123 triple col is bad for examples

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: dominoSignal
 Title: Cell Communication Analysis for Single Cell RNA Sequencing
-Version: 0.99.2
+Version: 0.99.3
 Authors@R: c(
     person("Christopher", "Cherry", role = c("aut"), email = "[email protected]", comment = c(ORCID = "0000-0002-5481-0055")),
     person("Jacob T", "Mitchell", role = c("aut", "cre"), email = "[email protected]", comment = c(ORCID = "0000-0002-5370-9692")),
@@ -31,7 +31,7 @@ Imports:
 License: GPL-3 | file LICENSE
 Encoding: UTF-8
 LazyData: false
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 biocViews:
     SystemsBiology,
     SingleCell,

NAMESPACE

@@ -24,6 +24,7 @@ export(feat_heatmap)
 export(gene_network)
 export(incoming_signaling_heatmap)
 export(mean_ligand_expression)
+export(mock_linkage_summary)
 export(plot_differential_linkages)
 export(rename_clusters)
 export(signaling_heatmap)

R/class_definitions.R

@@ -79,8 +79,9 @@ linkage_summary <- setClass(
 #' @return A printed description of the number of cells and clusters in the domino object
 #' @export
 #' @examples
-#' print(dominoSignal:::pbmc_dom_built_tiny)
-#' 
+#' example(build_domino)
+#' print(pbmc_dom_built_tiny)
+#'
 setMethod("print", "domino", function(x, ...) {
   if (x@misc$build) {
     message(
@@ -106,9 +107,8 @@ setMethod("print", "domino", function(x, ...) {
 #' @return A printed description of cell numbers and clusters in the object
 #' @export
 #' @examples
-#' dominoSignal:::pbmc_dom_built_tiny
-#' 
-#' show(dominoSignal:::pbmc_dom_built_tiny)
+#' example(build_domino)
+#' show(pbmc_dom_built_tiny)
 #' 
 setMethod("show", "domino", function(object) {
   if (object@misc$build) {

R/convenience_fxns.R

@@ -13,9 +13,10 @@ NULL
 #' @return A domino object with clusters renamed in all applicable slots.
 #' @export
 #' @examples 
+#' example(build_domino)
 #' 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)
+#' pbmc_dom_built_tiny <- rename_clusters(pbmc_dom_built_tiny, new_clust)
 #'
 rename_clusters <- function(dom, clust_conv, warning = FALSE) {
     if (is.null(dom@clusters)) {

R/data.R

@@ -0,0 +1,47 @@
+#' SCENIC AUC subset
+#'
+#' A subset of SCENIC AUCs as applied to PBMC data.
+#'
+#' @format A list of:
+#' \describe{
+#'  \item{auc_tiny}{A subset of SCENIC AUCs}
+#'  \item{regulons_tiny}{A subset of SCENIC regulons}
+#' }
+#'
+#' @source <https://zenodo.org/records/10951634/files>
+#' @usage data("SCENIC")
+"SCENIC"
+
+
+#' PBMC RNAseq data subset
+#'
+#' A subset of the results of PBMC RNA-seq data.
+#'
+#' @format A list of::
+#' \describe{
+#'  \item{RNA_count_tiny}{A subset of PBMC RNA-seq data: counts assay}
+#'  \item{RNA_zscore_tiny}{A subset of PBMC RNA-seq data: zscore assay}
+#'  \item{clusters_tiny}{A subset of PBMC RNA-seq data: clusters as defined by cell_type}
+#' }
+#'
+#' @source <https://zenodo.org/records/10951634/files/pbmc3k_sce.rds>
+#' @usage data("PBMC")
+"PBMC"
+
+
+#' CellPhoneDB subset
+#'
+#' A list of four subsets of CellPhoneDB data.
+#'
+#'
+#' @format A list of:
+#' \describe{
+#'  \item{genes_tiny}{A subet of CellPhoneDB gene_input.csv}
+#'  \item{proteins_tiny}{A subset of CellPhoneDB protein_input.csv}
+#'  \item{complexes_tiny}{A subset of CellPhoneDB complex_input.csv}
+#'  \item{interactions_tiny}{A subset of CellPhoneDB interaction_input.csv}
+#' }
+#' 
+#' @source <https://github.com/ventolab/cellphonedb-data/archive/refs/tags/v4.0.0.tar.gz>
+#' @usage data("CellPhoneDB")
+"CellPhoneDB"

