module-trait correlation (e93d6c44) · Commits · github_fork / HdWGCNA

R/WGCNA_functions.R

+477 −30

Original line number	Diff line number	Diff line
		@@ -14,6 +14,7 @@
		SelectNetworkGenes <- function(
		seurat_obj,
		gene_select="variable", fraction=0.05,
		n_chunks = 5, # for binarizing the exp matrix. This can be increased to save memory
		group.by=NULL, # should be a column in the Seurat object, eg clusters
		gene_list=NULL, wgcna_name=NULL
		){
		@@ -23,17 +24,22 @@ SelectNetworkGenes <- function(
		stop(paste0("Invalid selection gene_select: ", gene_select, '. Valid gene_selects are variable, fraction, all, or custom.'))
		}

		# TODO:
		# get genes that are expressed in a fraction of cells in select groups (ie celltypes)
		# this would reduce a bias against genes that are only expressed in underrepresented
		# cell-types

		# handle different selection strategies
		if(gene_select == "fraction"){

		# binarize counts matrix
		print('here1')

		# binarize counts matrix in chunks to save memory
		expr_mat <- GetAssayData(seurat_obj, slot='counts')
		expr_mat[expr_mat > 0] <- 1
		chunks <- cut(1:nrow(expr_mat), n_chunks)
		expr_mat <- do.call(rbind, lapply(levels(chunks), function(x){
		print(x)
		cur <- expr_mat[chunks == x,]
		cur[cur > 0] <- 1
		cur
		}))
		print('here')

		group_gene_list <- list()
		if(!is.null(group.by)){
		@@ -55,6 +61,7 @@ SelectNetworkGenes <- function(
		# identify genes that are expressed in at least some fraction of cells
		gene_filter <- rowSums(expr_mat) >= round(fraction*ncol(seurat_obj));
		gene_list <- rownames(seurat_obj)[gene_filter]
		print('here')
		}

		} else if(gene_select == "variable"){
		@@ -155,6 +162,7 @@ TestSoftPowers <- function(
		if(!("datExpr" %in% names(GetActiveWGCNA(seurat_obj))) \| setDatExpr == TRUE){
		seurat_obj <- SetDatExpr(seurat_obj, use_metacells=use_metacells, group.by=group.by, group_name=group_name)
		}

		# get datExpr
		datExpr <- GetDatExpr(seurat_obj)

		@@ -213,12 +221,114 @@ TestSoftPowers <- function(
		seurat_obj
		}



		TestSoftPowersConsensus <- function(
		seurat_obj,
		use_metacells = TRUE,
		group.by=NULL, group_name=NULL,
		multi.group.by = NULL,
		multi_groups = NULL,
		setDatExpr = TRUE,
		powers=c(seq(1,10,by=1), seq(12,30, by=2)),
		make_plot=TRUE, outfile="softpower", figsize=c(7,7)
		){

		# add multiExpr if not already added:
		if(!("multiExpr" %in% names(GetActiveWGCNA(seurat_obj))) \| setDatExpr == TRUE){

		# set
		seurat_obj <- SetMultiExpr(
		seurat_obj,
		group_name = group_name,
		group.by = group.by,
		multi.group.by = multi.group.by,
		multi_groups = multi_groups
		)
		}

		multiExpr <- GetMultiExpr(seurat_obj)

		# pick soft thresh for each consensus group:
		powerTables <- list()
		for(i in 1:length(multiExpr)){

		cur_group <- names(multiExpr)[i]
		print(cur_group)

		# Call the network topology analysis function for each set in turn
		powerTable = list(
		data = WGCNA::pickSoftThreshold(
		multiExpr[[cur_group]]$data,
		powerVector=powers,
		verbose = 100,
		networkType="signed",
		corFnc="bicor"
		)[[2]]
		);
		powerTables[[cur_group]] <- powerTable

		# Plot the results:
		if(make_plot){
		pdf(paste0(outfile, '_', cur_group, '.pdf'), height=figsize[1], width=figsize[2], useDingbats=FALSE)

		colors = c("blue", "red","black")
		# Will plot these columns of the returned scale free analysis tables
		plotCols = c(2,5,6,7)
		colNames = c("Scale Free Topology Model Fit", "Mean connectivity", "Mean connectivity",
		"Max connectivity");

