ModuleTraitCorrelation.RdCorrelates categorical and numeric variables with Module Eigengenes or hub-gene scores.
ModuleTraitCorrelation(
seurat_obj,
traits,
group.by = NULL,
features = "hMEs",
cor_method = "pearson",
subset_by = NULL,
subset_groups = NULL,
wgcna_name = NULL,
...
)A list of column names in the Seurat object's metadata that you wish to correlate with each module. Traits must be a categorical variable (not a character vector), or a numeric variable.
Which features to use to summarize each modules? Valid choices are hMEs, MEs, or scores
The name of the hdWGCNA experiment in the seurat_obj@misc slot
Which method to use for correlation? Valid choices are pearson, spearman, kendall.
ModuleTraitCorrelation
#> function (seurat_obj, traits, group.by = NULL, features = "hMEs",
#> cor_method = "pearson", subset_by = NULL, subset_groups = NULL,
#> wgcna_name = NULL, ...)
#> {
#> if (is.null(wgcna_name)) {
#> wgcna_name <- seurat_obj@misc$active_wgcna
#> }
#> CheckWGCNAName(seurat_obj, wgcna_name)
#> if (features == "hMEs") {
#> MEs <- GetMEs(seurat_obj, TRUE, wgcna_name)
#> }
#> else if (features == "MEs") {
#> MEs <- GetMEs(seurat_obj, FALSE, wgcna_name)
#> }
#> else if (features == "scores") {
#> MEs <- GetModuleScores(seurat_obj, wgcna_name)
#> }
#> else {
#> stop("Invalid feature selection. Valid choices: hMEs, MEs, scores, average")
#> }
#> if (!is.null(subset_by)) {
#> print("subsetting")
#> seurat_full <- seurat_obj
#> MEs <- MEs[seurat_obj@meta.data[[subset_by]] %in% subset_groups,
#> ]
#> seurat_obj <- seurat_obj[, seurat_obj@meta.data[[subset_by]] %in%
#> subset_groups]
#> }
#> if (sum(traits %in% colnames(seurat_obj@meta.data)) != length(traits)) {
#> stop(paste("Some of the provided traits were not found in the Seurat obj:",
#> paste(traits[!(traits %in% colnames(seurat_obj@meta.data))],
#> collapse = ", ")))
#> }
#> if (is.null(group.by)) {
#> group.by <- "temp_ident"
#> seurat_obj$temp_ident <- Idents(seurat_obj)
#> }
#> valid_types <- c("numeric", "factor", "integer")
#> data_types <- sapply(traits, function(x) {
#> class(seurat_obj@meta.data[, x])
#> })
#> if (!all(data_types %in% valid_types)) {
#> incorrect <- traits[!(data_types %in% valid_types)]
#> stop(paste0("Invalid data types for ", paste(incorrect,
#> collapse = ", "), ". Accepted data types are numeric, factor, integer."))
#> }
#> if (any(data_types == "factor")) {
#> factor_traits <- traits[data_types == "factor"]
#> for (tr in factor_traits) {
#> warning(paste0("Trait ", tr, " is a factor with levels ",
#> paste0(levels(seurat_obj@meta.data[, tr]), collapse = ", "),
#> ". Levels will be converted to numeric IN THIS ORDER for the correlation, is this the expected order?"))
