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"${SRC_FOLDER_PATHS[@]}" -type f) - -# Add indicated individual files -for F in "${SRC_FILE_PATHS[@]}" -do -FILES+=" ${F}" -done - -echo "${FILES[@]}" - -# Pass these to github: -git config --global user.name 'GitHub Action' -git config --global user.email 'action@github.com' -# Fetch branches -git fetch -# Checkout target branch -git checkout $TARGET_BRANCH -# copy files from the branch the action is being run upon -SRC_BRANCH=$(git symbolic-ref --short HEAD) -git checkout $SRC_BRANCH -- $FILES -# Commit to the repository (ignore if no changes) -git add -A -git diff-index --quiet HEAD || git commit -am "update files" -# Push to remote branch -git push origin $TARGET_BRANCH \ No newline at end of file diff --git a/.github/workflows/bioc_branch.yml b/.github/workflows/bioc_branch.yml deleted file mode 100644 index c42a4333..00000000 --- a/.github/workflows/bioc_branch.yml +++ /dev/null @@ -1,22 +0,0 @@ -name: Update Bioconductor package files - -on: - push: - branches: [main, master] - pull_request: - branches: [main, master] - release: - types: [published] - workflow_dispatch: - -jobs: - copy: - name: Copy files - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v2 - - name: copy - env: - TARGET_BRANCH: 'bioconductor' - run: .github/copy_to_branch.sh - shell: bash diff --git a/.github/workflows/document.yaml b/.github/workflows/document.yaml deleted file mode 100644 index 59afa1a7..00000000 --- a/.github/workflows/document.yaml +++ /dev/null @@ -1,43 +0,0 @@ -# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples -# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help -on: - push: - paths: ["R/**"] - workflow_dispatch: - -name: documentation-update - -jobs: - document: - runs-on: ubuntu-latest - env: - GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} - steps: - - name: Checkout repo - uses: actions/checkout@v3 - with: - fetch-depth: 0 - - - name: Setup R - uses: r-lib/actions/setup-r@v2 - with: - use-public-rspm: true - - - name: Install dependencies - uses: r-lib/actions/setup-r-dependencies@v2 - with: - extra-packages: any::roxygen2 - needs: roxygen2 - - - name: Document - run: roxygen2::roxygenise() - shell: Rscript {0} - - - name: Commit and push changes - run: | - git config --local user.name "$GITHUB_ACTOR" - git config --local user.email "$GITHUB_ACTOR@users.noreply.github.com" - git add man/\* NAMESPACE DESCRIPTION - git commit -m "Update documentation" || echo "No changes to commit" - git pull --ff-only - git push origin diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml deleted file mode 100644 index 03e2c6ba..00000000 --- a/.github/workflows/pkgdown.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples -# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help -on: - push: - branches: [main, master] - pull_request: - branches: [main, master] - release: - types: [published] - workflow_dispatch: - -name: pkgdown-update - -jobs: - pkgdown: - runs-on: ubuntu-latest - # Only restrict concurrency for non-PR jobs - concurrency: - group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }} - env: - GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} - permissions: - contents: write - steps: - - uses: actions/checkout@v3 - - - uses: r-lib/actions/setup-pandoc@v2 - - - uses: r-lib/actions/setup-r@v2 - with: - use-public-rspm: true - - - uses: r-lib/actions/setup-r-dependencies@v2 - with: - extra-packages: any::pkgdown - needs: website - - - name: Build site - run: pkgdown::build_site_github_pages(new_process = FALSE, install = TRUE) - shell: Rscript {0} - - - name: Deploy to GitHub pages 🚀 - if: github.event_name != 'pull_request' - uses: JamesIves/github-pages-deploy-action@v4.4.1 - with: - clean: false - branch: gh-pages - folder: docs diff --git a/.github/workflows/r-build-check.yml b/.github/workflows/r-build-check.yml deleted file mode 100644 index 6ddc15e0..00000000 --- a/.github/workflows/r-build-check.yml +++ /dev/null @@ -1,12 +0,0 @@ -name: r-build-check -on: - push: - branches: ['v0.2.2_development'] - pull_request: - branches: ['master', 'v0.2.2'] - workflow_dispatch: - -jobs: - build-check: - uses: FertigLab/actions/.github/workflows/r-build-check.yml@v1 - secrets: inherit diff --git a/_pkgdown.yml b/_pkgdown.yml deleted file mode 100644 index dca3701b..00000000 --- a/_pkgdown.yml +++ /dev/null @@ -1,126 +0,0 @@ -url: https://FertigLab.github.io/domino2 - -deploy: - install_metadata: true - -template: - bootstrap: 5 - # bootswatch: pulse - theme: a11y-light - bslib: - primary: "#457E78" - secondary: "#1D132B" - navbar-bg: "#7DDED2" - navbar-light-color: "#1D132B" - nav-link-color: "#1D132B" - nav-tabs-link-active-bg: "#DFD4EC" - headings-color: "#457E78" - component-active-bg: "#ece5f4" - code-font-size: 95% - code-bg: "#fefefe" - link-color: "#8963BA" - border-color: "#DFD4EC" - dropdown-bg: "#F4F1F9" - - -reference: -- title: "Domino Classes" - desc: > - An object class designed to store information used - and produced in Domino2 analysis - contents: - - domino-class - - linkage_summary -- title: "Analysis Functions" - desc: > - Functions for performing cell-cell communication analysis - with the Domino2 package - contents: - - create_rl_map_cellphonedb - - create_regulon_list_scenic - - create_domino - - build_domino - - summarize_linkages - - test_differential_linkages -- title: "Plotting Functions" - desc: "Functions for visualizing Domino2 analysis results" -- subtitle: "Heatmaps" - contents: - - cor_heatmap - - feat_heatmap - - incoming_signaling_heatmap - - signaling_heatmap -- subtitle: "Network Plots" - contents: - - gene_network - - signaling_network -- subtitle: "Other Plots" - contents: - - circos_ligand_receptor - - cor_scatter - - plot_differential_linkages -- title: "Helper Functions" - desc: "Convenience functions for working with Domino2 objects" -- subtitle: "Access functions" - contents: - - dom_clusters - - dom_correlations - - dom_counts - - dom_database - - dom_de - - dom_info - - dom_linkages - - dom_network_items - - dom_signaling - - dom_tf_activation - - dom_zscores -- subtitle: "Misc. utility functions" - contents: - - add_rl_column - - count_linkage - - mean_ligand_expression - - rename_clusters - -navbar: - type: light - bg: "#7DDED2" - structure: - left: [intro, articles, reference, news] - right: [search, github] - -home: - title: Cell Communication from Single Cell RNA Sequencing Data - description: > - Domino2 uses transcription factor activation and ligand - receptor linkages to infer intra- and inter- cellular signaling from single cell RNA sequencing data. - sidebar: - structure: [toc, authors, labs, citation, license] - components: - labs: - title: Contributing Labs - text: | - For more information about these labs, click below.\n - - Elisseeff Lab \n - - Fertig Lab - -footer: - structure: - left: [package, developed_by] - right: built_with - -news: - one_page: true - cran_dates: false - releases: - - text: "domino2 v0.2.1" - href: https://github.com/FertigLab/domino2/releases/tag/v0.2.1-alpha - - text: "domino2 v0.2.2" - href: https://github.com/FertigLab/domino2/releases/tag/v0.2.2-alpha - -authors: - footer: - roles: aut - text: "is developed by" - sidebar: - roles: [aut, ctb] diff --git a/docs/404.html b/docs/404.html deleted file mode 100644 index 2c4fd69d..00000000 --- a/docs/404.html +++ /dev/null @@ -1,115 +0,0 @@ - - - - - - - -Page not found (404) • domino2 - - - - - - - - - - - - - - - - - Skip to contents - - -
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domino2 is a tool for analysis of intra- and intercellular signaling in single cell RNA sequencing (scRNAseq) data based on transcription factor (TF) activation. Here, we show a basic example pipeline for generating communication networks as a domino object. This vignette will demonstrate how to use domino2 on the 10X Genomics Peripheral Blood Mononuclear Cells (PBMC) data set of 2,700 cells. The data can be downloaded here.

