can re-use metaclel objects in setupforwgcna (31b48656) · Commits · github_fork / HdWGCNA

R/WGCNA_functions.R

+10 −1

Original line number	Diff line number	Diff line
		@@ -111,7 +111,9 @@ SelectNetworkGenes <- function(
		#' SetupForWGCNA(pbmc)
		SetupForWGCNA <- function(
		seurat_obj, wgcna_name,
		group=NULL, features = NULL,
		group=NULL,
		features = NULL,
		metacell_location = NULL,
		...
		){

		@@ -133,6 +135,13 @@ SetupForWGCNA <- function(
		seurat_obj <- SetWGCNAGenes(seurat_obj, gene_list=features, wgcna_name=wgcna_name)
		}

		if(!is.null(metacell_location)){
		if(!(metacell_location %in% names(seurat_obj@misc))){
		stop('metacell_location not found in seurat_obj@misc')
		}
		seurat_obj <- SetMetacellObject(seurat_obj, metacell_location, wgcna_name)
		}

		seurat_obj
		}

R/getters_and_setters.R

+8 −1

Original line number	Diff line number	Diff line
		@@ -109,7 +109,14 @@ GetMetacellObject <- function(seurat_obj, wgcna_name=NULL){

		# get data from active assay if wgcna_name is not given
		if(is.null(wgcna_name)){wgcna_name <- seurat_obj@misc$active_wgcna}
		seurat_obj@misc[[wgcna_name]]$wgcna_metacell_obj

		if(class(seurat_obj@misc[[wgcna_name]]$wgcna_metacell_obj) == "Seurat"){
		return(seurat_obj@misc[[wgcna_name]]$wgcna_metacell_obj)
		} else{
		metacell_location <- seurat_obj@misc[[wgcna_name]]$wgcna_metacell_obj
		return(seurat_obj@misc[[metacell_location]]$wgcna_metacell_obj)
		}

		}

		############################

R/plotting.R

+39 −10

Original line number	Diff line number	Diff line
		@@ -485,7 +485,8 @@ ModuleFeaturePlot<- function(
		seurat_obj, module_names=NULL, wgcna_name = NULL,
		reduction='umap', features = 'hMEs',
		order_points=TRUE, restrict_range=TRUE, point_size = 0.5, alpha=1,
		label_legend = FALSE, ucell = FALSE, raster=FALSE, raster_dpi=500
		label_legend = FALSE, ucell = FALSE, raster=FALSE, raster_dpi=500,
		plot_ratio = 1, title=TRUE
		){

		if(is.null(wgcna_name)){wgcna_name <- seurat_obj@misc$active_wgcna}
		@@ -564,7 +565,14 @@ ModuleFeaturePlot<- function(
		}

		# add title and theme:
		p <- p + ggtitle(cur_mod) + umap_theme() + labs(color="")
		p <- p + umap_theme() + labs(color="")

		if(title){
		p <- p + ggtitle(cur_mod)
		}

		# aspect ratio:
		p <- p + coord_fixed(ratio = plot_ratio)

		# UCell?
		if(!ucell){
		@@ -600,7 +608,7 @@ ModuleFeaturePlot<- function(

		#' EnrichrBarPlot
		#'
		#' Makes barplots from RunEnrhcr output.
		#' Makes barplots from RunEnrichr output.
		#'
		#' @param seurat_obj A Seurat object
		#' @param outdir directory to place output .pdf files
		@@ -1042,6 +1050,7 @@ HubGeneNetworkPlot <- function(
		edge_df <- temp
		print(dim(edge_df))


		# scale edge values between 0 and 1 for each module
		edge_df <- edge_df %>% group_by(color) %>% mutate(value=scale01(value))

		@@ -1051,6 +1060,8 @@ HubGeneNetworkPlot <- function(
		alpha(edge_df$color[i], alpha=a)
		})



		g <- igraph::graph_from_data_frame(
		edge_df,
		directed=FALSE,
		@@ -1121,17 +1132,24 @@ ModuleUMAPPlot <- function(

		# get the UMAP df:
		umap_df <- GetModuleUMAP(seurat_obj, wgcna_name)
		mods <- levels(umap_df$modules)

		# subset module df by genes in the UMAP df:
		selected_modules <- modules[umap_df$gene,]
		selected_modules <- cbind(selected_modules, umap_df[,c('UMAP1', 'UMAP2', 'hub', 'kME')])
		mods <- levels(umap_df$module)
		mods <- mods[mods != 'grey']

		# subset the TOM:
		subset_TOM <- TOM[umap_df$gene, umap_df$gene[umap_df$hub == 'hub']]

		# genes to label:
		hub_labels <- selected_modules %>% group_by(module) %>% top_n(label_hubs, wt=kME) %>% .$gene_name
		# hub_labels <- selected_modules %>% group_by(module) %>% top_n(label_hubs, wt=kME) %>% .$gene_name
		hub_list <- lapply(mods, function(cur_mod){
		cur <- subset(modules, module == cur_mod)
		cur[,c('gene_name', paste0('kME_', cur_mod))] %>%
		top_n(label_hubs) %>% .$gene_name
		})
		names(hub_list) <- mods
		hub_labels <- as.character(unlist(hub_list))
		print('hub labels')
		print(hub_labels)
		print(label_genes)
		if(is.null(label_genes)){
		label_genes <- hub_labels
		} else{
		@@ -1140,6 +1158,12 @@ ModuleUMAPPlot <- function(
		}
		label_genes <- unique(c(label_genes, hub_labels))
		}
		print(label_genes)

		# subset module df by genes in the UMAP df:
		selected_modules <- modules[umap_df$gene,]
		selected_modules <- cbind(selected_modules, umap_df[,c('UMAP1', 'UMAP2', 'hub', 'kME')])

		selected_modules$label <- ifelse(selected_modules$gene_name %in% label_genes, selected_modules$gene_name, '')
		selected_modules$fontcolor <- ifelse(selected_modules$color == 'black', 'gray50', 'black')

		@@ -1231,6 +1255,10 @@ ModuleUMAPPlot <- function(
		vertices=selected_modules
		)

		print('making net')
		print(head(edge_df))
		print(head(selected_modules))

		if(return_graph){return(g)}

		plot(
		@@ -1870,7 +1898,8 @@ PlotModuleTraitCorrelation <- function(
		text_color = 'black',
		text_digits = 3,
		combine = TRUE,
		wgcna_name = NULL
		wgcna_name = NULL,
		flip_coords = FALSE
		){

		if(is.null(wgcna_name)){wgcna_name <- seurat_obj@misc$active_wgcna}

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