#> }
#> }
#> modules <- GetModules(seurat_obj, wgcna_name)
#> mods <- levels(modules$module)
#> mods <- mods[mods != "grey"]
#> trait_df <- seurat_obj@meta.data[, traits]
#> if (length(traits == 1)) {
#> trait_df <- data.frame(x = trait_df)
#> colnames(trait_df) <- traits
#> }
#> if (any(data_types == "factor")) {
#> factor_traits <- traits[data_types == "factor"]
#> for (tr in factor_traits) {
#> trait_df[, tr] <- as.numeric(trait_df[, tr])
#> }
#> }
#> cor_list <- list()
#> pval_list <- list()
#> fdr_list <- list()
#> temp <- Hmisc::rcorr(as.matrix(trait_df), as.matrix(MEs),
#> type = cor_method)
#> cur_cor <- temp$r[traits, mods]
#> cur_p <- temp$P[traits, mods]
#> p_df <- cur_p %>% reshape2::melt()
#> if (length(traits) == 1) {
#> tmp <- rep(mods, length(traits))
#> tmp <- factor(tmp, levels = mods)
#> tmp <- tmp[order(tmp)]
#> p_df$Var1 <- traits
#> p_df$Var2 <- tmp
#> rownames(p_df) <- 1:nrow(p_df)
#> p_df <- dplyr::select(p_df, c(Var1, Var2, value))
#> }
#> p_df <- p_df %>% dplyr::mutate(fdr = p.adjust(value, method = "fdr")) %>%
#> dplyr::select(c(Var1, Var2, fdr))
#> cur_fdr <- reshape2::dcast(p_df, Var1 ~ Var2, value.var = "fdr")
#> rownames(cur_fdr) <- cur_fdr$Var1
#> cur_fdr <- cur_fdr[, -1]
#> cor_list[["all_cells"]] <- cur_cor
#> pval_list[["all_cells"]] <- cur_p
#> fdr_list[["all_cells"]] <- cur_fdr
#> trait_df <- cbind(trait_df, seurat_obj@meta.data[, group.by])
#> colnames(trait_df)[ncol(trait_df)] <- "group"
#> MEs <- cbind(as.data.frame(MEs), seurat_obj@meta.data[, group.by])
#> colnames(MEs)[ncol(MEs)] <- "group"
#> if (class(seurat_obj@meta.data[, group.by]) == "factor") {
#> group_names <- levels(seurat_obj@meta.data[, group.by])
#> }
#> else {
#> group_names <- levels(as.factor(seurat_obj@meta.data[,
#> group.by]))
#> }
#> trait_list <- dplyr::group_split(trait_df, group, .keep = FALSE)
#> ME_list <- dplyr::group_split(MEs, group, .keep = FALSE)
#> names(trait_list) <- group_names
#> names(ME_list) <- group_names
#> for (i in names(trait_list)) {
#> temp <- Hmisc::rcorr(as.matrix(trait_list[[i]]), as.matrix(ME_list[[i]]))
#> cur_cor <- temp$r[traits, mods]
#> cur_p <- temp$P[traits, mods]
#> p_df <- cur_p %>% reshape2::melt()
#> if (length(traits) == 1) {
#> tmp <- rep(mods, length(traits))
#> tmp <- factor(tmp, levels = mods)
#> tmp <- tmp[order(tmp)]
#> p_df$Var1 <- traits
#> p_df$Var2 <- tmp
#> rownames(p_df) <- 1:nrow(p_df)
#> p_df <- dplyr::select(p_df, c(Var1, Var2, value))
#> }
#> p_df <- p_df %>% dplyr::mutate(fdr = p.adjust(value,
#> method = "fdr")) %>% dplyr::select(c(Var1, Var2,
#> fdr))
#> cur_fdr <- reshape2::dcast(p_df, Var1 ~ Var2, value.var = "fdr")
#> rownames(cur_fdr) <- cur_fdr$Var1
#> cur_fdr <- cur_fdr[, -1]
#> cor_list[[i]] <- cur_cor
#> pval_list[[i]] <- cur_p
#> fdr_list[[i]] <- as.matrix(cur_fdr)
#> }
#> mt_cor <- list(cor = cor_list, pval = pval_list, fdr = fdr_list)
#> if (!is.null(subset_by)) {
#> seurat_full <- SetModuleTraitCorrelation(seurat_full,
#> mt_cor, wgcna_name)
#> seurat_obj <- seurat_full
#> }
#> else {
#> seurat_obj <- SetModuleTraitCorrelation(seurat_obj, mt_cor,
#> wgcna_name)
#> }
#> seurat_obj
#> }
#> <bytecode: 0x7f77d279f2d0>
#> <environment: namespace:hdWGCNA>