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Data preparation -

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Analysis of cell-cell communication with domino2 often follows initial processing and annotation of scRNAseq data. We used the Satija Lab’s Guided Clustering Tutorial to prepare the data set for analysis with domino2. The complete processing script is available in the data-raw directory of the domino2 package. The processed data can be downloaded from Zenodo.

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-pbmc <- readRDS(paste0(pbmc_dir, "/pbmc_seurat.rds"))
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Preparing Preprocessed Data: -

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domino2 was designed to be compatible with Seurat objects in addition to accepting matrices and vectors of the data set as function inputs. However, pySCENIC is used for TF activation inference, and it requires as input a cell by gene matrix. This is the opposite orientation of Seurat objects but is the default for Python based tools such as scanpy. The RNA counts matrix can be saved as a tab-seperated value (.tsv) or comma-sperated value (.csv) file after transposing the matrix to cell by gene orientation.

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-pbmc_counts <- GetAssayData(object = pbmc, slot = "counts")
-write.table(t(as.matrix(pbmc_counts)), paste0(input_dir, "/pbmc3k_counts.tsv"), sep = "\t",
-    col.names = NA)
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tsv and csv files are very inefficient for storing large matrices. As an alternative, the counts matrix can be saved as a loom file and directly passed to pySCENIC in this format. Generating a loom file in R requires use of the loomR package. The package automatically handles the transposition of the counts matrix to cell by gene orientation as well. We recommend this approach to passing a counts matrix to pySCENIC. However, users should be aware that loomR is not hosted on CRAN or BioConductor at the time of this vignette’s creation.

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-library(loomR)
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-# save loom counts matrix
-pbmc_counts <- GetAssayData(object = pbmc, slot = "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()
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Installation -

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Installation of domino2 from Github can be achieved using the remotes package. The current stable version is v0.2.2, so we will install that version.

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-# if(!require(remotes)){ install.packages('remotes') }
-# remotes::install_github('FertigLab/domino2@v0.2.2')
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Additional Downloads -

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For this tutorial, pySCENIC is used as the method for TF activity inference, and the assessment of ligand-receptor interactions is based on curated interactions from CellPhoneDB v4.0.0. Each of these requires downloading some files prior to use in our analysis pipeline.

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Downloads for SCENIC: -

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A singularity image of SCENIC v0.12.1 can be installed from the DockerHub image as a source (example scripts for use with Docker are provided in the scenic_bash directory as well). SCENIC requires a list of TFs, motif annotations, and cisTarget motifs which are all available from the authors of SCENIC for human (HGNC), mouse (MGI), and fly. The following will download everything necessary for an analysis of a data set with HGNC gene labels for the hg38 genome.

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SCENIC_DIR="'temp_dir'/scenic"
-mkdir ${SCENIC_DIR}
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-# Build singularity image
-singularity build "${SCENIC_DIR}/aertslab-pyscenic-0.12.1.sif" docker://aertslab/pyscenic:0.12.1
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-# 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.
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-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 "${SCENIC_DIR}/hg38_10kbp_up_10kbp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather"
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-curl "https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg38/refseq_r80/mc_v10_clust/gene_based/hg38_500bp_up_100bp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather" \
-  -o "${SCENIC_DIR}/hg38_500bp_up_100bp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather"
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-# List of genes encoding TFs in the HG38 reference genome
-curl "https://resources.aertslab.org/cistarget/tf_lists/allTFs_hg38.txt" \
-  -o "${SCENIC_DIR}/allTFs_hg38.txt"
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-# Motif annotations based on the 2017 cisTarget motif collection. Use these files if you are using the mc9nr databases.
-curl "https://resources.aertslab.org/cistarget/motif2tf/motifs-v9-nr.hgnc-m0.001-o0.0.tbl" \
-  -o "${SCENIC_DIR}/motifs-v9-nr.hgnc-m0.001-o0.0.tbl"
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Downloads for CellPhoneDB: -

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Database files from CellPhoneDB v4.0.0 for human scRNAseq data can be installed from a public Github repository from the Tiechmann Group that developed CellPhoneDB.

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-#
-cellphone_url <- "https://github.com/ventolab/cellphonedb-data/archive/refs/tags/v4.0.0.tar.gz"
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-# download compressed database
-cellphone_tar <- paste0(temp_dir, "/cellphoneDB_v4.tar.gz")
-download.file(url = cellphone_url, destfile = cellphone_tar)
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-# 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")
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SCENIC Analysis -

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SCENIC is our lab’s preferred software for TF activity scoring. We recommend using the Python implementation (pySCENIC) as it is faster than the original R implementation. Be aware, the use of SCENIC is often the slowest and most memory intensive step of this analysis pipeline. SCENIC should be run on computing resources with access to multi-core processing and large amounts of memory.

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As described in the section on saving preprocessed data, SCENIC requires RNA counts to be provided in a cell by gene matrix format, so we will be using the pbmc3k_counts.loom file in this stage of analysis.

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pySCENIC is initiated using bash scripting in the terminal. The analysis consists of 3 steps to score genes for TF motif enrichment, construct TF regulons consisting of genes targeted by the TFs, and arrive at AUC scores for enrichment of regulon gene transcription within each cell.

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grn: construct TF-modules -

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Co-expression modules are used to quantify gene-TF adjacencies.

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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
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-# 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
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ctx: construct TF regulons with pruning based on TF motif enrichment -

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The rankings of genes based on enrichment of TF motifs to the transcription start site (TSS) are considered in the construction of regulons, where target genes in the TF-modules are removed if they lack motifs proximal to the TSS where the TF may bind.

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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                                                          
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-# 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
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aucell: calculate TF activity scores -

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Enrichment of a regulon is measured as the Area Under the recovery Curve (AUC) of the genes that define this regulon.

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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"
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-# Arguments:
-    # counts matrix loom file
-    # regulon table output from ctx
-    # cell x TF matrix of TF enrichment AUC values
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Building a Signaling Network with domino2 -

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Loading SCENIC Results -

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The TF activities are a required input for domino2, and the regulons learned by SCENIC are an important but optional input needed to annotate TF-target interactions and to prune TF-receptor linkages where the receptor is a target of the TF. This prevents the distinction of receptor expression driving TF activity or the TF inducing the receptor’s expression.

