon measured Area recovery Curve (AUC) genes define regulon. csv files created analysis can used TF activation inputs dominoSignal analysis. Please see Getting Started page information perform analysis dominoSignal. brief tutorial download necessary files using CellPhoneDB, please see Using CellPhoneDB database vignette. Vignette Build Information Date last built session information:","code":"singularity exec \"${SCENIC_DIR}/aertslab-pyscenic-0.12.1.sif\" pyscenic aucell \\     \"pbmc3k_counts.loom\" \\     \"${SCENIC_DIR}/regulons_pbmc_3k.csv\" \\     -o \"${SCENIC_DIR}/auc_pbmc_3k.csv\"      # Arguments:     # counts matrix loom file     # regulon table output from ctx     # cell x TF matrix of TF enrichment AUC values Sys.Date() #> [1] \"2024-06-25\" sessionInfo() #> R version 4.2.1 (2022-06-23) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #>  #> Matrix products: default #> BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 #> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 #>  #> locale: #>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               #>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     #>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    #>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  #>  [9] LC_ADDRESS=C               LC_TELEPHONE=C             #> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C        #>  #> attached base packages: #> [1] stats     graphics  grDevices utils     datasets  methods   base      #>  #> loaded via a namespace (and not attached): #>  [1] digest_0.6.35     R6_2.5.1          lifecycle_1.0.4   jsonlite_1.8.8    #>  [5] formatR_1.14      magrittr_2.0.3    evaluate_0.23     rlang_1.1.3       #>  [9] cachem_1.0.8      cli_3.6.2         fs_1.6.3          jquerylib_0.1.4   #> [13] bslib_0.6.1       ragg_1.3.0        vctrs_0.6.5       rmarkdown_2.26    #> [17] pkgdown_2.0.7     textshaping_0.3.7 desc_1.4.3        tools_4.2.1       #> [21] purrr_1.0.2       yaml_2.3.8        xfun_0.42         fastmap_1.1.1     #> [25] compiler_4.2.1    systemfonts_1.0.6 memoise_2.0.1     htmltools_0.5.7   #> [29] knitr_1.45        sass_0.4.8"},{"path":"https://FertigLab.github.io/dominoSignal/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Christopher Cherry. Author. Jacob T Mitchell. Author, maintainer. Sushma Nagaraj. Author. Kavita Krishnan. Author. Dmitrijs Lvovs. Author. Elana Fertig. Contributor. Jennifer Elisseeff. Contributor.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Cherry C, Maestas D, Han J, Andorko J, Cahan P, Fertig E, Garmire L, Elisseeff J (2021). “Computational reconstruction signalling networks surrounding implanted biomaterials single-cell transcriptomics.” Nature Biomedical Engineering, 5(10), 1228-1238. doi:10.1038/s41551-021-00770-5, https://doi.org/10.1038%2Fs41551-021-00770-5. Cherry C, Mitchell J, Nagaraj S, Krishnan K, Fertig E, Elisseeff J (2024). dominoSignal: Cell Communication Analysis Single Cell RNA Sequencing. R package version 0.99.1.","code":"@Article{,   title = {Computational reconstruction of the signalling networks surrounding implanted biomaterials from single-cell transcriptomics},   author = {Christopher Cherry and David R. Maestas and Jin Han and James I. Andorko and Patrick Cahan and Elana J. Fertig and Lana X. Garmire and Jennifer H. Elisseeff},   journal = {Nature Biomedical Engineering},   year = {2021},   volume = {5},   number = {10},   pages = {1228-1238},   doi = {10.1038/s41551-021-00770-5},   url = {https://doi.org/10.1038%2Fs41551-021-00770-5}, } @Manual{,   title = {dominoSignal: Cell Communication Analysis for Single Cell RNA Sequencing},   author = {Christopher Cherry and Jacob T Mitchell and Sushma Nagaraj and Kavita Krishnan and Elana Fertig and Jennifer Elisseeff},   year = {2024},   note = {R package version 0.99.1}, }"},{"path":"https://FertigLab.github.io/dominoSignal/index.html","id":"introducing-dominosignal-","dir":"","previous_headings":"","what":"Introducing dominoSignal","title":"Cell Communication from Single Cell RNA Sequencing Data","text":"dominoSignal updated version original domino R package published Nature Biomedical Engineering Computational reconstruction signalling networks surrounding implanted biomaterials single-cell transcriptomics. dominoSignal tool analysis intra- intercellular signaling single cell RNA sequencing data based transcription factor activation receptor ligand linkages.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Cell Communication from Single Cell RNA Sequencing Data","text":"version currently hosted FertigLab GitHub dominoSignal repository forked primary repository hosted Elisseeff-Lab GitHub, can installed using remotes package. current stable version active updates reproducible usage (see changelog information changes).","code":"if (!require(remotes)) {     install.packages(\"remotes\") } remotes::install_github(\"FertigLab/dominoSignal\")"},{"path":"https://FertigLab.github.io/dominoSignal/index.html","id":"usage-overview","dir":"","previous_headings":"","what":"Usage Overview","title":"Cell Communication from Single Cell RNA Sequencing Data","text":"overview dominoSignal might used analysis single cell RNA sequencing data set: Transcription factor activation scores calculated (recommend using pySCENIC, methods can used well). information use SCENIC, please see Using SCENIC TF Activation page. ligand-receptor database used map linkages ligands receptors (recommend using cellphoneDB, methods can used well). information downloading necessary files cellphoneDB, please see Using cellphoneDB Database page. domino object created using counts, z-scored counts, clustering information, data steps 1 2. Parameters maximum number transcription factors receptors minimum correlation threshold (among others) used make cell communication network Communication networks can extracted within domino object visualized using variety plotting functions Please see Getting Started page example analysis includes steps dominoSignal analysis detail, creating domino object parameters building network visualizing domino results. articles include details plotting functions structure domino object.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/add_rl_column.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a column to the RL signaling data frame. — add_rl_column","title":"Adds a column to the RL signaling data frame. — add_rl_column","text":"function adds column internal rl 'map' used map receptor receptor complexes ligand ligand complexes.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/add_rl_column.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a column to the RL signaling data frame. — add_rl_column","text":"","code":"add_rl_column(map, map_ref, conv, new_name)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/add_rl_column.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a column to the RL signaling data frame. — add_rl_column","text":"map RL signaling data frame. map_ref Name column match new data conv Data frame matching current data map new data. new_name Name new column created RL map","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/add_rl_column.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a column to the RL signaling data frame. — add_rl_column","text":"updated RL signaling data frame","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/add_rl_column.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds a column to the RL signaling data frame. — add_rl_column","text":"","code":"lr_name <- data.frame(\"abbrev\" = c(\"L\", \"R\"), \"full\" = c(\"Ligand\", \"Receptor\")) rl_map_expanded <- add_rl_column(map = dominoSignal:::rl_map_tiny, map_ref = \"type_A\",  conv = lr_name, new_name = \"type_A_full\")"},{"path":"https://FertigLab.github.io/dominoSignal/reference/build_domino.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate a signaling network for a domino object — build_domino","title":"Calculate a signaling network for a domino object — build_domino","text":"function calculates signaling network. requires domino object preprocessed create_domino returns domino object prepared plotting various plotting functions package.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/build_domino.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate a signaling network for a domino object — build_domino","text":"","code":"build_domino(   dom,   max_tf_per_clust = 5,   min_tf_pval = 0.01,   max_rec_per_tf = 5,   rec_tf_cor_threshold = 0.15,   min_rec_percentage = 0.1 )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/build_domino.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate a signaling network for a domino object — build_domino","text":"dom Domino object create_domino(). max_tf_per_clust Maximum number transcription factors called active cluster. min_tf_pval Minimum p-value differential feature score test call transcription factor active cluster. max_rec_per_tf Maximum number receptors link transcription factor. rec_tf_cor_threshold Minimum Spearman correlation used consider receptor linked transcription factor. Increasing decrease number receptors linked transcription factor. min_rec_percentage Minimum percentage cells cluster expressing receptor receptor linked trancription factors cluster.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/build_domino.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate a signaling network for a domino object — build_domino","text":"domino object signaling network built","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/build_domino.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate a signaling network for a domino object — build_domino","text":"","code":"pbmc_dom_tiny_built <- build_domino(  dom = dominoSignal:::pbmc_dom_tiny, min_tf_pval = .001, max_tf_per_clust = 25,  max_rec_per_tf = 25, rec_tf_cor_threshold = .25, min_rec_percentage = 0.1 )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/check_arg.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input arguments — check_arg","title":"Check input arguments — check_arg","text":"Accepts object rules check ; stops requirements met","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/check_arg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input arguments — check_arg","text":"","code":"check_arg(   arg,   allow_class = NULL,   allow_len = NULL,   allow_range = NULL,   allow_values = NULL,   need_vars = c(NULL),   need_colnames = FALSE,   need_rownames = FALSE,   need_names = FALSE )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/check_arg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input arguments — check_arg","text":"arg argument check allow_class vector allowed classes allow_len vector allowed lengths allow_range range minimum maximum values .e. c(1, 5) allow_values vector allowed values need_vars vector required variables need_colnames vogical whether colnames required need_rownames logical whether rownames required need_names logical whether names required","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/check_arg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input arguments — check_arg","text":"Logical indicating whether argument meets requirements","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/circos_ligand_receptor.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot expression of a receptor's ligands by other cell types as a chord plot — circos_ligand_receptor","title":"Plot expression of a receptor's ligands by other cell types as a chord plot — circos_ligand_receptor","text":"Creates chord plot expression ligands can activate specified receptor chord widths correspond mean ligand expression cluster.