		# Get the minima and maxima of the plotted points
		ylim = matrix(NA, nrow = 2, ncol = 4);
		for (col in 1:length(plotCols)){
		ylim[1, col] = min(ylim[1, col], powerTable$data[, plotCols[col]], na.rm = TRUE);
		ylim[2, col] = max(ylim[2, col], powerTable$data[, plotCols[col]], na.rm = TRUE);
		}

		# Plot the quantities in the chosen columns vs. the soft thresholding power
		par(mfcol = c(2,2));
		par(mar = c(4.2, 4.2 , 2.2, 0.5))
		cex1 = 0.7;

		for (col in 1:length(plotCols)){
		plot(powerTable$data[,1], -sign(powerTable$data[,3])*powerTable$data[,2],
		xlab="Soft Threshold (power)",ylab=colNames[col],type="n", ylim = ylim[, col],
		main = colNames[col]);
		addGrid();

		if (col==1){
		text(powerTable$data[,1], -sign(powerTable$data[,3])*powerTable$data[,2],
		labels=powers,cex=cex1,col=colors[1]);
		} else
		text(powerTable$data[,1], powerTable$data[,plotCols[col]],
		labels=powers,cex=cex1,col=colors[1]);
		}
		dev.off()
		}
		}

		# set the power table in Seurat object:
		seurat_obj <- SetPowerTable(seurat_obj, powerTable$data)
		seurat_obj

		}



		#' ConstructNetwork
		#'
		#' This function constructs a co-expression network from a Seurat object
		#'
		#' @param seurat_obj A Seurat object
		#' @param soft_power
		#' @param
		#' @param
		#' @param
		#' @param
		#' @param
		#' @param
		#' @param
		#' @param
		#' @keywords scRNA-seq
		#' @export
		#' @examples
		@@ -226,6 +336,9 @@ TestSoftPowers <- function(
		ConstructNetwork <- function(
		seurat_obj, soft_power=NULL, use_metacells=TRUE,
		setDatExpr=TRUE, group.by=NULL, group_name=NULL,
		consensus = FALSE,
		multi.group.by = NULL,
		multi_groups = NULL,
		tom_outdir="TOM",
		blocks=NULL, maxBlockSize=30000, randomSeed=12345, corType="pearson",
		consensusQuantile=0.3, networkType = "signed", TOMType = "unsigned",
		@@ -236,8 +349,31 @@ ConstructNetwork <- function(
		mergeCutHeight = 0.2, saveConsensusTOMs = TRUE, ...
		){

		# constructing network on multiple datasets (consensus WGCNA)
		if(consensus){

		# add multiExpr if not already added:
		if(!("multiExpr" %in% names(GetActiveWGCNA(seurat_obj))) \| setDatExpr == TRUE){

		# set
		seurat_obj <- SetMultiExpr(
		seurat_obj,
		group_name = group_name,
		group.by = group.by,
		multi.group.by = multi.group.by,
		multi_groups = multi_groups
		)
		}

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

		# constructing network on a single dataset
		} else{

		# add datExpr if not already added:
		if(!("datExpr" %in% names(GetActiveWGCNA(seurat_obj))) \| setDatExpr == TRUE){
		print('in here')
		seurat_obj <- SetDatExpr(
		seurat_obj,
		group_name = group_name,
		@@ -245,25 +381,27 @@ ConstructNetwork <- function(
		use_metacells=use_metacells,
		return_seurat=TRUE
		)
		print('out here')

		}

		# get datExpr
		# get datExpr from seurat object
		datExpr <- GetDatExpr(seurat_obj)

		if(is.null(group_name)){
		group_name <- 'all'
		}

		# Add functionality to accept multiExpr and perform consensus WGCNA
		# TODO

		nSets = 1
		setLabels = gsub(' ', '_', group_name)
		shortLabels = setLabels
		multiExpr <- list()
		multiExpr[[group_name]] <- list(data=datExpr)
		checkSets(multiExpr) # check data size
		}