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-regulons <- read.csv(paste0(scenic_dir, "/regulons_pbmc_3k.csv"))
-auc <- read.table(paste0(scenic_dir, "/auc_pbmc_3k.csv"), header = TRUE, row.names = 1,
-    stringsAsFactors = FALSE, sep = ",")
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The initial regulons data frame read into R from the ctx function has 2 rows of column names that need to be replaced with one succinct description. domino2 has changed the input format for TF regulons to be a list storing vectors of target genes in each regulon, where the names of the list are the TF genes. This facilitates the use of alternative methods for TF activity quantification. We provide a helper function, create_regulon_list_scenic() for easy retrieval of TF regulons from the output of the pySCENIC ctx function.

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-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)
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Users should be aware that the AUC matrix from SCENIC is loaded in a cell x TF orientation and should be transposed to TF x cell orientation. pySCENIC also appends “(+)” to TF names that are converted to “…” upon loading into R. These characters can be included without affecting the results of domino2 analysis, but can be confusing when querying TF features in the data. We recommend comprehensive removal of the “…” characters using the gsub() function.

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-auc_in <- as.data.frame(t(auc))
-# Remove pattern '...' from the end of all rownames:
-rownames(auc_in) <- gsub("\\.\\.\\.$", "", rownames(auc_in))
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Load CellPhoneDB Database -

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domino2 has been updated to read ligand-receptor data bases in a uniform data.frame format referred to as a receptor-ligand map (rl_map) to enable the use of alternative or updated reference databases as opposed to a particular version of CellPhoneDB’s database in older versions of Domino. Each row corresponds to a ligand-receptor interaction. Genes participating in an interaction are referred to as “partner A” and “partner B” without a requirement for fixed ordering of whether A or B is the ligand and vice versa. The minimum required columns of this data frame are:

-
    -
  • int_pair: the names of the interacting ligand and receptor separated by " & "
  • -
  • gene_A: the gene or genes encoding partner A
  • -
  • gene_B: the gene or genes encoding partner B
  • -
  • type_A: (“L”, “R”) - indicates whether partner A is a ligand (“L”) or receptor (“R”)
  • -
  • type_B: (“L”, “R”) - indicates whether partner B is a ligand (“L”) or receptor (“R”)
  • -
-

Additional annotation columns can be provided such as name_A and name_B for ligands or receptors whose name in the interaction database does not match the names of their encoding genes. This formatting also allows for the consideration of ligand and receptor complexes comprised of a heteromeric combination of multiple proteins that must be co-expressed to function. In these cases, the “name_*” column shows the name of the protein complex, and the “gene_*” column shows the names of the genes encoding the components of the complex separated by commas “,”. When plotting results from the build domino object, the names of the interacting ligands and receptors will be used based on combinatorial expression of the complex components.

-

To facilitate the use of this formatting with the CellPhoneDB database, we include a helper function, create_rl_map_cellphonedb(), that automatically parses files from the cellPhoneDB database to arrive at the rl_map format.

-
-complexes <- read.csv(paste0(cellphone_dir, "/cellphonedb-data-4.0.0/data/complex_input.csv"),
-    stringsAsFactors = FALSE)
-genes <- read.csv(paste0(cellphone_dir, "/cellphonedb-data-4.0.0/data/gene_input.csv"),
-    stringsAsFactors = FALSE)
-interactions <- read.csv(paste0(cellphone_dir, "/cellphonedb-data-4.0.0/data/interaction_input.csv"),
-    stringsAsFactors = FALSE)
-proteins <- read.csv(paste0(cellphone_dir, "/cellphonedb-data-4.0.0/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))
- --------------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
int_pairname_Auniprot_Agene_Atype_Aname_Buniprot_Bgene_Btype_Bannotation_strategysourcedatabase_name
4RAreceptor_RXRG & atRetinoicAcid_byALDH1A3RAreceptor_RXRGP48443,P29373RXRG,CRABP2RatRetinoicAcid_byALDH1A3P47895ALDH1A3Lcurateduniprot;reactomeCellPhoneDB_v4.0
5RAreceptor_RXRG & atRetinoicAcid_byALDH1A2RAreceptor_RXRGP48443,P29373RXRG,CRABP2RatRetinoicAcid_byALDH1A2O94788ALDH1A2Lcurateduniprot;reactomeCellPhoneDB_v4.0
6RAreceptor_RXRG & atRetinoicAcid_byALDH1A1RAreceptor_RXRGP48443,P29373RXRG,CRABP2RatRetinoicAcid_byALDH1A1P00352ALDH1A1Lcurateduniprot;reactomeCellPhoneDB_v4.0
7RAreceptor_RXRB & atRetinoicAcid_byALDH1A3RAreceptor_RXRBP28702,P29373RXRB,CRABP2RatRetinoicAcid_byALDH1A3P47895ALDH1A3Lcurateduniprot;reactomeCellPhoneDB_v4.0
8RAreceptor_RXRB & atRetinoicAcid_byALDH1A2RAreceptor_RXRBP28702,P29373RXRB,CRABP2RatRetinoicAcid_byALDH1A2O94788ALDH1A2Lcurateduniprot;reactomeCellPhoneDB_v4.0
9RAreceptor_RXRB & atRetinoicAcid_byALDH1A1RAreceptor_RXRBP28702,P29373RXRB,CRABP2RatRetinoicAcid_byALDH1A1P00352ALDH1A1Lcurateduniprot;reactomeCellPhoneDB_v4.0
-
-

Optional: Adding interactions manually -

-

The change to use rl_map formatting also enables users to manually append interactions of interest that are not included in the interaction database if need be. This can be attained by formatting the desired interactions as a data.frame with the same column headers as the rl_map and using the rbind() function.

-
-# 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))
- --------------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
int_pairname_Auniprot_Agene_Atype_Aname_Buniprot_Bgene_Btype_Bannotation_strategysourcedatabase_name
1a11b1 complex & COLA1_HUMANa11b1 complexP05556,Q9UKX5ITB1,ITA11RCOLA1_HUMANP02452,P08123COL1A1,COL1A2Lmanualmanualmanual
4RAreceptor_RXRG & atRetinoicAcid_byALDH1A3RAreceptor_RXRGP48443,P29373RXRG,CRABP2RatRetinoicAcid_byALDH1A3P47895ALDH1A3Lcurateduniprot;reactomeCellPhoneDB_v4.0
5RAreceptor_RXRG & atRetinoicAcid_byALDH1A2RAreceptor_RXRGP48443,P29373RXRG,CRABP2RatRetinoicAcid_byALDH1A2O94788ALDH1A2Lcurateduniprot;reactomeCellPhoneDB_v4.0
6RAreceptor_RXRG & atRetinoicAcid_byALDH1A1RAreceptor_RXRGP48443,P29373RXRG,CRABP2RatRetinoicAcid_byALDH1A1P00352ALDH1A1Lcurateduniprot;reactomeCellPhoneDB_v4.0
7RAreceptor_RXRB & atRetinoicAcid_byALDH1A3RAreceptor_RXRBP28702,P29373RXRB,CRABP2RatRetinoicAcid_byALDH1A3P47895ALDH1A3Lcurateduniprot;reactomeCellPhoneDB_v4.0
8RAreceptor_RXRB & atRetinoicAcid_byALDH1A2RAreceptor_RXRBP28702,P29373RXRB,CRABP2RatRetinoicAcid_byALDH1A2O94788ALDH1A2Lcurateduniprot;reactomeCellPhoneDB_v4.0
-
-
-
-
-

Creating the Domino Object -

-

domino2 analysis takes place in two steps.