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/circos_ligand_receptor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot expression of a receptor's ligands by other cell types as a chord plot — circos_ligand_receptor","text":"","code":"circos_ligand_receptor(   dom,   receptor,   ligand_expression_threshold = 0.01,   cell_idents = NULL,   cell_colors = NULL )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/circos_ligand_receptor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot expression of a receptor's ligands by other cell types as a chord plot — circos_ligand_receptor","text":"dom Domino object undergone network building build_domino() receptor Name receptor active least one cell type domino object ligand_expression_threshold Minimum mean expression value ligand cell type chord rendered cell type receptor cell_idents Vector cell types cluster assignments domino object included plot. cell_colors Named vector color names hex codes names correspond plotted cell types color values","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/circos_ligand_receptor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot expression of a receptor's ligands by other cell types as a chord plot — circos_ligand_receptor","text":"Renders circos plot active graphics device","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/circos_ligand_receptor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot expression of a receptor's ligands by other cell types as a chord plot — circos_ligand_receptor","text":"","code":"#basic usage circos_ligand_receptor(dominoSignal:::pbmc_dom_built_tiny, receptor = \"CXCR3\") #> There are more than one numeric columns in the data frame. Take the #> first two numeric columns and draw the link ends with unequal width. #>  #> Type `circos.par$message = FALSE` to suppress the message.  #specify colors cols = c(\"red\", \"orange\", \"green\", \"blue\", \"pink\", \"purple\", \"slategrey\", \"firebrick\", \"hotpink\") names(cols) = levels(dom_clusters(dominoSignal:::pbmc_dom_built_tiny)) circos_ligand_receptor(dominoSignal:::pbmc_dom_built_tiny, receptor = \"CXCR3\", cell_colors = cols) #> There are more than one numeric columns in the data frame. Take the #> first two numeric columns and draw the link ends with unequal width. #>  #> Type `circos.par$message = FALSE` to suppress the message."},{"path":"https://FertigLab.github.io/dominoSignal/reference/conv_py_bools.html","id":null,"dir":"Reference","previous_headings":"","what":"Change cases of True/False syntax from Python to TRUE/FALSE R syntax — conv_py_bools","title":"Change cases of True/False syntax from Python to TRUE/FALSE R syntax — conv_py_bools","text":"Change cases True/False syntax Python TRUE/FALSE R syntax","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/conv_py_bools.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Change cases of True/False syntax from Python to TRUE/FALSE R syntax — conv_py_bools","text":"","code":"conv_py_bools(obj)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/conv_py_bools.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Change cases of True/False syntax from Python to TRUE/FALSE R syntax — conv_py_bools","text":"obj object converted","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/conv_py_bools.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Change cases of True/False syntax from Python to TRUE/FALSE R syntax — conv_py_bools","text":"converted object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/convert_genes.html","id":null,"dir":"Reference","previous_headings":"","what":"Use biomaRt to convert genes — convert_genes","title":"Use biomaRt to convert genes — convert_genes","text":"function reads vector genes converts genes specified symbol type","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/convert_genes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Use biomaRt to convert genes — convert_genes","text":"","code":"convert_genes(   genes,   from = c(\"ENSMUSG\", \"ENSG\", \"MGI\", \"HGNC\"),   to = c(\"MGI\", \"HGNC\"),   host = \"https://www.ensembl.org\" )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/convert_genes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Use biomaRt to convert genes — convert_genes","text":"genes Vector genes convert. Format gene input (ENSMUSG, ENSG, MGI, HGNC) Format gene output (MGI HGNC) host Host connect . Defaults https://www.ensembl.org following useMart default, can changed archived hosts useMart fails connect.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/convert_genes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Use biomaRt to convert genes — convert_genes","text":"data frame input genes column 1 converted genes column 2","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/cor_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a heatmap of correlation between receptors and transcription factors — cor_heatmap","title":"Create a heatmap of correlation between receptors and transcription factors — cor_heatmap","text":"Creates heatmap correlation values receptors transcription factors either boolean threshold continuous values displayed","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/cor_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a heatmap of correlation between receptors and transcription factors — cor_heatmap","text":"","code":"cor_heatmap(   dom,   bool = FALSE,   bool_thresh = 0.15,   title = TRUE,   feats = NULL,   recs = NULL,   mark_connections = FALSE,   ... )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/cor_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a heatmap of correlation between receptors and transcription factors — cor_heatmap","text":"dom Domino object network built (build_domino()) bool Boolean indicating whether heatmap continuous boolean. boolean bool_thresh used determine define activity positive negative. bool_thresh Numeric indicating threshold separating '' '' feature activity making boolean heatmap. title Either string use title boolean describing whether include title. order pass 'main' parameter  ComplexHeatmap::Heatmap()  must set title FALSE. feats Either vector features include heatmap '' features. left NULL features selected signaling network shown. recs Either vector receptors include heatmap '' receptors. left NULL receptors selected signaling network connected features plotted shown. mark_connections Boolean indicating whether add 'x' cells connected receptor TF. Default FALSE. ... parameters pass  ComplexHeatmap::Heatmap() . Note use 'main' parameter  ComplexHeatmap::Heatmap()  must set title = FALSE use 'annCol' 'annColors' ann_cols must FALSE.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/cor_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a heatmap of correlation between receptors and transcription factors — cor_heatmap","text":"heatmap rendered active graphics device","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/cor_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a heatmap of correlation between receptors and transcription factors — cor_heatmap","text":"","code":"# basic usage cor_heatmap(dominoSignal:::pbmc_dom_built_tiny, title = \"PBMC R-TF Correlations\")  # show correlations above a specific value cor_heatmap(dominoSignal:::pbmc_dom_built_tiny, bool = TRUE, bool_thresh = 0.25)  # identify combinations that are connected cor_heatmap(dominoSignal:::pbmc_dom_built_tiny, bool = FALSE, mark_connections = TRUE)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/cor_scatter.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a correlation plot between TF and receptor — cor_scatter","title":"Create a correlation plot between TF and receptor — cor_scatter","text":"Create correlation plot transcription factor activation score receptor expression","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/cor_scatter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a correlation plot between TF and receptor — cor_scatter","text":"","code":"cor_scatter(dom, tf, rec, remove_rec_dropout = TRUE, ...)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/cor_scatter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a correlation plot between TF and receptor — cor_scatter","text":"dom Domino object network built (build_domino()) tf Target TF plottting AUC score rec Target receptor plotting expression remove_rec_dropout Whether remove cells zero expression plot. match setting build_domino(). ... parameters pass ggpubr::ggscatter().","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/cor_scatter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a correlation plot between TF and receptor — cor_scatter","text":"ggplot scatter plot rendered active graphics device","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/cor_scatter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a correlation plot between TF and receptor — cor_scatter","text":"","code":"cor_scatter(dominoSignal:::pbmc_dom_built_tiny, \"FLI1\",\"CXCR3\")"},{"path":"https://FertigLab.github.io/dominoSignal/reference/count_linkage.html","id":null,"dir":"Reference","previous_headings":"","what":"Count occurrences of linkages across multiple domino results from a linkage summary — count_linkage","title":"Count occurrences of linkages across multiple domino results from a linkage summary — count_linkage","text":"Count occurrences linkages across multiple domino results linkage summary","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/count_linkage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count occurrences of linkages across multiple domino results from a linkage summary — count_linkage","text":"","code":"count_linkage(   linkage_summary,   cluster,   group.by = NULL,   linkage = \"rec_lig\",   subject_names = NULL )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/count_linkage.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count occurrences of linkages across multiple domino results from a linkage summary — count_linkage","text":"linkage_summary linkage_summary() object cluster name cell cluster compared across multiple domino results group.name column linkage_summary@subject_meta group subjects counting. NULL, total counts linkages linkages cluster across subjects given. linkage stored linkage domino object. Can compare 'tfs', 'rec', 'incoming_lig', 'tfs_rec', 'rec_lig' subject_names vector subject_names linkage_summary compared. NULL, subject_names linkage summary included counting.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/count_linkage.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count occurrences of linkages across multiple domino results from a linkage summary — count_linkage","text":"data frame columns unique linkage features counts many times linkage occured across compared domino results. group.used, counts linkages also provided columns named unique values group.variable.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/count_linkage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count occurrences of linkages across multiple domino results from a linkage summary — count_linkage","text":"","code":"count_linkage(   linkage_summary = dominoSignal:::linkage_sum_tiny, cluster = \"C1\",    group.by = \"group\", linkage = \"rec\") #>   feature total_count G1 G2 #> 1      R1           3  3  0 #> 2      R2           4  3  1 #> 3      R3           3  1  2 #> 4      R4           2  1  1"},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_domino.