		# make output dir for the TOM
		if(!dir.exists(tom_outdir)){
		dir.create(tom_outdir)
		}
		@@ -338,6 +476,129 @@ ConstructNetwork <- function(

		}

		#
		# #' ConstructNetwork
		# #'
		# #' This function constructs a co-expression network from a Seurat object
		# #'
		# #' @param seurat_obj A Seurat object
		# #' @param soft_power
		# #' @keywords scRNA-seq
		# #' @export
		# #' @examples
		# #' ConstructNetwork(pbmc)
		# ConstructNetwork <- function(
		# seurat_obj, soft_power=NULL, use_metacells=TRUE,
		# setDatExpr=TRUE, group.by=NULL, group_name=NULL,
		# tom_outdir="TOM",
		# blocks=NULL, maxBlockSize=30000, randomSeed=12345, corType="pearson",
		# consensusQuantile=0.3, networkType = "signed", TOMType = "unsigned",
		# TOMDenom = "min", scaleTOMs = TRUE, scaleQuantile = 0.8,
		# sampleForScaling = TRUE, sampleForScalingFactor = 1000,
		# useDiskCache = TRUE, chunkSize = NULL,
		# deepSplit = 4, pamStage=FALSE, detectCutHeight = 0.995, minModuleSize = 50,
		# mergeCutHeight = 0.2, saveConsensusTOMs = TRUE, ...
		# ){
		#
		# # add datExpr if not already added:
		# if(!("datExpr" %in% names(GetActiveWGCNA(seurat_obj))) \| setDatExpr == TRUE){
		# seurat_obj <- SetDatExpr(
		# seurat_obj,
		# group_name = group_name,
		# group.by=group.by,
		# use_metacells=use_metacells,
		# return_seurat=TRUE
		# )
		# }
		#
		# # get datExpr
		# datExpr <- GetDatExpr(seurat_obj)
		#
		# if(is.null(group_name)){
		# group_name <- 'all'
		# }
		#
		# nSets = 1
		# setLabels = gsub(' ', '_', group_name)
		# shortLabels = setLabels
		# multiExpr <- list()
		# multiExpr[[group_name]] <- list(data=datExpr)
		# checkSets(multiExpr) # check data size
		#
		# if(!dir.exists(tom_outdir)){
		# dir.create(tom_outdir)
		# }
		#
		#
		# net <- WGCNA::blockwiseConsensusModules(
		# multiExpr,
		# power = soft_power,
		# blocks = blocks,
		# maxBlockSize = maxBlockSize, ## This should be set to a smaller size if the user has limited RAM
		# randomSeed = randomSeed,
		# corType = corType,
		# consensusQuantile = consensusQuantile,
		# networkType = networkType,
		# TOMType = TOMType,
		# TOMDenom = TOMDenom,
		# scaleTOMs = scaleTOMs, scaleQuantile = scaleQuantile,
		# sampleForScaling = sampleForScaling, sampleForScalingFactor = sampleForScalingFactor,
		# useDiskCache = useDiskCache, chunkSize = chunkSize,
		# deepSplit = deepSplit,
		# pamStage=pamStage,
		# detectCutHeight = detectCutHeight, minModuleSize = minModuleSize,
		# mergeCutHeight = mergeCutHeight,
		# saveConsensusTOMs = saveConsensusTOMs,
		# consensusTOMFilePattern = "ConsensusTOM-block.%b.rda", ...)
		#
		# # rename consensusTOM file:
		# file.rename('ConsensusTOM-block.1.rda', paste0('TOM/', gsub(' ', '_',group_name), '_ConsensusTOM-block.1.rda'))
		#
		# # add network parameters to the Seurat object:
		#
		# params <- list(
		# power = soft_power,
		# blocks = blocks,
		# maxBlockSize = maxBlockSize, ## This should be set to a smaller size if the user has limited RAM
		# randomSeed = randomSeed,
		# corType = corType,
		# consensusQuantile = consensusQuantile,
		# networkType = networkType,
		# TOMType = TOMType,
		# TOMDenom = TOMDenom,
		# scaleTOMs = scaleTOMs, scaleQuantile = scaleQuantile,
		# sampleForScaling = sampleForScaling, sampleForScalingFactor = sampleForScalingFactor,
		# useDiskCache = useDiskCache, chunkSize = chunkSize,
		# deepSplit = deepSplit,
		# pamStage=pamStage,
		# detectCutHeight = detectCutHeight, minModuleSize = minModuleSize,
		# mergeCutHeight = mergeCutHeight,
		# saveConsensusTOMs = saveConsensusTOMs
		# )
		#
		# # add parameters:
		# seurat_obj <- SetWGCNAParams(seurat_obj, params)
		#
		# # append working directory to the TOM file so it has the full path:
		# net$TOMFiles <- paste0(getwd(), '/TOM/', group_name, '_', net$TOMFiles)
		#
		# # add network to seurat obj
		# seurat_obj <- SetNetworkData(seurat_obj, net)
		#
		# # set the modules df in the Seurat object
		# mods <- GetNetworkData(seurat_obj)$colors
		# seurat_obj <- SetModules(
		# seurat_obj, mod_df = data.frame(
		# "gene_name" = names(mods),
		# "module" = factor(mods, levels=unique(mods)),
		# "color" = mods
		# )
		# )
		#
		# seurat_obj
		#
		# }

