ModuleTFNetwork.RdPlotting the relationships between a TF and the co-expression modules
ModuleTFNetwork(
seurat_obj,
tf_name,
tf_gene_name,
edge.alpha = 0.75,
cor_thresh = 0.25,
high_color = "red",
mid_color = "grey",
low_color = "blue",
slot = "data",
size.scale = 30,
tf_x = 0,
tf_y = 0,
wgcna_name = NULL
)A Seurat object
the Motif name for the tf
the gene associated with this tf in the rownames(seurat_obj)
scaling factor for edge opacity in the network
threshold to plot correlation edges between modules
color for positive correlation
color for zero correlation
color for negative correlation
the slot in the seurat object to extract expression data for the tf_gene_name
scaling factor for the size of each node
x coordinate for the TF if the TF is not found in the UMAP
y coordinate for the TF if the TF is not foudn in the UMAP
the name of the WGCNA experiment in the seurat object
ModuleTFNetwork
#> function (seurat_obj, tf_name, tf_gene_name, edge.alpha = 0.75,
#> cor_thresh = 0.25, high_color = "red", mid_color = "grey",
#> low_color = "blue", slot = "data", size.scale = 30, tf_x = 0,
#> tf_y = 0, wgcna_name = NULL)
#> {
#> if (is.null(wgcna_name)) {
#> wgcna_name <- seurat_obj@misc$active_wgcna
#> }
#> modules <- GetModules(seurat_obj, wgcna_name) %>% subset(module !=
#> "grey") %>% mutate(module = droplevels(module))
#> MEs <- GetMEs(seurat_obj, TRUE, wgcna_name) %>% as.matrix
#> MEs <- MEs[, colnames(MEs) != "grey"]
#> mod_sizes <- table(modules$module)
#> module_cor <- Hmisc::rcorr(x = MEs, type = "pearson")$r
#> module_cor[lower.tri(module_cor)] <- NA
#> module_cor <- reshape2::melt(module_cor) %>% na.omit
#> module_cor <- subset(module_cor, abs(value) >= cor_thresh &
#> Var1 != Var2)
#> module_cor
#> cur_exp <- GetAssayData(seurat_obj, slot = slot)[tf_gene_name,
#> ]
#> exp_cor <- Hmisc::rcorr(x = MEs, y = cur_exp)$r[1:ncol(MEs),
#> "y"]
#> exp_cor <- data.frame(mod = names(exp_cor), value = as.numeric(exp_cor))
#> plot_lim <- abs(max(c(abs(range(exp_cor$value)), abs(range(module_cor$value)))))
#> p <- ggplot(module_cor, aes(x = Var1, y = Var2, color = value)) +
#> geom_point() + scale_color_gradient2(high = high_color,
#> mid = mid_color, low = low_color, limits = c(-1 * plot_lim,
#> plot_lim))
#> ggp <- ggplot_build(p)
#> module_cor$color <- ggp$data[[1]]$colour
#> p <- ggplot(exp_cor, aes(x = mod, y = mod, color = value)) +
#> geom_point() + scale_color_gradient2(high = high_color,
#> mid = mid_color, low = low_color, limits = c(-1 * plot_lim,
#> plot_lim))
#> ggp <- ggplot_build(p)
#> exp_cor$color <- ggp$data[[1]]$colour
#> umap_df <- GetModuleUMAP(seurat_obj, wgcna_name)
#> mods <- levels(umap_df$modules)
#> tf_match <- GetMotifMatrix(seurat_obj)
#> tf_targets <- GetMotifTargets(seurat_obj)
#> motif_df <- GetMotifs(seurat_obj)
#> overlap_df <- GetMotifOverlap(seurat_obj)
#> centroid_df <- umap_df %>% dplyr::select(c(UMAP1, UMAP2,
#> module)) %>% dplyr::group_by(module) %>% dplyr::summarise(x = mean(UMAP1),
#> y = mean(UMAP2))
#> cur_overlap <- subset(overlap_df, tf == tf_name)
#> node_df <- dplyr::left_join(centroid_df, cur_overlap, by = "module") %>%
#> dplyr::rename(c(UMAP1 = x, UMAP2 = y, name = module))
#> node_df$size <- as.numeric(node_df$size_intersection)/as.numeric(mod_sizes)
#> if (tf_gene_name %in% umap_df$gene) {
#> tf_df <- umap_df[umap_df$gene == tf_gene_name, ] %>%
#> dplyr::select(-c(hub, kME, module)) %>% dplyr::rename(name = gene)
#> }
#> else {
#> tf_df <- data.frame(name = tf_gene_name, module = "grey",
#> color = "grey", UMAP1 = tf_x, UMAP2 = tf_y)
#> }
#> tf_df$size <- 0.25
#> edge_df <- data.frame(Var1 = tf_gene_name, Var2 = as.character(node_df$name),
#> value = node_df$odds_ratio, color = exp_cor$color)
#> edge_df$color <- ifelse(node_df$fdr <= 0.05, edge_df$color,
#> "grey")
#> edge_df <- subset(edge_df, color != "grey")
#> node_df <- dplyr::bind_rows(node_df, tf_df) %>% as.data.frame()
#> rownames(node_df) <- as.character(node_df$name)
#> g1 <- igraph::graph_from_data_frame(edge_df, directed = TRUE,
#> vertices = node_df)
#> g2 <- igraph::graph_from_data_frame(module_cor, directed = FALSE,
#> vertices = node_df)
#> plot(g2, layout = as.matrix(node_df[, c("UMAP1", "UMAP2")]),
#> vertex.size = 1, edge.curved = 0, edge.width = 1, vertex.color = "grey",
#> vertex.label = "", edge.color = adjustcolor(igraph::E(g2)$color,
#> alpha.f = edge.alpha), )
#> plot(g1, layout = as.matrix(node_df[, c("UMAP1", "UMAP2")]),
#> edge.color = adjustcolor(igraph::E(g1)$color), vertex.size = igraph::V(g1)$size *
#> size.scale, edge.curved = 0, edge.width = edge_df$value *
#> 2, vertex.color = igraph::V(g1)$color, vertex.label = igraph::V(g1)$name,
#> vertex.label.dist = 1.1, vertex.label.degree = -pi/4,
#> vertex.label.family = "Helvetica", vertex.label.font = 3,
#> vertex.label.color = "black", vertex.label.cex = 0, vertex.frame.color = "black",
#> margin = 0, edge.arrow.size = edge_df$value/2, add = TRUE)
#> }
#> <bytecode: 0x7f77cfdfcbc0>
#> <environment: namespace:hdWGCNA>