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a domino object and prepare it for network construction — create_domino","title":"Create a domino object and prepare it for network construction — create_domino","text":"function reads receptor ligand signaling database, cell level features kind (ie. output pySCENIC), z-scored single cell data, cluster id single cell data, calculates correlation matrix receptors features (transcription factor module scores using pySCENIC), finds features enriched cluster. return domino object prepared build_domino(), calculate signaling network.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_domino.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a domino object and prepare it for network construction — create_domino","text":"","code":"create_domino(   rl_map,   features,   counts = NULL,   z_scores = NULL,   clusters = NULL,   use_clusters = TRUE,   tf_targets = NULL,   verbose = TRUE,   use_complexes = TRUE,   rec_min_thresh = 0.025,   remove_rec_dropout = TRUE,   tf_selection_method = \"clusters\",   tf_variance_quantile = 0.5 )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_domino.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a domino object and prepare it for network construction — create_domino","text":"rl_map Data frame row describes receptor-ligand interaction required columns gene_A & gene_B including gene names receptor ligand type_A & type_B annotating genes B ligand (L) receptor (R) features Either path csv containing cell level features interest (ie. auc matrix pySCENIC) named matrix cells columns features rows. counts Counts matrix data. used threshold receptors dropout. z_scores Matrix containing z-scored expression data cells cells columns features rows. clusters Named factor containing cell cluster names cells. use_clusters Boolean indicating whether use clusters. tf_targets Optional. list names transcription factors stored values character vectors genes transcription factor's regulon. verbose Boolean indicating whether print progress computation. use_complexes Boolean indicating whether wish use receptor/ligand complexes receptor ligand signaling database. FALSE, receptor/ligand pairs either functions protein complex considered constructing signaling network. rec_min_thresh Minimum expression level receptors cell. Default 0.025 2.5 percent cells data set. important calculating correlation connect receptors transcription activation. threshold low correlation calculations proceed cells non-zero expression. remove_rec_dropout Whether remove receptors 0 expression counts calculating correlations. can reduce false positive correlation calculations receptors high dropout rates. tf_selection_method Selection method target transcription factors. 'clusters' differential expression clusters calculated. 'variable' variable transcription factors selected. '' transcription factors feature matrix used. Default 'clusters'. Note wish use clusters intercellular signaling downstream MUST choose clusters. tf_variance_quantile proportion variable features take using variance threshold features. Default 0.5. Higher numbers keep features. Ignored tf_selection_method 'variable'","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_domino.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a domino object and prepare it for network construction — create_domino","text":"domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_domino.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a domino object and prepare it for network construction — create_domino","text":"","code":"pbmc_dom_tiny_all <- create_domino(  rl_map = dominoSignal:::rl_map_tiny, features = dominoSignal:::auc_tiny,   counts = dominoSignal:::RNA_count_tiny, z_scores = dominoSignal:::RNA_zscore_tiny,  clusters = dominoSignal:::clusters_tiny, tf_targets = dominoSignal:::regulon_list_tiny,   use_clusters = FALSE, use_complexes = FALSE,   rec_min_thresh = 0.1, remove_rec_dropout = TRUE,  tf_selection_method = \"all\") #> Reading in and processing signaling database #> Database provided from source: CellPhoneDB #> Getting z_scores, clusters, and counts #> Calculating correlations #> 1 of 6 #> 2 of 6 #> 3 of 6 #> 4 of 6 #> 5 of 6 #> 6 of 6  pbmc_dom_tiny_clustered <- create_domino(  rl_map = dominoSignal:::rl_map_tiny, features = dominoSignal:::auc_tiny,   counts = dominoSignal:::RNA_count_tiny, z_scores = dominoSignal:::RNA_zscore_tiny,  clusters = dominoSignal:::clusters_tiny, tf_targets = dominoSignal:::regulon_list_tiny,  use_clusters = TRUE, use_complexes = TRUE, remove_rec_dropout = FALSE) #> Reading in and processing signaling database #> Database provided from source: CellPhoneDB #> Getting z_scores, clusters, and counts #> Calculating feature enrichment by cluster #> 1 of 3 #> 2 of 3 #> 3 of 3 #> Calculating correlations #> 1 of 6 #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> 2 of 6 #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> 3 of 6 #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> 4 of 6 #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> 5 of 6 #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> 6 of 6 #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties"},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_regulon_list_scenic.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a list of genes in regulons inferred by SCENIC — create_regulon_list_scenic","title":"Create a list of genes in regulons inferred by SCENIC — create_regulon_list_scenic","text":"Generates list transcription factors genes targeted transcription factor part regulon inferred pySCENIC","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_regulon_list_scenic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a list of genes in regulons inferred by SCENIC — create_regulon_list_scenic","text":"","code":"create_regulon_list_scenic(regulons)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_regulon_list_scenic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a list of genes in regulons inferred by SCENIC — create_regulon_list_scenic","text":"regulons Data frame file path table output ctx function pySCENIC","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_regulon_list_scenic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a list of genes in regulons inferred by SCENIC — create_regulon_list_scenic","text":"list names transcription factors stored values character vectors genes inferred regulons","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_regulon_list_scenic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a list of genes in regulons inferred by SCENIC — create_regulon_list_scenic","text":"","code":"regulon_list_tiny <- create_regulon_list_scenic(regulons = dominoSignal:::regulons_tiny)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_rl_map_cellphonedb.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a receptor - ligand map from a CellPhoneDB signaling database — create_rl_map_cellphonedb","title":"Create a receptor - ligand map from a CellPhoneDB signaling database — create_rl_map_cellphonedb","text":"Generates data frame ligand-receptor interactions CellPhoneDB database annotating genes encoding interacting ligands receptors queried transcriptomic data.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_rl_map_cellphonedb.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a receptor - ligand map from a CellPhoneDB signaling database — create_rl_map_cellphonedb","text":"","code":"create_rl_map_cellphonedb(   genes,   proteins,   interactions,   complexes = NULL,   database_name = \"CellPhoneDB\",   gene_conv = NULL,   gene_conv_host = \"https://www.ensembl.org\",   alternate_convert = FALSE,   alternate_convert_table = NULL )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_rl_map_cellphonedb.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a receptor - ligand map from a CellPhoneDB signaling database — create_rl_map_cellphonedb","text":"genes data frame file path table gene names uniprot, hgnc_symbol, ensembl format CellPhoneDB database format proteins data frame file path table protein features CellPhoneDB format interactions data frame file path table protein-protein interactions CellPhoneDB format complexes optional: data frame file path table protein complexes CellPhoneDB format database_name name database used, stored output gene_conv tuple (, ) (source, target) gene conversion orthologs desired; options ENSMUSG, ENSG, MGI, HGNC gene_conv_host host conversion; default ensembl, also use mirrors desired alternate_convert boolean like use non-ensembl method conversion (must supply table; recommended, use ensembl ) alternate_convert_table supplied table non-ensembl method conversion","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_rl_map_cellphonedb.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a receptor - ligand map from a CellPhoneDB signaling database — create_rl_map_cellphonedb","text":"Data frame row describes possible receptor-ligand interaction","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/create_rl_map_cellphonedb.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a receptor - ligand map from a CellPhoneDB signaling database — create_rl_map_cellphonedb","text":"","code":"rl_map_tiny <- create_rl_map_cellphonedb(genes = dominoSignal:::genes_tiny,   proteins = dominoSignal:::proteins_tiny, interactions = dominoSignal:::interactions_tiny,   complexes = dominoSignal:::complexes_tiny)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/do_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalize a matrix to its max value by row or column — do_norm","title":"Normalize a matrix to its max value by row or column — do_norm","text":"Normalizes matrix max value row column","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/do_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalize a matrix to its max value by row or column — do_norm","text":"","code":"do_norm(mat, dir)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/do_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalize a matrix to its max value by row or column — do_norm","text":"mat Matrix normalized dir Direction normalize matrix (either \"row\" row \"col\" column)","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/do_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalize a matrix to its max value by row or column — do_norm","text":"normalized matrix direction specified.