		#' ComputeModuleEigengene
		#'
		#' Internal helper function that computes module eigengene for a single module.
		@@ -482,7 +743,7 @@ ModuleEigengenes <- function(
		seurat_obj
		}

		#' ModuleExprScores
		#' ModuleExprScore
		#'
		#' Computes module eigengenes for all WGCNA co-expression modules
		#'
		@@ -1284,6 +1545,64 @@ OverlapModulesMotifs <- function(
		}


		#' MotifTargetScore
		#'
		#' Computes gene expression scores for TF Motif target genes based on the MotifScan.
		#'
		#' @param seurat_obj A Seurat object
		#' @param method Seurat or UCell?
		#' @keywords scRNA-seq
		#' @export
		#' @examples
		#' MotifTargetScore(pbmc)
		MotifTargetScore <- function(
		seurat_obj,
		method='Seurat',
		wgcna_genes=TRUE,
		wgcna_name=NULL,
		...
		){

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

		# get modules:
		modules <- GetModules(seurat_obj, wgcna_name)

		# get TF target genes:
		target_genes <- GetMotifTargets(seurat_obj)

		# subset by WGCNA genes only:
		if(wgcna_genes){
		target_genes <- lapply(target_genes, function(x){
		x[x %in% modules$gene_name]
		})
		}

		# run gene scoring function
		if(method == "Seurat"){
		tf_scores <- Seurat::AddModuleScore(
		seurat_obj, features=target_genes, ...
		)@meta.data
		} else if(method == "UCell"){
		tf_scores <- UCell::AddModuleScore_UCell(
		seurat_obj, features=target_genes, ...
		)@meta.data
		} else{
		stop("Invalid method selection. Valid choices are Seurat, UCell")
		}

		tf_scores <- tf_scores[,(ncol(tf_scores)-length(target_genes)+1):ncol(tf_scores)]
		colnames(tf_scores) <- names(target_genes)

		# add tf scores to seurat object:
		seurat_obj <- SetMotifScores(seurat_obj, tf_scores, wgcna_name)

		seurat_obj

		}



		#' Run UMAP on co-expression matrix using hub genes as features.
		#'
		#' @param seurat_obj A Seurat object
		@@ -1377,3 +1696,131 @@ RunModuleUMAP <- function(

		seurat_obj
		}


		#' ModuleTraitCorrelation'
		#'
		#' Correlates categorical and numeric variables with Module Eigengenes or hub-gene scores.
		#'
		#'
		#' @param seurat_obj A Seurat object
		#' @param seurat_obj 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.
		#' @param features Which features to use to summarize each modules? Valid choices are hMEs, MEs, or scores
		#' @param cor_meth Which method to use for correlation? Valid choices are pearson, spearman, kendall.
		#' @param wgcna_name
		#' @keywords scRNA-seq
		#' @export
		#' @examples
		#' ModuleTraitCorrelation
		ModuleTraitCorrelation <- function(
		seurat_obj,
		traits,
		group.by = NULL,
		features = 'hMEs',
		cor_method = 'pearson',
		wgcna_name = NULL,
		...
		){