-
    -
  1. -

    create_domino()

    -
      -
    1. initializes the domino result object

    2. -
    3. assesses differential TF activity across cell clusters by wilcoxon rank-sum test

    4. -
    5. establishes TF-receptor linkages based on Spearman correlation of TF activities with receptor expression across the queried data set.

    6. -
    -
  2. -
  3. -

    build_domino()

    -
      -
    1. sets the parameters for which TFs and receptors are called as active within a cell cluster

    2. -
    3. aggregates the scaled expression of ligands capable of interacting with the active receptors for assessment of ligand type and cellular source triggering activation of a receptor.

    4. -
    -
  4. -
-
-

Required inputs from data set -

-

domino2 infers active receipt of signals via receptors based on the correlation of the receptor’s expression with TF activity across the data set and differential activity of the TF within a cell cluster. Correlations are conducted using scaled expression values rather than raw counts or normalized counts. For assessment of receptor activity on a per cell type basis, a named vector of cell cluster assignments, where the names are cell barcodes matching the expression matrix, are provided. Assessing signaling based on other categorical groupings of cells can be achieved by passing these groupings as “clusters” to build_domino() in place of cell types. domino2 accepts either a Seurat object with counts, scaled counts, and clusters included, or it requires a matrix of counts, a matrix of scaled counts, and a named vector of cell cluster labels as factors. Shown below is how to extract these elements from a Seurat object.

-
-counts <- GetAssayData(object = pbmc, slot = "counts")
-z_scores <- as.matrix(GetAssayData(object = pbmc, slot = "scale.data"))
-clusters <- pbmc$cell_type
-

Note: Ligand and receptor expression can only be assessed for genes included in the z_scores matrix. Many scRNAseq analysis pipelines recommend storing only genes with high variance in scaled expression slots for these data objects, thereby missing many genes encoding ligands and receptors. Ensure that all your genes of interest are included in the rows of your z_scores matrix. Scaled expression was calculated for all genes in this PBMC data set after removal of genes expressed in less than 3 cells.

-
-
-

Create Domino object -

-

At this point, the create_domino() function can be used to make the object. Parameters of note include “use_clusters” which is required to assess signaling between cell types rather than linkage of TFs and receptors broadly across the data set. “use_complexes” decides if receptors that function in heteromeric complexes will be considered in testing linkages between TFs and receptors. If TRUE, a receptor complex is only linked to a TF if a majority of the component genes meet the Spearman correlation threshold. “remove_rec_dropout” decides whether receptor values of 0 will be considered in correlation calculations; this measure is intended to reduce the effect of dropout on receptors with low expression.

-

To run create_domino() with matrix and vector inputs:

-
-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 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
-

The above results will be equivalent to using the Seurat object directly. Note that inclusion of both the matrix inputs and the Seurat object will prioritize the Seurat object.

-
-pbmc_dom <- create_domino(rl_map = rl_map, features = auc_in, ser = pbmc, tf_targets = regulon_list,
-    use_clusters = TRUE, use_complexes = TRUE, remove_rec_dropout = FALSE)
-

For information on what is stored in a domino object and how to access it, please see our vignette on the structure of domino objects.

-
-
-

Build Domino -

-

build_domino() finalizes the construction of the domino object by setting parameters for identifying TFs with differential activation between clusters, receptor linkage with TFs based on magnitude of positive correlation, and the minimum percentage of cells within a cluster that have expression of a receptor for the receptor to be called as active.

-

There are also options for thresholds of the number of TFs that may be called active in a cluster and the number of receptors that may be linked to any one TF. For thresholds of n TFs and m receptors, the bottom n TFs by lowest p-values from the wilcoxon rank sum test and the top m receptors by Spearman correlation coefficient are chosen.

-
-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
-

Both of the thresholds for the number of receptors and TFs can be sent to infinity (Inf) to collect all receptors and TFs that meet statistical significance thresholds.

-
-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)
-
-
-
-

Visualization of Domino Results -

-

Multiple functions are available to visualize the intracellular networks between receptors and TFs and the ligand-receptor mediated intercellular networks between cell types.

-
-

Summarize TF Activity and Linkage -

-

Enrichment of TF activities by cell types can be visualized by feat_heatmap() which plots data set-wide TF activity scores as a heatmap.

-
-feat_heatmap(pbmc_dom, norm = TRUE, bool = FALSE, use_raster = FALSE, row_names_gp = grid::gpar(fontsize = 4))
-

-
-
-

Cumulative signaling between cell types -

-

The cumulative degree of signaling between clusters is assessed as the sum of the scaled expression of ligands targeting active receptors on another cluster. This can be visualized in graph format using the signaling_network() function. Nodes represent each cell cluster and edges scale with the magnitude of signaling between the clusters. The color of the edge corresponds to the sender cluster for that signal.

-
-signaling_network(pbmc_dom, edge_weight = 0.5, max_thresh = 3)
-

-

Signaling networks can also be drawn with the edges only rendering the signals directed towards a given cell type or signals from one cell type directed to others. To see those options in use, as well as other plotting functions and their options, please see our plotting vignette.

-
-
-

Specific Signaling Interactions between Clusters -

-

Beyond the aggregated degree of signaling between cell types, the degrees of signaling through specific ligand-receptor interactions can be assessed. gene_network() provides a graph of linkages between active TFs in a cluster, the linked receptors in that cluster, and the possible ligands of these active receptors.

-
-gene_network(pbmc_dom, clust = "dendritic_cell", layout = "grid")
-

-

New to domino2, gene_network() can be used between two clusters to determine if any of the possible ligands for a given receptor are expressed by a putative outgoing signaling cluster.

-
-gene_network(pbmc_dom, clust = "dendritic_cell", OutgoingSignalingClust = "CD14_monocyte",
-    layout = "grid")
-

-

A comprehensive assessment of ligand expression targeting active receptors on a given cluster can be assessed with incoming_signaling_heatmap().

-
-incoming_signaling_heatmap(pbmc_dom, rec_clust = "dendritic_cell", max_thresh = 2.5,
-    use_raster = FALSE)
-

-

Another form of comprehensive ligand expression assessment is available for individual active receptors in the form of circos plots new to domino2. The outer arcs correspond to clusters in the domino object with inner arcs representing each possible ligand of the plotted receptor. Arcs are drawn between ligands on a cell type and the receptor if the ligand is expressed above the specified threshold. Arc widths correspond to the mean express of the ligand by the cluster with the widest arc width scaling to the maximum expression of the ligand within the data.

-
-circos_ligand_receptor(pbmc_dom, receptor = "CD74")
-

-
-
-
-

Continued Development -

-

Since domino2 is a package still being developed, there are new functions and features that will be implemented in future versions. In the meantime, we have put together further information on plotting and the domino object structure if you would like to explore more of the package’s functionality. Additionally, if you find any bugs, have further questions, or want to share an idea, please let us know here.