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_clusters.html","id":null,"dir":"Reference","previous_headings":"","what":"Access clusters — dom_clusters","title":"Access clusters — dom_clusters","text":"function pull cluster information domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_clusters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access clusters — dom_clusters","text":"","code":"dom_clusters(dom, labels = FALSE)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_clusters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access clusters — dom_clusters","text":"dom domino object created create_domino() labels boolean whether return cluster labels cell clusters used inferring communication","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_clusters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access clusters — dom_clusters","text":"vector containing either names clusters used factors cluster label individual cell","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_clusters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access clusters — dom_clusters","text":"","code":"cluster_names <- dom_clusters(dominoSignal:::pbmc_dom_built_tiny) cell_cluster_label <- dom_clusters(dominoSignal:::pbmc_dom_built_tiny, labels = TRUE)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_correlations.html","id":null,"dir":"Reference","previous_headings":"","what":"Access correlations — dom_correlations","title":"Access correlations — dom_correlations","text":"function pull receptor-transcription factor correlations domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_correlations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access correlations — dom_correlations","text":"","code":"dom_correlations(dom, type = \"rl\")"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_correlations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access correlations — dom_correlations","text":"dom domino object created create_domino() type either \"rl\" \"complex\", select receptor-ligand complex correlation matrix","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_correlations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access correlations — dom_correlations","text":"matrix containing correlation values receptor (row) transcription factor (column)","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_correlations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access correlations — dom_correlations","text":"","code":"cor_matrix <- dom_correlations(dominoSignal:::pbmc_dom_built_tiny, \"rl\")"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_counts.html","id":null,"dir":"Reference","previous_headings":"","what":"Access counts — dom_counts","title":"Access counts — dom_counts","text":"function pull gene expression domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_counts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access counts — dom_counts","text":"","code":"dom_counts(dom)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_counts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access counts — dom_counts","text":"dom domino object created create_domino()","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_counts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access counts — dom_counts","text":"matrix containing gene expression values gene (row) cell (column)","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_counts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access counts — dom_counts","text":"","code":"counts <- dom_counts(dominoSignal:::pbmc_dom_built_tiny)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_database.html","id":null,"dir":"Reference","previous_headings":"","what":"Access database — dom_database","title":"Access database — dom_database","text":"function pull database information domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_database.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access database — dom_database","text":"","code":"dom_database(dom, name_only = TRUE)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_database.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access database — dom_database","text":"dom domino object created name_only boolean whether return name database used entire database stored. Default TRUE.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_database.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access database — dom_database","text":"vector unique databases used building domino object data frame includes database information used domino object creation","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_database.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access database — dom_database","text":"","code":"database_name <- dom_database(dominoSignal:::pbmc_dom_built_tiny) full_database <- dom_database(dominoSignal:::pbmc_dom_built_tiny, name_only = FALSE)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_de.html","id":null,"dir":"Reference","previous_headings":"","what":"Access differential expression — dom_de","title":"Access differential expression — dom_de","text":"function pull differential expression p-values domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_de.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access differential expression — dom_de","text":"","code":"dom_de(dom)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_de.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access differential expression — dom_de","text":"dom domino object created create_domino()","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_de.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access differential expression — dom_de","text":"matrix containing p-values differential expression transcription factors (rows) cluster (columns)","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_de.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access differential expression — dom_de","text":"","code":"de_mat <- dom_de(dominoSignal:::pbmc_dom_built_tiny)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_info.html","id":null,"dir":"Reference","previous_headings":"","what":"Access build information — dom_info","title":"Access build information — dom_info","text":"function pull parameters used running build_domino() domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access build information — dom_info","text":"","code":"dom_info(dom)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access build information — dom_info","text":"dom domino object created create_domino()","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access build information — dom_info","text":"list containing booleans whether object created built list build parameters used build_domino() infer signaling network","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_info.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access build information — dom_info","text":"","code":"build_details <- dom_info(dominoSignal:::pbmc_dom_built_tiny)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_linkages.html","id":null,"dir":"Reference","previous_headings":"","what":"Access linkages — dom_linkages","title":"Access linkages — dom_linkages","text":"function pull linkages domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_linkages.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access linkages — dom_linkages","text":"","code":"dom_linkages(   dom,   link_type = c(\"complexes\", \"receptor-ligand\", \"tf-target\", \"tf-receptor\", \"receptor\",     \"incoming-ligand\"),   by_cluster = FALSE )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_linkages.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access linkages — dom_linkages","text":"dom domino object created create_domino() link_type one value (\"complexes\", \"receptor-ligand\", \"tf-target\", \"tf-receptor\", \"receptor\", \"incoming-ligand\") used select desired type linkage by_cluster boolean indicate whether linkages returned overall cluster","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_linkages.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access linkages — dom_linkages","text":"list containing linkages combination receptors, ligands, transcription factors, clusters","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_linkages.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access linkages — dom_linkages","text":"","code":"complexes <- dom_linkages(dominoSignal:::pbmc_dom_built_tiny, \"complexes\") tf_rec_by_cluster <- dom_linkages(dominoSignal:::pbmc_dom_built_tiny, \"tf-receptor\", TRUE)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_network_items.html","id":null,"dir":"Reference","previous_headings":"","what":"Access all features, receptors, or ligands present in a signaling network. — dom_network_items","title":"Access all features, receptors, or ligands present in a signaling network. — dom_network_items","text":"function collates features, receptors, ligands found signaling network anywhere list clusters. can useful comparing signaling networks across two separate conditions. order run build_domino() must run object previously.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_network_items.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access all features, receptors, or ligands present in a signaling network. — dom_network_items","text":"","code":"dom_network_items(dom, clusters = NULL, return = NULL)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_network_items.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access all features, receptors, or ligands present in a signaling network. — dom_network_items","text":"dom domino object containing signaling network (.e. build_domino() run) clusters vector indicating clusters collate network items . left NULL clusters included. return string indicating whether collate \"features\", \"receptors\", \"ligands\". \"\" list three returned.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_network_items.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access all features, receptors, or ligands present in a signaling network. — dom_network_items","text":"vector containing features, receptors, ligands data set list containing three.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_network_items.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access all features, receptors, or ligands present in a signaling network. — dom_network_items","text":"","code":"monocyte_receptors <- dom_network_items(dominoSignal:::pbmc_dom_built_tiny, \"CD14_monocyte\", \"receptors\") all_tfs <- dom_network_items(dominoSignal:::pbmc_dom_built_tiny, return = \"features\")"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_signaling.html","id":null,"dir":"Reference","previous_headings":"","what":"Access signaling — dom_signaling","title":"Access signaling — dom_signaling","text":"function pull signaling matrices domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_signaling.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access signaling — dom_signaling","text":"","code":"dom_signaling(dom, cluster = NULL)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_signaling.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access signaling — dom_signaling","text":"dom domino object created create_domino() cluster either NULL indicate global signaling specific cluster signaling matrix desired","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_signaling.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access signaling — dom_signaling","text":"data frame containing signaling score ligand (row) cluster (column) data frame containing global summed signaling scores receptors (rows) ligands (columns) cluster","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_signaling.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access signaling — dom_signaling","text":"","code":"monocyte_signaling <- dom_signaling(dominoSignal:::pbmc_dom_built_tiny, cluster = \"CD14_monocyte\")"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_tf_activation.html","id":null,"dir":"Reference","previous_headings":"","what":"Access transcription factor activation — dom_tf_activation","title":"Access transcription factor activation — dom_tf_activation","text":"function pull transcription factor activation scores domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_tf_activation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access transcription factor activation — dom_tf_activation","text":"","code":"dom_tf_activation(dom)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_tf_activation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access transcription factor activation — dom_tf_activation","text":"dom domino object created create_domino()","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_tf_activation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access transcription factor activation — dom_tf_activation","text":"matrix containing transcription factor activation scores TF (row) cell (column)","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_tf_activation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access transcription factor activation — dom_tf_activation","text":"","code":"tf_activation <- dom_tf_activation(dominoSignal:::pbmc_dom_built_tiny)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_zscores.html","id":null,"dir":"Reference","previous_headings":"","what":"Access z-scores — dom_zscores","title":"Access z-scores — dom_zscores","text":"function pull z-scored expression domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_zscores.