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

		# get MEs, module data from seurat object
		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')
		}

		# 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=', ')))
		}

		#use idents as grouping variable if not specified
		if(is.null(group.by)){
		group.by <- 'temp_ident'
		seurat_obj$temp_ident <- Idents(seurat_obj)
		}

		# check the class of each trait provided:
		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.'))
		}

		# print warnings about factor levels:
		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?'))
		}
		}

		# get modules
		modules <- GetModules(seurat_obj, wgcna_name)
		mods <- levels(modules$module)
		mods <- mods[mods != 'grey']

		# get trait table:
		trait_df <- seurat_obj@meta.data[,traits]

		# convert factors to numeric
		if(any(data_types == 'factor')){
		factor_traits <- traits[data_types == 'factor']
		for(tr in factor_traits){
		trait_df[,tr] <- as.numeric(trait_df[,tr])
		}
		}

		# also correlate all cells:
		cor_list <- list()
		cor_list[['all_cells']] <- cor(as.matrix(trait_df), as.matrix(MEs), method=cor_method)


		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]))
		}

		# do the correlation for each group:
		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)){
		cor_list[[i]] <- cor(as.matrix(trait_list[[i]]), as.matrix(ME_list[[i]]), method=cor_method)
		}


		# compute the correlation matrix
		#cor_mat <- cor(as.matrix(trait_df), as.matrix(MEs), method=cor_method)

		# this is the wgcna way but I think t-test doesn't make sense for single-cell data right???
		# cor_p <- corPvalueStudent(cor_mat, ncol(seurat_obj))

		# add Module-trait correlations to the seruat object:
		mt_cor <- list(
		'cor_mat' = cor_list,
		'pval' = NA,
		'fdr' = NA
		)

		seurat_obj <- SetModuleTraitCorrelation(seurat_obj, mt_cor, wgcna_name)
		seurat_obj
		}

R/getters_and_setters.R

+33 −2

Original line number	Diff line number	Diff line
		@@ -122,6 +122,7 @@ SetDatExpr <- function(
		multi_group_name = NULL,
		return_seurat = TRUE,
		wgcna_name=NULL,
		slot = 'data',
		...
		){

		@@ -171,7 +172,7 @@ SetDatExpr <- function(
		Seurat::GetAssayData(
		s_obj,
		assay=assay,
		slot='data'
		slot=slot
		)[genes_use,cells]
		)

		@@ -634,6 +635,22 @@ GetMotifOverlap <- function(seurat_obj, wgcna_name=NULL){
		seurat_obj@misc[[wgcna_name]]$motif_module_overlaps
		}


		############################
		# motif scores
		###########################

		SetMotifScores <- function(seurat_obj, tf_scores, wgcna_name=NULL){
		if(is.null(wgcna_name)){wgcna_name <- seurat_obj@misc$active_wgcna}
		seurat_obj@misc[[wgcna_name]]$motif_target_scores <- tf_scores
		seurat_obj
		}

		GetMotifScores <- function(seurat_obj, wgcna_name=NULL){
		if(is.null(wgcna_name)){wgcna_name <- seurat_obj@misc$active_wgcna}
		seurat_obj@misc[[wgcna_name]]$motif_target_scores
		}

		############################
		# ModuleUMAP
		###########################
		@@ -650,6 +667,20 @@ GetModuleUMAP <- function(seurat_obj, wgcna_name=NULL){
		seurat_obj@misc[[wgcna_name]]$module_umap
		}

		############################
		# ModuleTraitCorrelation
		###########################

		SetModuleTraitCorrelation <- function(seurat_obj, mt_cor, wgcna_name=NULL){
		if(is.null(wgcna_name)){wgcna_name <- seurat_obj@misc$active_wgcna}
		seurat_obj@misc[[wgcna_name]]$mt_cor <- mt_cor
		seurat_obj
		}