R/differential_fxns.R

@@ -11,14 +11,47 @@ NULL
 #' @param subject_names vector of subject names in domino_results. If NULL, defaults to first column of subject_meta.
 #' @return A linkage summary class object consisting of nested lists of the active transcription factors, active receptors, and incoming ligands for each cluster across multiple domino results
 #' @export
-#' @examples 
-#' dom_ls <- dominoSignal:::dom_ls_tiny
+#' @examples
+#' example(build_domino)
+#' 
+#' #create alternative clustering
+#' clusters_tiny_alt <- setNames(c(121:240, 1:120, 241:360), names(PBMC$clusters_tiny))
+#' clusters_tiny_alt <- as.factor(clusters_tiny_alt)
+#' 
+#' #build an alternative domino object
+#' pbmc_dom_tiny_alt <- create_domino(
+#'   rl_map = rl_map_tiny,
+#'   features = SCENIC$auc_tiny,
+#'   counts = PBMC$RNA_count_tiny,
+#'   z_scores = PBMC$RNA_zscore_tiny,
+#'   clusters = clusters_tiny_alt,
+#'   tf_targets = regulon_list_tiny,
+#'   use_clusters = TRUE,
+#'   use_complexes = TRUE,
+#'   remove_rec_dropout = FALSE
+#' )
+#' 
+#' pbmc_dom_built_tiny_alt <- build_domino(
+#'   dom = pbmc_dom_tiny_alt,
+#'   min_tf_pval = .05,
+#'   max_tf_per_clust = Inf,
+#'   max_rec_per_tf = Inf,
+#'   rec_tf_cor_threshold = .1,
+#'   min_rec_percentage = 0.01
+#' )
+#'
+#' #create a list of domino objects
+#' dom_ls <- list(
+#'  dom1 = pbmc_dom_built_tiny,
+#'  dom2 = pbmc_dom_built_tiny_alt
+#')
+#'
+#' #compare the linkages across the two domino objects
 #' meta_df <- data.frame("ID" = c("dom1", "dom2"), "group" = c("A", "B"))
 #' summarize_linkages(
-#'  domino_results = dom_ls, subject_meta = meta_df, 
+#'  domino_results = dom_ls, subject_meta = meta_df,
 #'  subject_names = meta_df$ID
 #')
-#' 
 summarize_linkages <- function(domino_results, subject_meta, subject_names = NULL) {
   if (!is(domino_results, "list")) {
     stop("domino_results must be provided as a named list where names correspond to subject names")
@@ -108,7 +141,7 @@ summarize_linkages <- function(domino_results, subject_meta, subject_names = NUL
 #' @export
 #' @examples
 #' count_linkage(
-#'   linkage_summary = dominoSignal:::linkage_sum_tiny, cluster = "C1", 
+#'   linkage_summary = mock_linkage_summary(), cluster = "C1", 
 #'   group.by = "group", linkage = "rec")
 #' 
 count_linkage <- function(linkage_summary, cluster, group.by = NULL, linkage = "rec_lig", subject_names = NULL) {
@@ -169,8 +202,8 @@ count_linkage <- function(linkage_summary, cluster, group.by = NULL, linkage = "
 #' }
 #' @export
 #' @examples
-#' test_differential_linkages(
-#'   linkage_summary = dominoSignal:::linkage_sum_tiny, cluster = "C1", group.by = "group", 
+#' tiny_differential_linkage_c1 <- test_differential_linkages(
+#'   linkage_summary = mock_linkage_summary(), cluster = "C1", group.by = "group",
 #'   linkage = "rec", test_name = "fishers.exact"
 #' )
 #' 