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access z-scores — dom_zscores","text":"","code":"dom_zscores(dom)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_zscores.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access z-scores — dom_zscores","text":"dom domino object created create_domino()","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_zscores.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access z-scores — dom_zscores","text":"matrix containing z-scored gene expression values gene (row) cell (column)","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/dom_zscores.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access z-scores — dom_zscores","text":"","code":"zscores <- dom_zscores(dominoSignal:::pbmc_dom_built_tiny)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/domino-class.html","id":null,"dir":"Reference","previous_headings":"","what":"The domino class — domino-class","title":"The domino class — domino-class","text":"domino class contains information necessary calculate receptor-ligand signaling. contains z-scored expression, cell cluster labels, feature values, referenced receptor-ligand database formatted receptor-ligand map. Calculated intermediate values also stored.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/domino-class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The domino class — domino-class","text":"instance class domino","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/domino-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"The domino class — domino-class","text":"db_info List data sets ligand - receptor database counts Raw count gene expression data z_scores Matrix z-scored expression data cells columns clusters Named factor cluster identity cell features Matrix features (TFs) correlate receptor - ligand expression . Cells columns features rows. cor Correlation matrix receptor expression features. linkages List lists containing info linking cluster->tf->rec->lig clust_de Data frame containing differential expression results features cluster. misc List miscellaneous info pertaining run parameters etc. cl_signaling_matrices Incoming signaling matrix cluster signaling Signaling matrix clusters.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/feat_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a heatmap of features organized by cluster — feat_heatmap","title":"Create a heatmap of features organized by cluster — feat_heatmap","text":"Creates heatmap transcription factor activation scores cells grouped cluster.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/feat_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a heatmap of features organized by cluster — feat_heatmap","text":"","code":"feat_heatmap(   dom,   feats = NULL,   bool = FALSE,   bool_thresh = 0.2,   title = TRUE,   norm = FALSE,   cols = NULL,   ann_cols = TRUE,   min_thresh = NULL,   max_thresh = NULL,   ... )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/feat_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a heatmap of features organized by cluster — feat_heatmap","text":"dom Domino object network built (build_domino()) feats Either vector features include heatmap '' features. left NULL features selected signaling network shown. bool Boolean indicating whether heatmap continuous boolean. boolean bool_thresh used determine define activity positive negative. bool_thresh Numeric indicating threshold separating '' '' feature activity making boolean heatmap. title Either string use title boolean describing whether include title. order pass 'main' parameter  ComplexHeatmap::Heatmap()  must set title FALSE. norm Boolean indicating whether normalize transcrption factors max value. cols Named vector colors annotate cells cluster color. Values taken colors names cluster. left NULL default ggplot colors generated. ann_cols Boolean indicating whether include cell cluster column annotation. Colors can defined cols. FALSE custom annotations can passed ComplexHeatmap::Heatmap(). min_thresh Minimum threshold color scaling boolean heatmap max_thresh Maximum threshold color scaling boolean heatmap ... parameters pass  ComplexHeatmap::Heatmap() . Note use 'main' parameter  ComplexHeatmap::Heatmap()  must set title = FALSE use 'annCol' 'annColors' ann_cols must FALSE.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/feat_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a heatmap of features organized by cluster — feat_heatmap","text":"heatmap rendered active graphics device","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/feat_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a heatmap of features organized by cluster — feat_heatmap","text":"","code":"# basic usage feat_heatmap(dominoSignal:::pbmc_dom_built_tiny)  # using thresholds feat_heatmap(  dominoSignal:::pbmc_dom_built_tiny, min_thresh = 0.1,    max_thresh = 0.6, norm = TRUE, bool = FALSE) #> Warning: You are using norm with min_thresh and max_thresh. Note that values will be thresholded AFTER normalization."},{"path":"https://FertigLab.github.io/dominoSignal/reference/gene_network.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a gene association network — gene_network","title":"Create a gene association network — gene_network","text":"Create gene association network genes given cluster. selected cluster acts receptor gene association network, ligands, receptors, features associated receptor cluster included plot.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/gene_network.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a gene association network — gene_network","text":"","code":"gene_network(   dom,   clust = NULL,   OutgoingSignalingClust = NULL,   class_cols = c(lig = \"#FF685F\", rec = \"#47a7ff\", feat = \"#39C740\"),   cols = NULL,   lig_scale = 1,   layout = \"grid\",   ... )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/gene_network.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a gene association network — gene_network","text":"dom Domino object network built (build_domino()) clust Receptor cluster create gene association network . vector clusters may provided. OutgoingSignalingClust Vector clusters plot outgoing signaling class_cols Named vector colors used color classes vertices. Values must colors names must classes ('rec', 'lig', 'feat' receptors, ligands, features.). cols Named vector colors individual genes. Genes included vector colored according class_cols. lig_scale FALSE numeric value scale size ligand vertices based z-scored expression data set. layout Type layout use. Options 'grid', 'random', 'sphere', 'circle', 'fr' Fruchterman-Reingold force directed layout, 'kk' Kamada Kawai directed layout. ... parameters pass plot() igraph object. See igraph manual options.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/gene_network.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a gene association network — gene_network","text":"igraph plot rendered active graphics device","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/gene_network.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a gene association network — gene_network","text":"","code":"# basic usage gene_network(  dominoSignal:::pbmc_dom_built_tiny, clust = \"CD8_T_cell\",   OutgoingSignalingClust = \"CD14_monocyte\")"},{"path":"https://FertigLab.github.io/dominoSignal/reference/ggplot_col_gen.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate ggplot colors — ggplot_col_gen","title":"Generate ggplot colors — ggplot_col_gen","text":"Accepts number colors generate generates ggplot color spectrum.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/ggplot_col_gen.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate ggplot colors — ggplot_col_gen","text":"","code":"ggplot_col_gen(n)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/ggplot_col_gen.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate ggplot colors — ggplot_col_gen","text":"n Number colors generate","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/ggplot_col_gen.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate ggplot colors — ggplot_col_gen","text":"vector colors according ggplot color generation.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/incoming_signaling_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a cluster incoming signaling heatmap — incoming_signaling_heatmap","title":"Create a cluster incoming signaling heatmap — incoming_signaling_heatmap","text":"Creates heatmap cluster incoming signaling matrix. cluster list ligands capable activating enriched transcription factors. function creates heatmap cluster average expression ligands. list cluster incoming signaling matrices can found cl_signaling_matrices slot domino option alternative plotting function.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/incoming_signaling_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a cluster incoming signaling heatmap — incoming_signaling_heatmap","text":"","code":"incoming_signaling_heatmap(   dom,   rec_clust,   clusts = NULL,   min_thresh = -Inf,   max_thresh = Inf,   scale = \"none\",   normalize = \"none\",   title = TRUE,   ... )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/incoming_signaling_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a cluster incoming signaling heatmap — incoming_signaling_heatmap","text":"dom Domino object network built (build_domino()) rec_clust cluster select receptor. Must match naming clusters domino object. clusts Vector clusters included. NULL clusters used. min_thresh Minimum signaling threshold plotting. Defaults -Inf threshold. max_thresh Maximum signaling threshold plotting. Defaults Inf threshold. scale scale values (thresholding). Options 'none', 'sqrt' square root, 'log' log10. normalize Options normalize matrix. Accepted inputs 'none' normalization, 'rec_norm' normalize maximum value receptor cluster, 'lig_norm' normalize maximum value within ligand cluster title Either string use title boolean describing whether include title. order pass 'main' parameter  ComplexHeatmap::Heatmap()  must set title FALSE. ... parameters pass  ComplexHeatmap::Heatmap(). Note use 'column_title' parameter  ComplexHeatmap::Heatmap()  must set title = FALSE","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/incoming_signaling_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a cluster incoming signaling heatmap — incoming_signaling_heatmap","text":"Heatmap rendered active graphics device","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/incoming_signaling_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a cluster incoming signaling heatmap — incoming_signaling_heatmap","text":"","code":"#incoming signaling of the CD8  T cells incoming_signaling_heatmap(dominoSignal:::pbmc_dom_built_tiny, \"CD8_T_cell\")"},{"path":"https://FertigLab.github.io/dominoSignal/reference/lc.html","id":null,"dir":"Reference","previous_headings":"","what":"Pulls all items from a list pooled into a single vector — lc","title":"Pulls all items from a list pooled into a single vector — lc","text":"Helper function convert nested series lists single vector.