		GetModuleTraitCorrelation <- function(seurat_obj, wgcna_name=NULL){
		if(is.null(wgcna_name)){wgcna_name <- seurat_obj@misc$active_wgcna}
		seurat_obj@misc[[wgcna_name]]$mt_cor
		}


		############################
		@@ -833,7 +864,7 @@ ResetModuleColors <- function(
		# update module umap:
		umap_df <- GetModuleUMAP(seurat_obj, wgcna_name)
		if(!is.null(umap_df)){
		umap_df$module <- new_color_df[match(umap_df$color, new_color_df$old),'new']
		umap_df$color <- new_color_df[match(umap_df$color, new_color_df$old),'new']
		seurat_obj <- SetModuleUMAP(seurat_obj, umap_df, wgcna_name)
		}

R/metacells.R

+14 −3

Original line number	Diff line number	Diff line
		@@ -103,6 +103,10 @@ ConstructMetacells <- function(
		counts = new_exprs
		)

		print('here')
		print(meta)
		print(names(meta))

		# add meta-data:
		if(!is.null(meta)){
		meta_names <- names(meta)
		@@ -148,7 +152,7 @@ ConstructMetacells <- function(
		#' This function takes a Seurat object and constructs averaged 'metacells' based
		#' on neighboring cells in provided groupings, such as cluster or cell type.
		#' @param seurat_obj A Seurat object
		#' @param group.by A character vector of Seurat metadata column names representing groups for which metacells will be computed. Default = 'seurat_clusters'
		#' @param group.by A character vector of Seurat metadata column names representing groups for which metacells will be computed.
		#' @param k Number of nearest neighbors to aggregate. Default = 50
		#' @param name A string appended to resulting metalcells. Default = 'agg'
		#' @param reduction A dimensionality reduction stored in the Seurat object. Default = 'umap'
		@@ -167,6 +171,11 @@ MetacellsByGroups <- function(

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

		# check group.by for invalid characters:
		if(any(grepl('#', group.by))){
		stop('Invalid character # found in group.by, please re-name the group.')
		}

		# subset seurat object by seleted cells:
		if(!is.null(cells.use)){
		seurat_full <- seurat_obj
		@@ -179,7 +188,7 @@ MetacellsByGroups <- function(
		for(col in colnames(seurat_meta)){
		seurat_meta[[col]] <- as.character(seurat_meta[[col]])
		}
		seurat_obj$metacell_grouping <- apply(seurat_meta, 1, paste, collapse='_')
		seurat_obj$metacell_grouping <- apply(seurat_meta, 1, paste, collapse='#')
		} else {
		seurat_obj$metacell_grouping <- as.character(seurat_obj@meta.data[[group.by]])
		}
		@@ -192,7 +201,7 @@ MetacellsByGroups <- function(
		print(groupings)

		# unique meta-data for each group
		meta_df <- as.data.frame(do.call(rbind, strsplit(groupings, '_')))
		meta_df <- as.data.frame(do.call(rbind, strsplit(groupings, '#')))
		colnames(meta_df) <- group.by

		# list of meta-data to pass to each metacell seurat object
		@@ -206,6 +215,8 @@ MetacellsByGroups <- function(
		seurat_list <- lapply(groupings, function(x){seurat_obj[,seurat_obj$metacell_grouping == x]})
		names(seurat_list) <- groupings

		print(meta_list)

		# construct metacells
		metacell_list <- mapply(
		ConstructMetacells,

pkgdown_notes.Rmd

+3 −0

Original line number	Diff line number	Diff line
		@@ -33,3 +33,6 @@ random helper functions.
		I have no idea how to deploy yet. I also have no idea how to customize it.

		The author info goes into the DESCRIPTION file, and the citation goes in inst/CITATION


		To embed images inside of articles, images must all be placed inside of docs/articles !!! Sub-directories inside of docs/articles can be used for organization.

scWGCNA_testing.Rmd

+169 −1

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