R/import_fxns.R

@@ -22,9 +22,11 @@ NULL
 #' @return Data frame where each row describes a possible receptor-ligand interaction
 #' @export create_rl_map_cellphonedb
 #' @examples
-#' rl_map_tiny <- create_rl_map_cellphonedb(genes = dominoSignal:::genes_tiny, 
-#'  proteins = dominoSignal:::proteins_tiny, interactions = dominoSignal:::interactions_tiny, 
-#'  complexes = dominoSignal:::complexes_tiny)
+#' data(CellPhoneDB)
+#' rl_map_tiny <- create_rl_map_cellphonedb(genes = CellPhoneDB$genes_tiny,
+#'  proteins = CellPhoneDB$proteins_tiny,
+#'  interactions = CellPhoneDB$interactions_tiny,
+#'  complexes =CellPhoneDB$complexes_tiny)
 #' 
 create_rl_map_cellphonedb <- function(
     genes, proteins, interactions, complexes = NULL, database_name = "CellPhoneDB",
@@ -242,7 +244,8 @@ create_rl_map_cellphonedb <- function(
 #' @return A list where names are transcription factors and the stored values are character vectors of genes in the inferred regulons
 #' @export create_regulon_list_scenic
 #' @examples
-#' regulon_list_tiny <- create_regulon_list_scenic(regulons = dominoSignal:::regulons_tiny)
+#' data(SCENIC)
+#' regulon_list_tiny <- create_regulon_list_scenic(regulons = SCENIC$regulons_tiny)
 #'
 create_regulon_list_scenic <- function(regulons) {
   if (is(regulons, "character")) {
@@ -293,21 +296,26 @@ create_regulon_list_scenic <- function(regulons) {
 #' @param tf_variance_quantile What proportion of variable features to take if using variance to threshold features. Default is 0.5. Higher numbers will keep more features. Ignored if tf_selection_method is not 'variable'
 #' @return A domino object
 #' @export create_domino
-#' @examples 
-#' pbmc_dom_tiny_all <- create_domino(
-#'  rl_map = dominoSignal:::rl_map_tiny, features = dominoSignal:::auc_tiny, 
-#'  counts = dominoSignal:::RNA_count_tiny, z_scores = dominoSignal:::RNA_zscore_tiny,
-#'  clusters = dominoSignal:::clusters_tiny, tf_targets = dominoSignal:::regulon_list_tiny, 
-#'  use_clusters = FALSE, use_complexes = FALSE, 
+#' @examples
+#' example(create_rl_map_cellphonedb)
+#' example(create_regulon_list_scenic)
+#' data(SCENIC)
+#' data(PBMC)
+#'
+#' pbmc_dom_tiny <- create_domino(
+#'  rl_map = rl_map_tiny, features = SCENIC$auc_tiny,
+#'  counts = PBMC$RNA_count_tiny, z_scores = PBMC$RNA_zscore_tiny,
+#'  clusters = PBMC$clusters_tiny, tf_targets = regulon_list_tiny,
+#'  use_clusters = TRUE, use_complexes = TRUE, remove_rec_dropout = FALSE)
+#'
+#' pbmc_dom_tiny_no_clusters <- create_domino(
+#'  rl_map = rl_map_tiny, features = SCENIC$auc_tiny,
+#'  counts = PBMC$RNA_count_tiny, z_scores =PBMC$RNA_zscore_tiny,
+#'  clusters = PBMC$clusters_tiny, tf_targets = regulon_list_tiny,
+#'  use_clusters = FALSE, use_complexes = FALSE,
 #'  rec_min_thresh = 0.1, remove_rec_dropout = TRUE,
 #'  tf_selection_method = "all")
-#' 
-#' 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)
-#' 
+#'
 create_domino <- function(
     rl_map, features, counts = NULL, z_scores = NULL,
     clusters = NULL, use_clusters = TRUE, tf_targets = NULL, verbose = TRUE,
@@ -633,9 +641,10 @@ convert_genes <- function(
 #' @return An updated RL signaling data frame
 #' @export
 #' @examples 
+#' example(create_rl_map_cellphonedb)
 #' 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")
+#' rl_map_expanded <- add_rl_column(map = rl_map_tiny, map_ref = "type_A",
+#' conv = lr_name, new_name = "type_A_full")
 #' 
 add_rl_column <- function(map, map_ref, conv, new_name) {
   map_in_ref <- match(map[[map_ref]], conv[, 1])
@@ -676,7 +685,8 @@ add_rl_column <- function(map, map_ref, conv, new_name) {
 #' @return A data frame of ligand expression targeting the specified receptor
 #' @export
 #' @examples
-#' counts <- dom_counts(dominoSignal:::pbmc_dom_built_tiny)
+#' example(build_domino)
+#' counts <- dom_counts(pbmc_dom_built_tiny)
 #' mean_exp <- mean_ligand_expression(counts,
 #'  ligands = c("PTPRC", "FASLG"), cell_ident = "CD14_monocyte",
 #'  cell_barcodes = colnames(counts), destination = "FAS")