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/lc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pulls all items from a list pooled into a single vector — lc","text":"","code":"lc(list, list_names)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/lc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pulls all items from a list pooled into a single vector — lc","text":"list List pull items list_names Names items list pool","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/lc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pulls all items from a list pooled into a single vector — lc","text":"vector contaning items list list_names","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/linkage_summary-class.html","id":null,"dir":"Reference","previous_headings":"","what":"The domino linkage summary class — linkage_summary-class","title":"The domino linkage summary class — linkage_summary-class","text":"linkage summary class contains linkages established multiple domino objects gene regulatory network inference reference receptor- ligand data bases. data frame summarizing meta features describe domino objects compared linkage summary facilitates comparisons established linkages differential signaling interactions across categorical sample covariates.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/linkage_summary-class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The domino linkage summary class — linkage_summary-class","text":"instance class linkage_summary","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/linkage_summary-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"The domino linkage summary class — linkage_summary-class","text":"subject_names unique names domino result included summary subject_meta data.frame row describing one subject columns describing features subjects draw comparisons signaling networks subject_linkages nested list linkages inferred subject. Lists stored heirarchical structure subject-cluster-linkage linkages include transcription factors (tfs), linkages transcription factors receptors (tfs_rec), active receptors (rec), possible receptor-ligand interactions (rec_lig), incoming ligands (incoming_lig)","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/mean_ligand_expression.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate mean ligand expression as a data frame for plotting in circos plot — mean_ligand_expression","title":"Calculate mean ligand expression as a data frame for plotting in circos plot — mean_ligand_expression","text":"Creates data frame mean ligand expression use plotting circos plot ligand expression saving tables mean expression.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/mean_ligand_expression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate mean ligand expression as a data frame for plotting in circos plot — mean_ligand_expression","text":"","code":"mean_ligand_expression(x, ligands, cell_ident, cell_barcodes, destination)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/mean_ligand_expression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate mean ligand expression as a data frame for plotting in circos plot — mean_ligand_expression","text":"x Gene cell expression matrix ligands Character vector ligand genes quantified cell_ident Vector cell type (identity) names calculate mean ligand gene expression cell_barcodes Vector cell barcodes (colnames x) belonging cell_ident calculate mean expression across destination Name receptor ligand interacts","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/mean_ligand_expression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate mean ligand expression as a data frame for plotting in circos plot — mean_ligand_expression","text":"data frame ligand expression targeting specified receptor","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/mean_ligand_expression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate mean ligand expression as a data frame for plotting in circos plot — mean_ligand_expression","text":"","code":"counts <- dom_counts(dominoSignal:::pbmc_dom_built_tiny) mean_exp <- mean_ligand_expression(counts,  ligands = c(\"PTPRC\", \"FASLG\"), cell_ident = \"CD14_monocyte\",  cell_barcodes = colnames(counts), destination = \"FAS\")"},{"path":"https://FertigLab.github.io/dominoSignal/reference/plot_differential_linkages.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot differential linkages among domino results ranked by a comparative statistic — plot_differential_linkages","title":"Plot differential linkages among domino results ranked by a comparative statistic — plot_differential_linkages","text":"Plot differential linkages among domino results ranked comparative statistic","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/plot_differential_linkages.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot differential linkages among domino results ranked by a comparative statistic — plot_differential_linkages","text":"","code":"plot_differential_linkages(   differential_linkages,   test_statistic,   stat_range = c(0, 1),   stat_ranking = c(\"ascending\", \"descending\"),   group_palette = NULL )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/plot_differential_linkages.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot differential linkages among domino results ranked by a comparative statistic — plot_differential_linkages","text":"differential_linkages data frame output test_differential_linkages() function test_statistic column name differential_linkages test statistic used ranking linkages stored (ex. 'p.value') stat_range two value vector minimum maximum values test_statistic plotting linkage features stat_ranking 'ascending' (lowest value test statisic colored red plotted top) 'descending' (highest value test statistic colored red plotted top). group_palette named vector colors use group compared","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/plot_differential_linkages.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot differential linkages among domino results ranked by a comparative statistic — plot_differential_linkages","text":"heatmap-class object features ranked test_statistic annotated proportion subjects showed active linkage features.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/plot_differential_linkages.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot differential linkages among domino results ranked by a comparative statistic — plot_differential_linkages","text":"","code":"plot_differential_linkages(  differential_linkages = dominoSignal:::tiny_differential_linkage_c1,  test_statistic = \"p.value\",  stat_ranking = \"ascending\" )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/print-domino-method.html","id":null,"dir":"Reference","previous_headings":"","what":"Print domino object — print,domino-method","title":"Print domino object — print,domino-method","text":"Prints summary domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/print-domino-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print domino object — print,domino-method","text":"","code":"# S4 method for domino print(x, ...)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/print-domino-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print domino object — print,domino-method","text":"x domino object ... Additional arguments passed methods","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/print-domino-method.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print domino object — print,domino-method","text":"printed description number cells clusters domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/print-domino-method.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print domino object — print,domino-method","text":"","code":"print(dominoSignal:::pbmc_dom_built_tiny) #> A domino object of 360 cells #>                 Contains signaling between 3 clusters #>                 Built with a maximum of Inf TFs per cluster #>                 and a maximum of Inf receptors per TF"},{"path":"https://FertigLab.github.io/dominoSignal/reference/read_if_char.html","id":null,"dir":"Reference","previous_headings":"","what":"Read in data if an object looks like path to it — read_if_char","title":"Read in data if an object looks like path to it — read_if_char","text":"Read data object looks like path ","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/read_if_char.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read in data if an object looks like path to it — read_if_char","text":"","code":"read_if_char(obj)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/read_if_char.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read in data if an object looks like path to it — read_if_char","text":"obj object read already object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/read_if_char.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read in data if an object looks like path to it — read_if_char","text":"Object data read path","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/rename_clusters.html","id":null,"dir":"Reference","previous_headings":"","what":"Renames clusters in a domino object — rename_clusters","title":"Renames clusters in a domino object — rename_clusters","text":"function renames clusters used build domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/rename_clusters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Renames clusters in a domino object — rename_clusters","text":"","code":"rename_clusters(dom, clust_conv, warning = FALSE)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/rename_clusters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Renames clusters in a domino object — rename_clusters","text":"dom domino object rename clusters clust_conv named vector conversions old new clusters. Values taken new clusters IDs names old cluster IDs. warning logical. TRUE, warn cluster found conversion table. Default FALSE.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/rename_clusters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Renames clusters in a domino object — rename_clusters","text":"domino object clusters renamed applicable slots.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/rename_clusters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Renames clusters in a domino object — rename_clusters","text":"","code":"new_clust <- c(\"CD8_T_cell\" = \"CD8+ T Cells\",  \"CD14_monocyte\" = \"CD14+ Monocytes\", \"B_cell\" = \"B Cells\") pbmc_dom_built_tiny <- rename_clusters(dominoSignal:::pbmc_dom_built_tiny, new_clust)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/show-domino-method.html","id":null,"dir":"Reference","previous_headings":"","what":"Show domino object information — show,domino-method","title":"Show domino object information — show,domino-method","text":"Shows content overview domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/show-domino-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Show domino object information — show,domino-method","text":"","code":"# S4 method for domino show(object)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/show-domino-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Show domino object information — show,domino-method","text":"object domino object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/show-domino-method.