-

Vignette Build Information Date last built and session information:

-
-Sys.Date()
-#> [1] "2023-12-20"
-sessionInfo()
-#> R version 4.2.1 (2022-06-23)
-#> Platform: x86_64-pc-linux-gnu (64-bit)
-#> Running under: Ubuntu 22.04.3 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      stats     graphics  grDevices utils     datasets  methods  
-#> [8] base     
-#> 
-#> other attached packages:
-#> [1] knitr_1.45            ComplexHeatmap_2.14.0 circlize_0.4.15      
-#> [4] plyr_1.8.9            Seurat_5.0.1          SeuratObject_5.0.1   
-#> [7] sp_2.1-1              domino2_0.2.2        
-#> 
-#> loaded via a namespace (and not attached):
-#>   [1] utf8_1.2.4             spatstat.explore_3.2-5 reticulate_1.34.0     
-#>   [4] tidyselect_1.2.0       RSQLite_2.3.3          AnnotationDbi_1.60.2  
-#>   [7] htmlwidgets_1.6.2      Rtsne_0.16             munsell_0.5.0         
-#>  [10] codetools_0.2-19       ragg_1.2.6             ica_1.0-3             
-#>  [13] future_1.33.0          miniUI_0.1.1.1         spatstat.random_3.2-1 
-#>  [16] colorspace_2.1-0       progressr_0.14.0       Biobase_2.58.0        
-#>  [19] filelock_1.0.2         highr_0.10             stats4_4.2.1          
-#>  [22] ROCR_1.0-11            tensor_1.5             ggsignif_0.6.4        
-#>  [25] listenv_0.9.0          GenomeInfoDbData_1.2.9 polyclip_1.10-6       
-#>  [28] bit64_4.0.5            rprojroot_2.0.4        parallelly_1.36.0     
-#>  [31] vctrs_0.6.4            generics_0.1.3         xfun_0.41             
-#>  [34] BiocFileCache_2.6.1    R6_2.5.1               doParallel_1.0.17     
-#>  [37] GenomeInfoDb_1.34.9    clue_0.3-65            bitops_1.0-7          
-#>  [40] spatstat.utils_3.0-4   cachem_1.0.8           promises_1.2.1        
-#>  [43] scales_1.3.0           gtable_0.3.4           Cairo_1.6-1           
-#>  [46] globals_0.16.2         goftest_1.2-3          spam_2.10-0           
-#>  [49] rlang_1.1.2            systemfonts_1.0.5      GlobalOptions_0.1.2   
-#>  [52] splines_4.2.1          rstatix_0.7.2          lazyeval_0.2.2        
-#>  [55] spatstat.geom_3.2-7    broom_1.0.5            yaml_2.3.7            
-#>  [58] reshape2_1.4.4         abind_1.4-5            backports_1.4.1       
-#>  [61] httpuv_1.6.12          tools_4.2.1            ggplot2_3.4.4         
-#>  [64] ellipsis_0.3.2         jquerylib_0.1.4        RColorBrewer_1.1-3    
-#>  [67] BiocGenerics_0.44.0    ggridges_0.5.4         Rcpp_1.0.11           
-#>  [70] progress_1.2.2         zlibbioc_1.44.0        purrr_1.0.2           
-#>  [73] RCurl_1.98-1.13        prettyunits_1.2.0      deldir_1.0-9          
-#>  [76] ggpubr_0.6.0           pbapply_1.7-2          GetoptLong_1.0.5      
-#>  [79] cowplot_1.1.1          S4Vectors_0.36.2       zoo_1.8-12            
-#>  [82] ggrepel_0.9.4          cluster_2.1.4          fs_1.6.3              
-#>  [85] magrittr_2.0.3         magick_2.8.1           data.table_1.14.8     
-#>  [88] RSpectra_0.16-1        scattermore_1.2        lmtest_0.9-40         
-#>  [91] RANN_2.6.1             fitdistrplus_1.1-11    matrixStats_1.1.0     
-#>  [94] hms_1.1.3              patchwork_1.1.3        mime_0.12             
-#>  [97] evaluate_0.23          xtable_1.8-4           XML_3.99-0.16         
-#> [100] fastDummies_1.7.3      IRanges_2.32.0         gridExtra_2.3         
-#> [103] shape_1.4.6            compiler_4.2.1         biomaRt_2.54.1        
-#> [106] tibble_3.2.1           KernSmooth_2.23-22     crayon_1.5.2          
-#> [109] htmltools_0.5.7        later_1.3.1            tidyr_1.3.0           
-#> [112] DBI_1.1.3              formatR_1.14           dbplyr_2.4.0          
-#> [115] MASS_7.3-60            rappdirs_0.3.3         Matrix_1.6-3          
-#> [118] car_3.1-2              cli_3.6.1              parallel_4.2.1        
-#> [121] dotCall64_1.1-0        igraph_1.5.1           pkgconfig_2.0.3       
-#> [124] pkgdown_2.0.7          plotly_4.10.3          spatstat.sparse_3.0-3 
-#> [127] xml2_1.3.5             foreach_1.5.2          bslib_0.5.1           
-#> [130] XVector_0.38.0         stringr_1.5.1          digest_0.6.33         
-#> [133] sctransform_0.4.1      RcppAnnoy_0.0.21       spatstat.data_3.0-3   
-#> [136] Biostrings_2.66.0      rmarkdown_2.25         leiden_0.4.3.1        
-#> [139] uwot_0.1.16            curl_5.1.0             shiny_1.8.0           
-#> [142] rjson_0.2.21           nlme_3.1-163           lifecycle_1.0.4       
-#> [145] jsonlite_1.8.7         carData_3.0-5          desc_1.4.2            
-#> [148] viridisLite_0.4.2      fansi_1.0.5            pillar_1.9.0          
-#> [151] lattice_0.22-5         KEGGREST_1.38.0        fastmap_1.1.1         
-#> [154] httr_1.4.7             survival_3.5-7         glue_1.6.2            
-#> [157] png_0.1-8              iterators_1.0.14       bit_4.0.5             
-#> [160] stringi_1.8.2          sass_0.4.7             blob_1.2.4            
-#> [163] textshaping_0.3.7      RcppHNSW_0.5.0         memoise_2.0.1         
-#> [166] dplyr_1.1.4            irlba_2.3.5.1          future.apply_1.11.0
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In order to better implement some of the changes that have been made in domino2, the structure of the domino object has been modified. Here, we describe the data that is stored in the domino object, as well as how best to access this data.

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Object contents -

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The contents of the domino object class include a few broad groups: - input data, such as the L-R database used, counts, and z-scores - calculations, such as correlations or differential expression results - linkages, which show connections between TFs, receptors, and ligands across the data set and within clusters - signaling matrices, which show expression of ligands signaling to active receptors in clusters (or a summary of overall signaling) - build information, which includes the parameters used to build the object in the build_domino() functions

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

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-dom
-#> A domino object of 2700 cells
-#> Built with signaling between 9 clusters
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-print(dom)
-#> A domino object of 2700 cells
-#> Built with signaling between 9 clusters
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Access functions -

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In order to facilitate access to the information stored in the domino object, we have provided a collection of functions to retrieve specific items. These functions begin with “dom_” and can be listed using ls().

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-ls("package:domino2", 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"
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Input data -

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When creating a domino object with the create_domino() function, several inputs are required which are then stored in the domino object itself. These include cluster labels, the counts matrix, z-scored counts matrix, transcription factor activation scores, and the R-L database used in create_rl_map_cellphonedb().

