updated setup function to re-use metacell objects (e7a90a68) · Commits · github_fork / HdWGCNA

R/WGCNA_functions.R

+28 −21

Original line number	Diff line number	Diff line
		@@ -274,7 +274,7 @@ TestSoftPowersConsensus <- function(
		#' @examples
		#' ConstructNetwork(pbmc)
		ConstructNetwork <- function(
		seurat_obj, soft_power=NULL,
		seurat_obj, soft_power=NULL, min_power=3,
		tom_outdir="TOM",
		use_metacells=TRUE,
		setDatExpr=TRUE, group.by=NULL,
		@@ -309,7 +309,6 @@ ConstructNetwork <- function(

		multiExpr <- GetMultiExpr(seurat_obj)
		checkSets(multiExpr) # check data size
		print('here')

		# constructing network on a single dataset
		} else{
		@@ -345,20 +344,17 @@ ConstructNetwork <- function(
		if(!dir.exists(tom_outdir)){
		dir.create(tom_outdir)
		}
		print('here')

		if(is.null(soft_power) & !consensus){
		soft_power <- GetPowerTable(seurat_obj) %>% subset(SFT.R.sq >= 0.8) %>% .$Power %>% min
		soft_power <- GetPowerTable(seurat_obj) %>% subset(SFT.R.sq >= 0.8 & Power > min_power) %>% .$Power %>% min
		cat(paste0("Soft power not provided. Automatically using the lowest power that meets 0.8 scale-free topology fit. Using soft_power = ", soft_power, "\n"))
		} else if(is.null(soft_power)){
		power_tables <- GetPowerTable(seurat_obj) %>% dplyr::group_split(group)
		soft_power <- sapply(power_tables, function(power_table){
		power_table %>% subset(SFT.R.sq >= 0.8) %>% .$Power %>% min
		power_table %>% subset(SFT.R.sq >= 0.8 & Power > min_power) %>% .$Power %>% min
		})
		cat(paste0("Soft power not provided. Automatically using the lowest power that meets 0.8 scale-free topology fit. Using soft_power = c(", paste0(soft_power, collapse=','), ")\n"))
		}
		print('here')


		# construct the network
		net <- WGCNA::blockwiseConsensusModules(
		@@ -1033,6 +1029,7 @@ OverlapModulesDEGs <- function(
		#' ProjectModules
		ProjectModules <- function(
		seurat_obj, seurat_ref,
		modules=NULL,
		group.by.vars=NULL,
		gene_mapping=NULL, # table mapping genes from species 1 to species 2
		genome1_col=NULL, genome2_col=NULL,
		@@ -1046,7 +1043,13 @@ ProjectModules <- function(
		if(is.null(wgcna_name)){wgcna_name <- seurat_ref@misc$active_wgcna}

		# get modules to be projected:
		if(is.null(modules)){
		modules <- GetModules(seurat_ref, wgcna_name)
		} else{
		if(!all(c("gene_name", "module", "color") %in% colnames(modules))){
		stop('Missing columns in modules table. Required columns are gene_name, module, color')
		}
		}

		# are we mapping gene names?
		if(!is.null(gene_mapping)){
		@@ -1072,18 +1075,6 @@ ProjectModules <- function(
		)
		}

		#
		# # scale the dataset if needed:
		# if(!scale_genes & sum(genes_use %in% rownames(GetAssayData(seurat_obj, slot='scale.data'))) == length(genes_use)){
		# print("Scaling already done.")
		# } else if(scale_genes){
		# print("Scaling dataset...")
		# seurat_obj <- Seurat::ScaleData(
		# seurat_obj, features = genes_use, ...
		# )
		# }


		# project modules:
		seurat_obj <- ModuleEigengenes(
		seurat_obj,
		@@ -1703,6 +1694,8 @@ ModuleTraitCorrelation <- function(
		group.by = NULL,
		features = 'hMEs',
		cor_method = 'pearson',
		subset_by = NULL,
		subset_groups = NULL,
		wgcna_name = NULL,
		...
		){
		@@ -1720,6 +1713,14 @@ ModuleTraitCorrelation <- function(
		stop('Invalid feature selection. Valid choices: hMEs, MEs, scores, average')
		}

		# subset?
		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]
		}

		# check if traits are in the seurat object:
		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=', ')))
		@@ -1842,7 +1843,13 @@ ModuleTraitCorrelation <- function(
		'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
		}

R/getters_and_setters.R

+1 −0

Original line number	Diff line number	Diff line
		@@ -212,6 +212,7 @@ SetDatExpr <- function(
		}

		# get the metadata from the seurat object:
		print('here')
		seurat_meta <- s_obj@meta.data

		# columns to group by for cluster/celltype

R/plotting.R

+3 −3

Original line number	Diff line number	Diff line
		@@ -486,7 +486,7 @@ ModuleFeaturePlot<- function(
		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,
		plot_ratio = 1, title=TRUE
		raster_scale=1, plot_ratio = 1, title=TRUE
		){

		if(is.null(wgcna_name)){wgcna_name <- seurat_obj@misc$active_wgcna}
		@@ -559,7 +559,7 @@ ModuleFeaturePlot<- function(

		# rasterise?
		if(raster){
		p <- p + ggrastr::rasterise(geom_point(size=point_size, alpha=alpha), dpi=raster_dpi)
		p <- p + ggrastr::rasterise(geom_point(size=point_size, alpha=alpha), dpi=raster_dpi, scale=raster_scale)
		} else{
		p <- p + geom_point(size=point_size, alpha=alpha)
		}
		@@ -1899,7 +1899,7 @@ PlotModuleTraitCorrelation <- function(
		text_digits = 3,
		combine = TRUE,
		wgcna_name = NULL,
		flip_coords = FALSE
		flip_coords =
		){

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

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