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Show domino object information — show,domino-method","text":"printed description cell numbers clusters object","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/show-domino-method.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Show domino object information — show,domino-method","text":"","code":"dominoSignal:::pbmc_dom_built_tiny #> A domino object of 360 cells #> Built with signaling between 3 clusters  show(dominoSignal:::pbmc_dom_built_tiny) #> A domino object of 360 cells #> Built with signaling between 3 clusters"},{"path":"https://FertigLab.github.io/dominoSignal/reference/signaling_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a network heatmap — signaling_heatmap","title":"Create a network heatmap — signaling_heatmap","text":"Creates heatmap signaling network. Alternatively, network matrix can accessed directly signaling slot domino object using dom_signaling() function.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/signaling_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a network heatmap — signaling_heatmap","text":"","code":"signaling_heatmap(   dom,   clusts = NULL,   min_thresh = -Inf,   max_thresh = Inf,   scale = \"none\",   normalize = \"none\",   ... )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/signaling_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a network heatmap — signaling_heatmap","text":"dom domino object network built (build_domino()) clusts vector clusters included. NULL clusters used. min_thresh minimum signaling threshold plotting. Defaults -Inf threshold. max_thresh maximum signaling threshold plotting. Defaults Inf threshold. scale scale values (thresholding). Options 'none', 'sqrt' square root, 'log' log10. normalize options normalize matrix. Normalization done thresholding scaling. Accepted inputs 'none' normalization, 'rec_norm' normalize maximum value receptor cluster, 'lig_norm' normalize maximum value within ligand cluster ... parameters pass  ComplexHeatmap::Heatmap()","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/signaling_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a network heatmap — signaling_heatmap","text":"heatmap rendered active graphics device","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/signaling_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a network heatmap — signaling_heatmap","text":"","code":"# basic usage signaling_heatmap(dominoSignal:::pbmc_dom_built_tiny)  # scale signaling_heatmap(dominoSignal:::pbmc_dom_built_tiny, scale = \"sqrt\")  # normalize signaling_heatmap(dominoSignal:::pbmc_dom_built_tiny, normalize = \"rec_norm\") #> Warning: Some values are NA, replacing with 0s."},{"path":"https://FertigLab.github.io/dominoSignal/reference/signaling_network.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a cluster to cluster signaling network diagram — signaling_network","title":"Create a cluster to cluster signaling network diagram — signaling_network","text":"Creates network diagram signaling clusters. Nodes clusters directed edges indicate signaling one cluster another. Edges colored based color scheme ligand expressing cluster","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/signaling_network.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a cluster to cluster signaling network diagram — signaling_network","text":"","code":"signaling_network(   dom,   cols = NULL,   edge_weight = 0.3,   clusts = NULL,   showOutgoingSignalingClusts = NULL,   showIncomingSignalingClusts = NULL,   min_thresh = -Inf,   max_thresh = Inf,   normalize = \"none\",   scale = \"sq\",   layout = \"circle\",   scale_by = \"rec_sig\",   vert_scale = 3,   plot_title = NULL,   ... )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/signaling_network.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a cluster to cluster signaling network diagram — signaling_network","text":"dom domino object network built (build_domino()) cols named vector indicating colors clusters. Values colors names must match clusters domino object. left NULL ggplot colors generated clusters edge_weight weight determining thickness edges plot. Signaling values multiplied value clusts vector clusters included network plot showOutgoingSignalingClusts vector clusters plot outgoing signaling showIncomingSignalingClusts vector clusters plot incoming signaling min_thresh minimum signaling threshold. Values lower threshold set threshold. Defaults -Inf threshold max_thresh maximum signaling threshold plotting. Values higher threshold set threshold. Defaults Inf threshold normalize options normalize signaling matrix. Accepted inputs 'none' normalization, 'rec_norm' normalize maximum value receptor cluster, 'lig_norm' normalize maximum value within ligand cluster scale scale values (thresholding). Options 'none', 'sqrt' square root, 'log' log10, 'sq' square layout type layout use. Options 'random', 'sphere', 'circle', 'fr' Fruchterman-Reingold force directed layout, 'kk' Kamada Kawai directed layout scale_by size vertices. Options 'lig_sig' summed outgoing signaling, 'rec_sig' summed incoming signaling, 'none'. former two cases values scaled asinh summing incoming outgoing signaling vert_scale integer used scale size vertices without variable scaling size_verts_by. plot_title text plot's title. ... parameters passed plot used igraph object.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/signaling_network.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a cluster to cluster signaling network diagram — signaling_network","text":"igraph plot rendered active graphics device","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/signaling_network.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a cluster to cluster signaling network diagram — signaling_network","text":"","code":"# basic usage signaling_network(dominoSignal:::pbmc_dom_built_tiny)  # scaling, thresholds, layouts, selecting clusters signaling_network(  dominoSignal:::pbmc_dom_built_tiny, showOutgoingSignalingClusts = \"CD14_monocyte\",   scale = \"none\", norm = \"none\", layout = \"fr\", scale_by = \"none\",   vert_scale = 5)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/summarize_linkages.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize linkages from multiple domino objects — summarize_linkages","title":"Summarize linkages from multiple domino objects — summarize_linkages","text":"Creates linkage_summary() object storing linkages learned different domino objects nested lists facilitate comparisons networks learned domino across subject covariates.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/summarize_linkages.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize linkages from multiple domino objects — summarize_linkages","text":"","code":"summarize_linkages(domino_results, subject_meta, subject_names = NULL)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/summarize_linkages.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize linkages from multiple domino objects — summarize_linkages","text":"domino_results list domino result one domino object per subject. Names list must match subject_names subject_meta data frame includes subject features objects grouped. first column must subject names subject_names vector subject names domino_results. NULL, defaults first column subject_meta.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/summarize_linkages.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize linkages from multiple domino objects — summarize_linkages","text":"linkage summary class object consisting nested lists active transcription factors, active receptors, incoming ligands cluster across multiple domino results","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/summarize_linkages.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize linkages from multiple domino objects — summarize_linkages","text":"","code":"dom_ls <- dominoSignal:::dom_ls_tiny meta_df <- data.frame(\"ID\" = c(\"dom1\", \"dom2\"), \"group\" = c(\"A\", \"B\")) summarize_linkages(  domino_results = dom_ls, subject_meta = meta_df,   subject_names = meta_df$ID ) #> An object of class \"linkage_summary\" #> Slot \"subject_names\": #> [1] dom1 dom2 #> Levels: dom1 dom2 #>  #> Slot \"subject_meta\": #>     ID group #> 1 dom1     A #> 2 dom2     B #>  #> Slot \"subject_linkages\": #> $dom1 #> $dom1$B_cell #> $dom1$B_cell$tfs #> character(0) #>  #> $dom1$B_cell$rec #> NULL #>  #> $dom1$B_cell$incoming_lig #> NULL #>  #> $dom1$B_cell$tfs_rec #> character(0) #>  #> $dom1$B_cell$rec_lig #> character(0) #>  #>  #> $dom1$CD14_monocyte #> $dom1$CD14_monocyte$tfs #> [1] \"ZNF324\" \"CREM\"   \"FOSL1\"  #>  #> $dom1$CD14_monocyte$rec #> [1] \"CXCR3\"        \"IL7_receptor\" \"TGFBR3\"       \"NRG1\"         #>  #> $dom1$CD14_monocyte$incoming_lig #> [1] \"CCL20\"                 \"IL7\"                   \"TGFB3\"                 #> [4] \"integrin_a6b4_complex\" #>  #> $dom1$CD14_monocyte$tfs_rec #> [1] \"ZNF324 <- CXCR3\"      \"CREM <- IL7_receptor\" \"CREM <- TGFBR3\"       #> [4] \"FOSL1 <- NRG1\"        #>  #> $dom1$CD14_monocyte$rec_lig #> [1] \"CXCR3 <- CCL20\"                \"IL7_receptor <- IL7\"           #> [3] \"TGFBR3 <- TGFB3\"               \"NRG1 <- integrin_a6b4_complex\" #>  #>  #> $dom1$CD8_T_cell #> $dom1$CD8_T_cell$tfs #> [1] \"FLI1\" #>  #> $dom1$CD8_T_cell$rec #> [1] \"CXCR3\"        \"IL7_receptor\" #>  #> $dom1$CD8_T_cell$incoming_lig #> [1] \"CCL20\" \"IL7\"   #>  #> $dom1$CD8_T_cell$tfs_rec #> [1] \"FLI1 <- CXCR3\"        \"FLI1 <- IL7_receptor\" #>  #> $dom1$CD8_T_cell$rec_lig #> [1] \"CXCR3 <- CCL20\"      \"IL7_receptor <- IL7\" #>  #>  #>  #> $dom2 #> $dom2$B_cell #> $dom2$B_cell$tfs #> character(0) #>  #> $dom2$B_cell$rec #> NULL #>  #> $dom2$B_cell$incoming_lig #> NULL #>  #> $dom2$B_cell$tfs_rec #> character(0) #>  #> $dom2$B_cell$rec_lig #> character(0) #>  #>  #> $dom2$CD14_monocyte #> $dom2$CD14_monocyte$tfs #> [1] \"FLI1\" #>  #> $dom2$CD14_monocyte$rec #> [1] \"CXCR3\"        \"IL7_receptor\" #>  #> $dom2$CD14_monocyte$incoming_lig #> [1] \"CCL20\" \"IL7\"   #>  #> $dom2$CD14_monocyte$tfs_rec #> [1] \"FLI1 <- CXCR3\"        \"FLI1 <- IL7_receptor\" #>  #> $dom2$CD14_monocyte$rec_lig #> [1] \"CXCR3 <- CCL20\"      \"IL7_receptor <- IL7\" #>  #>  #> $dom2$CD8_T_cell #> $dom2$CD8_T_cell$tfs #> [1] \"ZNF324\" \"CREM\"   \"FOSL1\"  #>  #> $dom2$CD8_T_cell$rec #> [1] \"CXCR3\"        \"IL7_receptor\" \"TGFBR3\"       \"NRG1\"         #>  #> $dom2$CD8_T_cell$incoming_lig #> [1] \"CCL20\"                 \"IL7\"                   \"TGFB3\"                 #> [4] \"integrin_a6b4_complex\" #>  #> $dom2$CD8_T_cell$tfs_rec #> [1] \"ZNF324 <- CXCR3\"      \"CREM <- IL7_receptor\" \"CREM <- TGFBR3\"       #> [4] \"FOSL1 <- NRG1\"        #>  #> $dom2$CD8_T_cell$rec_lig #> [1] \"CXCR3 <- CCL20\"                \"IL7_receptor <- IL7\"           #> [3] \"TGFBR3 <- TGFB3\"               \"NRG1 <- integrin_a6b4_complex\" #>  #>  #>  #>"},{"path":"https://FertigLab.github.io/dominoSignal/reference/table_convert_genes.