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For example, to access the cluster names in the domino object:

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-dom_clusters(dom)
-#> [1] "naive_CD4_T_cell"  "CD14_monocyte"     "memory_CD4_T_cell"
-#> [4] "B_cell"            "CD8_T_cell"        "CD16_monocyte"    
-#> [7] "NK_cell"           "dendritic_cell"    "platelet"
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Setting an argument labels = TRUE will return the vector of cluster labels for each cell rather than the unique cluster names.

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To access the counts:

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-count_matrix <- dom_counts(dom)
-knitr::kable(count_matrix[1:5, 1:5])
- -------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
AAACATACAACCAC-1AAACATTGAGCTAC-1AAACATTGATCAGC-1AAACCGTGCTTCCG-1AAACCGTGTATGCG-1
AL627309.100000
AP006222.200000
RP11-206L10.200000
RP11-206L10.900000
LINC0011500000
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Or z-scored counts:

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-z_matrix <- dom_zscores(dom)
-knitr::kable(z_matrix[1:5, 1:5])
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AAACATACAACCAC-1AAACATTGAGCTAC-1AAACATTGATCAGC-1AAACCGTGCTTCCG-1AAACCGTGTATGCG-1
AL627309.1-0.0574500-0.0574500-0.0574500-0.0574500-0.0574500
AP006222.2-0.0331877-0.0331877-0.0331877-0.0331877-0.0331877
RP11-206L10.2-0.0411864-0.0411864-0.0411864-0.0411864-0.0411864
RP11-206L10.9-0.0332568-0.0332568-0.0332568-0.0332568-0.0332568
LINC00115-0.0812841-0.0812841-0.0812841-0.0812841-0.0812841
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The transcription factor activation scores can be similarly accessed:

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-activation_matrix <- dom_tf_activation(dom)
-knitr::kable(activation_matrix[1:5, 1:5])
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AAACATACAACCAC-1AAACATTGAGCTAC-1AAACATTGATCAGC-1AAACCGTGCTTCCG-1AAACCGTGTATGCG-1
ARID3A0.00000000.00000000.00000000.18253970.0000000
ARNTL0.00000000.03389210.00000000.02762390.0131924
ATF10.20179090.10391500.12619740.13036230.0000000
ATF20.07543730.08224000.14710880.06936350.0710641
ATF30.06785260.07631800.07747970.12797620.0527818
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Information about the database referenced for ligand-receptor pairs and composition of protien complexes can be extracted from the dom_database() function. By default, the function returns the name(s) of the database(s) used:

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-dom_database(dom)
-#> [1] "CellPhoneDB_v4.0"
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If you would like to view the entire ligand-receptor map, set name_only = FALSE:

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-db_matrix <- dom_database(dom, name_only = FALSE)
-knitr::kable(db_matrix[1:5, 1:5])
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int_pairname_Auniprot_Agene_Atype_A
FGF4 & FGFR1FGF4P08620FGF4L
ADORA3 & ENTPD1ADORA3P0DMS8ADORA3R
EPHB3 & EFNB1EPHB3P54753EPHB3R
TNFSF18 & TNFRSF18TNFSF18Q9UNG2TNFSF18L
EFNA1 & EPHA1EFNA1P20827EFNA1L
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Calculations -

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Active transcription factors in each cluster are determined by conducting wilcoxon rank sum tests for each transcription factor where the transcription factor activity scores amongst all cells in a cluster are tested against the activity scores of all cells outside of the cluster. The p-values for the one-sided test for greater activity within the cluster compared to outside can be accessed with the dom_de() function.

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-de_matrix <- dom_de(dom)
-knitr::kable(de_matrix[1:5, 1:5])
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
naive_CD4_T_cellCD14_monocytememory_CD4_T_cellB_cellCD8_T_cell
ARID3A1.00000000.00000000.99999090.99997180.9999031
ARNTL1.00000000.00000000.32879151.00000000.8408834
ATF10.09428210.79195840.00000000.99999990.8473444
ATF20.00000021.00000000.00000001.00000000.6222360
ATF31.00000000.00000001.00000001.00000001.0000000
-

Linkage between receptors and transcription factors is assessed by Spearman correlation between transcription factor activity scores and scaled expression of receptor-encoding genes across all cells in the data set. Spearman coefficients can be accessed with the dom_correlations() function.

-
-cor_matrix <- dom_correlations(dom)
-knitr::kable(cor_matrix[1:5, 1:5])
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
ARID3AARNTLATF1ATF2ATF3
FGFR10.00000000.00000000.00000000.00000000.0000000
ADORA30.00000000.00000000.00000000.00000000.0000000
EPHB30.00000000.00000000.00000000.00000000.0000000
TNFRSF18-0.04150260.02924650.04116160.0595802-0.0560694
EPHA10.00000000.00000000.00000000.00000000.0000000
-
-
-

Linkages -

-

Linkages between ligands, receptors, and transcription factors can be accessed in several different ways, depending on the specific link and the scope desired. The dom_linkages() function has three arguments - the first, like all of our access functions, is for the domino object. The second, link_type, is used to specify which linkages are desired (options are complexes, receptor-ligand, tf-target, or tf-receptor). The third argument, by_cluster, determines whether the linkages returned are arranged by cluster (though this does change the available linkage types to tf-receptor, receptor, or incoming-ligand). For example, to access the complexes used across the dataset:

-
-complex_links <- dom_linkages(dom, link_type = "complexes")
-# Look for components of NODAL receptor complex
-complex_links$NODAL_receptor
-#> [1] "ACVR1B"       "ACVR2B"       "TDGF1"        "character(0)"
-

To view incoming ligands to each cluster:

-
-incoming_links <- dom_linkages(dom, link_type = "incoming-ligand", by_cluster = TRUE)
-# Check incoming signals to dendritic cells
-incoming_links$dendritic_cell
-#>  [1] "MIF"          "APP"          "COPA"         "CD99"         "CD1D"        
-#>  [6] "HLA-F"        "INS"          "ANXA1"        "CXCL9"        "BMP8B"       
-#> [11] "a4b1_complex" "aVb3_complex" "TG"           "CSF3"         "IL34"        
-#> [16] "CSF1"         "CSF2"         "TNF"          "GRN"          "LTA"         
-#> [21] "CTLA4"        "CD28"
-

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")
-# 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"
-

Alternately, to obtain a simplified list of receptors, ligands, and/or features in the domino object, use the dom_network_items() function. To pull all transcription factors associated with the dendritic cell cluster:

-
-dc_tfs <- dom_network_items(dom, "dendritic_cell", return = "features")
-head(dc_tfs)
-#> [1] "ZNF189" "KLF12"  "ETV7"   "IRF8"   "KLF6"   "FOS"
-
-
-

Signaling Matrices -

-

The averaged z-scored expression of ligands and receptors between different clusters can be accessed in matrix form.

