Plot Module Eigengene correlogram
Usage
ModuleCorrNetwork(
seurat_obj,
cluster_col = NULL,
exclude_grey = TRUE,
features = "hMEs",
reduction = "umap",
cor_cutoff = 0.2,
label_vertices = FALSE,
edge_scale = 5,
vertex_size = 15,
niter = 100,
vertex_frame = FALSE,
wgcna_name = NULL
)Examples
ModuleCorrNetwork
#> function (seurat_obj, cluster_col = NULL, exclude_grey = TRUE,
#> features = "hMEs", reduction = "umap", cor_cutoff = 0.2,
#> label_vertices = FALSE, edge_scale = 5, vertex_size = 15,
#> niter = 100, vertex_frame = FALSE, wgcna_name = NULL)
#> {
#> if (is.null(wgcna_name)) {
#> wgcna_name <- seurat_obj@misc$active_wgcna
#> }
#> if (features == "hMEs") {
#> MEs <- GetMEs(seurat_obj, TRUE, wgcna_name)
#> }
#> else if (features == "MEs") {
#> MEs <- GetMEs(seurat_obj, FALSE, wgcna_name)
#> }
#> else if (features == "scores") {
#> MEs <- GetModuleScores(seurat_obj, wgcna_name)
#> }
#> else if (features == "average") {
#> MEs <- GetAvgModuleExpr(seurat_obj, wgcna_name)
#> restrict_range <- FALSE
#> }
#> else (stop("Invalid feature selection. Valid choices: hMEs, MEs, scores, average"))
#> modules <- GetModules(seurat_obj, wgcna_name)
#> if (exclude_grey) {
#> MEs <- MEs[, colnames(MEs) != "grey"]
#> modules <- modules %>% subset(color != "grey")
#> }
#> mods <- colnames(MEs)
#> if (is.null(cluster_col)) {
#> clusters <- Idents(seurat_obj)
#> }
#> else {
#> clusters <- droplevels(seurat_obj@meta.data[[cluster_col]])
#> }
#> MEs$cluster <- clusters
#> cluster_ME_av <- do.call(rbind, lapply(split(MEs, MEs$cluster),
#> function(x) {
#> colMeans(x[, mods])
#> })) %>% as.data.frame
#> top_clusters <- sapply(mods, function(x) {
#> rownames(cluster_ME_av[cluster_ME_av[, x] == max(cluster_ME_av[,
#> x]), ])
#> })
#> red_df <- as.data.frame(seurat_obj@reductions[[reduction]]@cell.embeddings)
#> red_df$cluster <- clusters
#> red_av <- do.call(rbind, lapply(split(red_df, red_df$cluster),
#> function(x) {
#> colMeans(x[, 1:2])
#> })) %>% as.data.frame
#> cor_mat <- Hmisc::rcorr(as.matrix(MEs[, 1:ncol(MEs) - 1]))$r
#> cor_mat[lower.tri(cor_mat)] <- NA
#> cor_df <- reshape2::melt(cor_mat) %>% na.omit
#> cor_df <- cor_df %>% subset(!(Var1 == Var2))
#> cor_df <- cor_df %>% subset(abs(value) >= cor_cutoff)
#> v_df <- data.frame(name = mods, cluster = as.character(top_clusters))
#> unique_mods <- dplyr::distinct(modules[, c("module", "color")])
#> rownames(unique_mods) <- unique_mods$module
#> v_df$color <- unique_mods[v_df$name, "color"]
#> v_df$x <- red_av[v_df$cluster, 1]
#> v_df$y <- red_av[v_df$cluster, 2]
#> g <- igraph::graph_from_data_frame(cor_df, directed = FALSE,
#> vertices = v_df)
#> e <- get.edgelist(g, name = FALSE)
#> l <- qgraph::qgraph.layout.fruchtermanreingold(e, vcount = vcount(g),
#> weights = igraph::E(g)$value, repulse.rad = (vcount(g)),
#> niter = niter, )
#> plot_df <- rbind(cor_df, data.frame(Var1 = c("x", "y"), Var2 = c("y",
#> "x"), value = c(-1, 1)))
#> temp <- ggplot(plot_df, aes(x = value, y = value, color = value)) +
#> geom_point() + scale_color_gradient2(high = "darkorchid1",
#> mid = "white", low = "seagreen", midpoint = 0)
#> temp <- ggplot_build(temp)
#> igraph::E(g)$color <- temp$data[[1]]$colour[1:nrow(cor_df)]
#> if (label_vertices) {
#> labels <- igraph::V(g)$name
#> }
#> else {
#> labels <- NA
#> }
#> if (vertex_frame) {
#> frame_color <- "black"
#> }
#> else {
#> frame_color <- igraph::V(g)$color
#> }
#> plot(g, layout = l, edge.color = igraph::E(g)$color, edge.curved = 0,
#> edge.width = abs(igraph::E(g)$value) * edge_scale, vertex.color = igraph::V(g)$color,
#> vertex.frame.color = frame_color, vertex.label = labels,
#> vertex.label.family = "Helvetica", vertex.label.color = "black",
#> vertex.label.cex = 0.5, vertex.size = vertex_size)
#> }
#> <bytecode: 0x321a80390>
#> <environment: namespace:hdWGCNA>