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert genes using a table — table_convert_genes","title":"Convert genes using a table — table_convert_genes","text":"Takes vector gene inputs conversion table returns converted gene table","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/table_convert_genes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert genes using a table — table_convert_genes","text":"","code":"table_convert_genes(genes, from, to, conversion_table)"},{"path":"https://FertigLab.github.io/dominoSignal/reference/table_convert_genes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert genes using a table — table_convert_genes","text":"genes genes convert gene symbol type input (ENSG, ENSMUSG, HGNC, MGI) desired gene symbol type output (HGNC, MGI) conversion_table data frame column names corresponding gene symbol types (mm.ens, hs.ens, mgi, hgnc) rows corresponding gene symbols ","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/table_convert_genes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert genes using a table — table_convert_genes","text":"data frame genes original corresponding converted symbols","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/test_differential_linkages.html","id":null,"dir":"Reference","previous_headings":"","what":"Statistical test for differential linkages across multiple domino results — test_differential_linkages","title":"Statistical test for differential linkages across multiple domino results — test_differential_linkages","text":"Statistical test differential linkages across multiple domino results","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/test_differential_linkages.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Statistical test for differential linkages across multiple domino results — test_differential_linkages","text":"","code":"test_differential_linkages(   linkage_summary,   cluster,   group.by,   linkage = \"rec_lig\",   subject_names = NULL,   test_name = \"fishers.exact\" )"},{"path":"https://FertigLab.github.io/dominoSignal/reference/test_differential_linkages.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Statistical test for differential linkages across multiple domino results — test_differential_linkages","text":"linkage_summary linkage_summary() object cluster name cell cluster compared across multiple domino results group.name column linkage_summary@subject_meta group subjects counting. linkage stored linkage domino object. Can compare 'tfs', 'rec', 'incoming_lig', 'tfs_rec', 'rec_lig' subject_names vector subject_names linkage_summary compared. NULL, subject_names linkage summary included counting. test_name statistical test used comparison. 'fishers.exact' : Fisher's exact test dependence proportion subjects active linkage cluster group subject belongs group.variable. Provides odds ratio, p-value, Benjamini-Hochberg FDR-adjusted p-value (p.adj) linkage tested.","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/test_differential_linkages.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Statistical test for differential linkages across multiple domino results — test_differential_linkages","text":"data frame results test differential linkages. Rows correspond linkage tested. Columns correspond : 'cluster' : name cell cluster compared 'linkage' : type linkage compared 'group.' : grouping variable 'test_name' : test used comparison 'feature' : individual linkages compared 'test statistics' : test statistics provided based test method. 'fishers.exact' provides odds ratio, p-value, fdr-adjusted p-value. 'total_count' : total number subjects linkage active 'X_count' : number subjects category group.(X) linkage active 'total_n' : number total subjects compared 'X_n' : total number subjects category group.(X)","code":""},{"path":"https://FertigLab.github.io/dominoSignal/reference/test_differential_linkages.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Statistical test for differential linkages across multiple domino results — test_differential_linkages","text":"","code":"test_differential_linkages(   linkage_summary = dominoSignal:::linkage_sum_tiny, cluster = \"C1\", group.by = \"group\",    linkage = \"rec\", test_name = \"fishers.exact\" ) #>    cluster linkage group.by     test_name feature odds.ratio p.value p.adj #> R1      C1     rec    group fishers.exact      R1        Inf     0.1   0.4 #> R2      C1     rec    group fishers.exact      R2        Inf     0.4   0.8 #> R3      C1     rec    group fishers.exact      R3  0.3219834     1.0   1.0 #> R4      C1     rec    group fishers.exact      R4  1.0000000     1.0   1.0 #>    total_count G1_count G2_count total_n G1_n G2_n #> R1           3        3        0       6    3    3 #> R2           4        3        1       6    3    3 #> R3           3        1        2       6    3    3 #> R4           2        1        1       6    3    3"},{"path":[]},{"path":"https://FertigLab.github.io/dominoSignal/news/index.html","id":"package-name-0-99-2","dir":"Changelog","previous_headings":"","what":"Package Name","title":"dominoSignal v0.99.2","text":"Update package name “domino2” “dominoSignal”","code":""},{"path":"https://FertigLab.github.io/dominoSignal/news/index.html","id":"bioconductor-standards-0-99-2","dir":"Changelog","previous_headings":"","what":"Bioconductor Standards","title":"dominoSignal v0.99.2","text":"Update vignettes presenting application DominoSignal pipeline data formatted SingleCellExperiment object Implemented caching example data BiocCache meet package size limits Removal deprecated scripts running SCENIC. Tutorials running SCENIC still present vignettes Corrected BiocCheck notes pertaining coding practices including paste conditional statements, functions dontrun examples, usage seq_len seq_along place seq, usage vapply place sapply","code":""},{"path":"https://FertigLab.github.io/dominoSignal/news/index.html","id":"dominosignal-v0991","dir":"Changelog","previous_headings":"","what":"dominoSignal v0.99.1","title":"dominoSignal v0.99.1","text":"Update Bioconductor version numbering conventions package submission","code":""},{"path":[]},{"path":"https://FertigLab.github.io/dominoSignal/news/index.html","id":"linkage-functions-0-2-2","dir":"Changelog","previous_headings":"","what":"Linkage functions","title":"dominoSignal v0.2.2","text":"Addition new class summarize linkages objects Addition helper functions count linkages compare objects Plotting function differential linkages","code":""},{"path":"https://FertigLab.github.io/dominoSignal/news/index.html","id":"package-structure-0-2-2","dir":"Changelog","previous_headings":"","what":"Package structure","title":"dominoSignal v0.2.2","text":"Adjustments made meet Bioconductor standards","code":""},{"path":[]},{"path":"https://FertigLab.github.io/dominoSignal/news/index.html","id":"updates-to-domino-object-construction-0-2-1","dir":"Changelog","previous_headings":"","what":"Updates to domino object construction","title":"dominoSignal v0.2.1","text":"Uniform formats inputs receptor - ligand interaction databases, transcription factor activity features, regulon gene lists operability alternative databases transcription factor activation inference methods Helper functions reformatting pySCENIC outputs CellPhoneDB database files domino-readable uniform formats Assessment transcription factor linkage receptors function heteromeric complex based correlation transcription factor activity receptor component genes Assessment complex ligand expression mean component gene expression plotting functions Minimum threshold percentage cells cluster expressing receptor gene receptor called active within cluster Additional linkage slots active receptors cluster, transcription factor - receptor linkages cluster, incoming ligands active receptors cluster","code":""},{"path":"https://FertigLab.github.io/dominoSignal/news/index.html","id":"plotting-functions-0-2-1","dir":"Changelog","previous_headings":"","what":"Plotting functions","title":"dominoSignal v0.2.1","text":"Chord plot ligand expression targeting specified receptor chord widths correspond quantity ligand expression cell cluster Signaling networks showing outgoing signaling specified cell clusters Gene networks two cell clusters","code":""},{"path":"https://FertigLab.github.io/dominoSignal/news/index.html","id":"bugfixes-0-2-1","dir":"Changelog","previous_headings":"","what":"Bugfixes","title":"dominoSignal v0.2.1","text":"Added host option gene ortholog conversions using {biomaRt} access maintained mirrors Transcription factor - target linkages now properly stored receptors transcription factor’s regulon excluded linkage Ligand nodes sizes gene networks correspond quantity ligand expression create_domino() can run without providing regulon list References host GitHub repository updated Elisseeff-Lab","code":""}]
diff --git a/inst/CITATION b/inst/CITATION
index 419a3088..a537f67a 100644
--- a/inst/CITATION
+++ b/inst/CITATION
@@ -30,5 +30,5 @@ bibentry(
     person("Jennifer", "Elisseeff", role = "ctb", email = "jhe@jhu.edu", comment = c(ORCID = "0000-0002-5066-1996"))
     ),
   year     = 2024,
-  note     = "R package version 0.99.1"
+  note     = "R package version 0.99.2"
 )
diff --git a/inst/pkgdown.yml b/inst/pkgdown.yml
index 83f51e23..14165eea 100644
--- a/inst/pkgdown.yml
+++ b/inst/pkgdown.yml
@@ -7,7 +7,7 @@ articles:
   dominoSignal: dominoSignal.html
   domino_object_vignette: domino_object_vignette.html
   plotting_vignette: plotting_vignette.html
-last_built: 2024-06-25T17:33Z
+last_built: 2024-06-25T18:20Z
 urls:
   reference: https://FertigLab.github.io/dominoSignal/reference
   article: https://FertigLab.github.io/dominoSignal/articles
diff --git a/vignettes/domino_object_vignette.Rmd b/vignettes/domino_object_vignette.Rmd
index d20b0d4d..c94a904a 100644
--- a/vignettes/domino_object_vignette.Rmd
+++ b/vignettes/domino_object_vignette.Rmd
@@ -40,32 +40,58 @@ dom <- readRDS(tmp_path)
 
 ## Object contents
 
-There is a great deal of information stored with the [domino object class]([domino-class()]):
+There is a great deal of information stored with the [domino object class](https://fertiglab.github.io/dominoSignal/reference/domino-class.html). The domino object is an S4 class object that contains a variety of information about the data set used to build the object, the calculated values, and the linkages between receptors, ligands, and transcription factors. The object is structured as follows (with some examples of the information stored within each slot:
+
 - Input Data
+
   - Information about the database used to construct the rl\_map
+
   - Inputted counts matrix
+  
   - Inputted z-scored counts matrix
+  
   - Inputted cluster labels
+  
   - Inputted transcription factor activation scores
+
 - Calculated values
+
   - Differential expression p-values of transcription factors in each cluster
+
   - Correlation values between ligands and receptors
+
   - Median correlation between components of receptor complexes
+
 - Linkages
+
   - Complexes show the component genes of any complexes in the rl map
+
   - Receptor - ligand linkages as determined from the rl map
+
   - Transcription factor - target linkages as determined from the SCENIC analysis (or other regulon inference method)
+
   - Transcription factors that are differentially expressed in each cluster
+
   - Transcription factors that are correlated with receptors
+
   - Transcription factors that are correlated with receptors in each cluster
+
   - Receptors which are active in each cluster
+
   - Ligands that may activate a receptor in a given cluster (so-called incoming ligands; these may include ligands from outside the data set)
+
 - Signaling matrices
+
   - For each cluster, incoming ligands and the clusters within the data set that they are coming from
+
   - A summary of signaling between all clusters
+
 - Miscellaneous Information
+
   - Build information, which includes the parameters used to build the object in the `build_domino()` functions
+
   - The pared down receptor ligand map information used in building the object
+
   - The percent expression of receptors within each cluster
 
 For commonly accessed information (the number of cells, clusters, and some build information), the show and print methods for domino objects can be used.