-
-signal_matrix <- dom_signaling(dom)
-knitr::kable(signal_matrix)
- ------------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
L_naive_CD4_T_cellL_CD14_monocyteL_memory_CD4_T_cellL_B_cellL_CD8_T_cellL_CD16_monocyteL_NK_cellL_dendritic_cellL_platelet
R_naive_CD4_T_cell0.00000000.00000000.17117380.11420250.13161750.22232120.24295050.0000000.00000
R_CD14_monocyte0.90542932.25959221.84841990.41139001.27934451.66683452.06531754.6292393.38459
R_memory_CD4_T_cell0.00000000.00000000.17117380.11420250.13161750.22232120.24295050.0000000.00000
R_B_cell0.05138750.19206100.28194200.00000000.00000000.25081790.16394651.0529962.47526
R_CD8_T_cell0.00000000.00000000.17117380.11420250.13161750.22232120.24295050.0000000.00000
R_CD16_monocyte0.90542932.25959221.84841990.41139001.27934451.66683452.06531754.6292393.38459
R_NK_cell0.11271731.51826600.65361310.00000000.22723701.01999250.41685402.5821742.47526
R_dendritic_cell0.24125942.25959221.48628730.05836761.27934451.06992152.06531754.6292393.38459
R_platelet0.44669700.73819640.51509580.41139000.32649031.04688530.53328122.3176552.47526
-

To view signaling to a specific cluster from the other clusters, set the cluster argument to the cluster name.

-
-dc_matrix <- dom_signaling(dom, "dendritic_cell")
-knitr::kable(dc_matrix)
- ------------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
dendritic_cell.L_naive_CD4_T_celldendritic_cell.L_CD14_monocytedendritic_cell.L_memory_CD4_T_celldendritic_cell.L_B_celldendritic_cell.L_CD8_T_celldendritic_cell.L_CD16_monocytedendritic_cell.L_NK_celldendritic_cell.L_dendritic_celldendritic_cell.L_platelet
MIF0.05138750.00000000.22833560.00000000.00000000.00000000.16394650.33837300.0000000
APP0.00000000.09107850.04785410.00000000.00000000.00000000.00000000.56518862.0283841
COPA0.00000000.10098250.00575230.00000000.00000000.25081790.00000000.14943490.4468762
CD990.00000000.02499290.19417520.00000000.52221390.00000000.95301710.16567050.9093296
CD1D0.00000000.54613550.00000000.05836760.00000000.17330570.00000001.26465850.0000000
HLA-F0.00000000.00000000.01334170.00000000.32649030.02584880.36933470.00000000.0000000
ANXA10.00000000.16184420.41007480.00000000.26185380.06874050.56000500.61673620.0000000
CXCL90.00000000.00835360.00000000.00000000.00000000.00000000.00000000.00000000.0000000
CSF10.00000000.00000000.04878870.00000000.00000000.00435520.00905720.00000000.0000000
TNF0.00000000.00000000.05431160.00000000.15742950.05899030.00000000.00611020.0000000
GRN0.00000001.32620500.00000000.00000000.00000000.48786320.00000001.52306760.0000000
LTA0.06132980.00000000.17467690.00000000.00000000.00000000.00995700.00000000.0000000
CTLA40.00000000.00000000.06383440.00000000.00000000.00000000.00000000.00000000.0000000
CD280.12854210.00000000.24514190.00000000.01135710.00000000.00000000.00000000.0000000
-
-
-

Build information -

-

To keep track of the options set when running build_domino(), they are stored within the domino object itself. To view these options, use the dom_info() function.

-
-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
-
-
-
-

Continued Development -

-

Since domino2 is a package still being developed, there are new functions and features that will be implemented in future versions. In the meantime, we have put together further information on plotting and an example analysis can be viewed on our Getting Started page. Additionally, if you find any bugs, have further questions, or want to share an idea, please let us know here.

-

Vignette Build Information Date last built and session information:

-
-Sys.Date()
-#> [1] "2023-12-20"
-sessionInfo()
-#> R version 4.2.1 (2022-06-23)
-#> Platform: x86_64-pc-linux-gnu (64-bit)
-#> Running under: Ubuntu 22.04.3 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] domino2_0.2.2
-#> 
-#> loaded via a namespace (and not attached):
-#>   [1] bitops_1.0-7           matrixStats_1.1.0      fs_1.6.3              
-#>   [4] bit64_4.0.5            filelock_1.0.2         doParallel_1.0.17     
-#>   [7] RColorBrewer_1.1-3     progress_1.2.2         httr_1.4.7            
-#>  [10] rprojroot_2.0.4        GenomeInfoDb_1.34.9    backports_1.4.1       
-#>  [13] tools_4.2.1            bslib_0.5.1            utf8_1.2.4            
-#>  [16] R6_2.5.1               DBI_1.1.3              BiocGenerics_0.44.0   
-#>  [19] colorspace_2.1-0       GetoptLong_1.0.5       tidyselect_1.2.0      
-#>  [22] prettyunits_1.2.0      bit_4.0.5              curl_5.1.0            
-#>  [25] compiler_4.2.1         textshaping_0.3.7      cli_3.6.1             
-#>  [28] Biobase_2.58.0         formatR_1.14           xml2_1.3.5            
-#>  [31] desc_1.4.2             sass_0.4.7             scales_1.3.0          
-#>  [34] rappdirs_0.3.3         pkgdown_2.0.7          systemfonts_1.0.5     
-#>  [37] stringr_1.5.1          digest_0.6.33          rmarkdown_2.25        
-#>  [40] XVector_0.38.0         pkgconfig_2.0.3        htmltools_0.5.7       
-#>  [43] dbplyr_2.4.0           fastmap_1.1.1          rlang_1.1.2           
-#>  [46] GlobalOptions_0.1.2    RSQLite_2.3.3          shape_1.4.6           
-#>  [49] jquerylib_0.1.4        generics_0.1.3         jsonlite_1.8.7        
-#>  [52] car_3.1-2              dplyr_1.1.4            RCurl_1.98-1.13       
-#>  [55] magrittr_2.0.3         GenomeInfoDbData_1.2.9 Matrix_1.6-3          
-#>  [58] munsell_0.5.0          Rcpp_1.0.11            S4Vectors_0.36.2      
-#>  [61] fansi_1.0.5            abind_1.4-5            lifecycle_1.0.4       
-#>  [64] stringi_1.8.2          yaml_2.3.7             carData_3.0-5         
-#>  [67] zlibbioc_1.44.0        plyr_1.8.9             BiocFileCache_2.6.1   
-#>  [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_0.4.15        hms_1.1.3              KEGGREST_1.38.0       
-#>  [79] knitr_1.45             ComplexHeatmap_2.14.0  pillar_1.9.0          
-#>  [82] igraph_1.5.1           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          glue_1.6.2            
-#>  [91] evaluate_0.23          png_0.1-8              vctrs_0.6.4           
-#>  [94] foreach_1.5.2          tidyr_1.3.0            gtable_0.3.4          
-#>  [97] purrr_1.0.2            clue_0.3-65            ggplot2_3.4.4         
-#> [100] cachem_1.0.8           xfun_0.41              broom_1.0.5           
-#> [103] rstatix_0.7.2          ragg_1.2.6             tibble_3.2.1          
-#> [106] iterators_1.0.14       AnnotationDbi_1.60.2   memoise_2.0.1         
-#> [109] IRanges_2.32.0         cluster_2.1.4
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    In addition to the numerous plotting options available in the original Domino, domino2 has added more functionality and new methods to improve visualization and interpretation of analysis results. Here, we will go over the different plotting functions available, as well as different options that can be utilized, and options for customization. We will be using the domino object (loaded as dom) from our example analysis in Getting Started.

