Makes barplots from Enrichr data
Usage
EnrichrDotPlot(
seurat_obj,
database,
mods = "all",
n_terms = 3,
break_ties = TRUE,
logscale = TRUE,
wgcna_name = NULL,
...
)Arguments
- seurat_obj
A Seurat object
- database
name of the enrichr database to plot.
- mods
names of modules to plot. All modules are plotted if mods='all' (default)
- n_terms
number of enriched terms to plot for each module
- break_ties
logical controlling whether or not to randomly select terms with equal enrichments to precisely enforce n_terms.
- logscale
logical controlling whether to plot the enrichment on a log scale.
- wgcna_name
The name of the hdWGCNA experiment in the seurat_obj@misc slot
Examples
EnrichrDotPlot
#> function (seurat_obj, database, mods = "all", n_terms = 3, p_cutoff = 0.05,
#> p_adj = TRUE, break_ties = TRUE, term_size = 10, wgcna_name = NULL)
#> {
#> if (is.null(wgcna_name)) {
#> wgcna_name <- seurat_obj@misc$active_wgcna
#> }
#> modules <- GetModules(seurat_obj, wgcna_name)
#> if (mods == "all") {
#> mods <- levels(modules$module)
#> mods <- mods[mods != "grey"]
#> }
#> enrichr_df <- GetEnrichrTable(seurat_obj, wgcna_name)
#> if (p_adj) {
#> enrichr_df <- subset(enrichr_df, Adjusted.P.value <=
#> p_cutoff)
#> }
#> else {
#> enrichr_df <- subset(enrichr_df, P.value <= p_cutoff)
#> }
#> mod_colors <- dplyr::select(modules, c(module, color)) %>%
#> dplyr::distinct()
#> enrichr_df$color <- mod_colors[match(enrichr_df$module, mod_colors$module),
#> "color"]
#> wrapText <- function(x, len) {
#> sapply(x, function(y) paste(strwrap(y, len), collapse = "\n"),
#> USE.NAMES = FALSE)
#> }
#> top_terms <- enrichr_df %>% subset(db == database & module %in%
#> mods) %>% group_by(module) %>% slice_max(order_by = Combined.Score,
#> n = n_terms) %>% .$Term
#> plot_df <- subset(enrichr_df, Term %in% top_terms)
#> if (break_ties) {
#> plot_df <- do.call(rbind, lapply(plot_df %>% group_by(module) %>%
#> group_split, function(x) {
#> x[sample(n_terms), ]
#> }))
#> }
#> plot_df <- plot_df %>% mutate(Term = stringr::str_replace(Term,
#> " \\s*\\([^\\)]+\\)", ""))
#> plot_df$Term <- wrapText(plot_df$Term, 45)
#> plot_df$module <- factor(as.character(plot_df$module), levels = levels(modules$module))
#> plot_df <- arrange(plot_df, module)
#> plot_df$Term <- factor(as.character(plot_df$Term), levels = unique(as.character(plot_df$Term)))
#> if (p_adj) {
#> plot_df$p <- plot_df$Adjusted.P.value
#> }
#> else {
#> plot_df$p <- plot_df$P.value
#> }
#> max_p <- quantile(-log(plot_df$p), 0.95)
#> plot_df$logp <- -log(plot_df$p)
#> plot_df$logp <- ifelse(plot_df$logp > max_p, max_p, plot_df$logp)
#> p <- plot_df %>% ggplot(aes(x = module, y = Term, size = log10(Combined.Score),
#> color = logp)) + geom_point() + Seurat::RotatedAxis() +
#> ylab("") + xlab("") + labs(color = bquote("-log"[10] ~
#> "(P)"), size = bquote("log"[10] ~ "(Enrich)")) + scale_y_discrete(limits = rev) +
#> ggtitle(database) + theme(plot.title = element_text(hjust = 0.5),
#> axis.line.x = element_blank(), axis.line.y = element_blank(),
#> axis.text.y = element_text(size = term_size), panel.border = element_rect(colour = "black",
#> fill = NA, size = 1), panel.grid = element_line(size = 0.25,
#> color = "lightgrey"))
#> p
#> }
#> <bytecode: 0x7f854eda05c8>
#> <environment: namespace:hdWGCNA>