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    Heatmaps -

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    Correlations between receptors and transcription factors -

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    cor_heatmap() can be used to show the correlations calculated between receptors and transcription factors.

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    -cor_heatmap(dom, title = "PBMC R-TF Correlations", column_names_gp = grid::gpar(fontsize = 8))
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    In addition to displaying the scores for the correlations, this function can also be used to identify correlations above a certain value (using the bool and bool_thresh arguments) or to identify the combinations of receptors and transcription factors (TFs) that are connected (with argument mark_connections).

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    -cor_heatmap(dom, bool = TRUE, bool_thresh = 0.25)
    -cor_heatmap(dom, bool = FALSE, mark_connections = TRUE)
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    If only a subset of receptors or transcription factors are of interest, a vector of either (or both) can be passed to the function.

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    -receptors <- c("CSF1R", "CSF3R", "CCR7", "FCER2")
    -tfs <- c("PAX5", "JUNB", "FOXJ3", "FOSB")
    -cor_heatmap(dom, feats = tfs, recs = receptors)
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    The heatmap functions in domino2 are based on ComplexHeatmap::Heatmap() and will also accept additional arguments meant for that function. For example, while an argument for clustering rows or columns of the heatmap is not explicitly stated, they can still be passed to ComplexHeatmap::Heatmap() through cor_heatmap().

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    -cor_heatmap(dom, cluster_rows = FALSE, cluster_columns = FALSE, column_title = "Heatmap Without Clustering",
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    Heatmap of Transcription Factor Activation Scores -

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    feat_heatmap() is used to show the transcription factor activation for features in the signaling network.

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    -feat_heatmap(dom, use_raster = FALSE, row_names_gp = grid::gpar(fontsize = 4))
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    It functions similarly to cor_heatmap(), with arguments to select a specific vector of features, to use a boolean view with a threshold, and to pass other arguments to ComplexHeatmap::Heatmap(). Specific to this function though are arguments for setting the range of values to be visualized and one to choose whether or not to normalize the scores to the max value.

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    -feat_heatmap(dom, min_thresh = 0.1, max_thresh = 0.6, norm = TRUE, bool = FALSE,
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    -feat_heatmap(dom, bool = TRUE, use_raster = FALSE)
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    Heatmap of Incoming Signaling for a Cluster -

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    incoming_signaling_heatmap() can be used to visualize the cluster average expression of the ligands capable of activating the TFs enriched in the cluster. For example, to view the incoming signaling of the CD8 T cells:

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    -incoming_signaling_heatmap(dom, "CD8_T_cell")
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    We can also select for specific clusters of interest that are signaling to the CD8 T cells. If we are only interested in viewing the monocyte signaling:

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    -incoming_signaling_heatmap(dom, "CD8_T_cell", clusts = c("CD14_monocyte", "CD16_monocyte"))
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    As with our other heatmap functions, options are available for a minimum threshold, maximum threshold, whether to scale the values after thresholding, whether to normalize the matrix, and the ability to pass further arguments to ComplexHeatmap::Heatmap().

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    Heatmap of Signaling Between Clusters -

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    signaling_heatmap() makes a heatmap showing the signaling strength of ligands from each cluster to receptors of each cluster based on averaged expression.

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    As with other functions, specific clusters can be selected, thresholds can be set, and normalization methods can be selected as well.

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    -signaling_heatmap(dom, scale = "sqrt")
    -signaling_heatmap(dom, normalize = "rec_norm")
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    Network Plots -

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    Network showing L-R-TF signaling between clusters -

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    gene_network() makes a network plot to display signaling in selected clusters such that ligands, receptors and features associated with those clusters are displayed as nodes with edges as linkages. To look at signaling to the CD16 Monocytes from the CD14 Monocytes:

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    -gene_network(dom, clust = "CD16_monocyte", OutgoingSignalingClust = "CD14_monocyte")
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    Options to modify this plot include adjusting scaling for the ligands and different layouts (some of which are more legible than others).

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    -gene_network(dom, clust = "CD16_monocyte", OutgoingSignalingClust = "CD14_monocyte",
    -    lig_scale = 25, layout = "sphere")
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    Additionally, colors can be given for select genes (for example, to highlight a specific signaling path).

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    -gene_network(dom, clust = "CD16_monocyte", OutgoingSignalingClust = "CD14_monocyte",
    -    cols = c(CD1D = "violet", LILRB2 = "violet", FOSB = "violet"), lig_scale = 10)
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    Network Showing Interaction Strength Across Data -

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    signaling_network() can be used to create a network plot such that nodes are clusters and the edges indicate signaling from one cluster to another.

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    In addition to changes such as scaling, thresholds, or layouts, this plot can be modified by selecting incoming or outgoing clusters (or both!). An example to view signaling from the CD14 Monocytes to other cluster:

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    -signaling_network(dom, showOutgoingSignalingClusts = "CD14_monocyte", scale = "none",
    -    norm = "none", layout = "fr", scale_by = "none", edge_weight = 2, vert_scale = 10)
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    Other Types of Plot -

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    Chord Diagrams Connecting Ligands and Receptors -

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    A new addition to domino2, circos_ligand_receptor() creates a chord plot showing ligands that can activate a specific receptor, displaying mean cluster expression of the ligand with the width of the chord.

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    -circos_ligand_receptor(dom, receptor = "CD74")
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    This function can be given cluster colors to match other plots you may make with your data. In addition, the plot can be adjusted by changing the threshold of ligand expression required for a linkage to be visualized or selecting clusters of interest.

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    -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)
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    Scatter Plot to Visualize Correlation -

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    cor_scatter() can be used to plot each cell based on expression of the selected TF and receptor. This produces a scatter plot as well as a line of best fit to look at receptor-TF correlation.

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    -cor_scatter(dom, "FOSB", "CD74")
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    Do keep in mind that there is an argument for remove_rec_dropout that should match the parameter that was used when the domino object was built. In this case, we did not use that build parameter, so we will leave the value in this argument as its default value of FALSE.

    -
    -
    -
    -

    Continued Development -

    -

    Since domino2 is a package still being developed, there are new functions and features that will be implemented in future versions. In the meantime, to view an example analysis, see our Getting Started page, or see our domino object structure page to get familiar with the object structure. Additionally, if you find any bugs, have further questions, or want to share an idea, please let us know here.

    -

    Vignette Build Information Date last built and session information:

    -
    -Sys.Date()
    -#> [1] "2023-12-20"
    -sessionInfo()
    -#> R version 4.2.1 (2022-06-23)
    -#> Platform: x86_64-pc-linux-gnu (64-bit)
    -#> Running under: Ubuntu 22.04.3 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_1.1.3 domino2_0.2.2  
    -#> 
    -#> loaded via a namespace (and not attached):
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    -#> [103] RCurl_1.98-1.13        tibble_3.2.1           crayon_1.5.2          
    -#> [106] car_3.1-2              utf8_1.2.4             BiocFileCache_2.6.1   
    -#> [109] rmarkdown_2.25         GetoptLong_1.0.5       progress_1.2.2        
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    -#> [115] tidyr_1.3.0            textshaping_0.3.7      stats4_4.2.1          
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