small change to module connectivity to allow the user to select the slot & assay (27b17bde) · Commits · github_fork / HdWGCNA

R/WGCNA_functions.R

+14 −4

Original line number	Diff line number	Diff line
		@@ -881,6 +881,9 @@ AvgModuleExpr <- function(seurat_obj, n_genes = 25, wgcna_name=NULL, ...){
		ModuleConnectivity <- function(
		seurat_obj,
		harmonized=TRUE,
		assay = NULL,
		slot = NULL,
		use_metacells = FALSE,
		wgcna_name = NULL,
		...
		){
		@@ -888,17 +891,23 @@ ModuleConnectivity <- function(
		# set as active assay if wgcna_name is not given
		if(is.null(wgcna_name)){wgcna_name <- seurat_obj@misc$active_wgcna}

		if(use_metacells){
		warning('use_metacells option is not implemented yet.')
		}

		# get module df, wgcna genes, and wgcna params:
		modules <- GetModules(seurat_obj, wgcna_name)
		MEs <- GetMEs(seurat_obj, harmonized, wgcna_name)
		genes_use <- GetWGCNAGenes(seurat_obj, wgcna_name)
		params <- GetWGCNAParams(seurat_obj, wgcna_name)

		# datExpr for full expression dataset
		if(is.null(assay)){assay <- params$metacell_assay}
		if(is.null(slot)){slot <- params$metacell_slot}

		datExpr <- t(GetAssayData(
		seurat_obj,
		assay=params$metacell_assay,
		slot=params$metacell_slot
		assay=assay,
		slot=slot
		))[,genes_use]

		print('datExpr')
		@@ -915,6 +924,7 @@ ModuleConnectivity <- function(
		)

		# add module color to the kMEs table
		modules <- modules[,1:3]
		kMEs <- cbind(modules, kMEs)
		colnames(kMEs) <- c(colnames(modules), paste0("kME_", colnames(MEs)))

vignettes/basic_tutorial.Rmd

+1 −1

Original line number	Diff line number	Diff line
		@@ -403,7 +403,7 @@ seurat_obj <- ModuleConnectivity(seurat_obj)

		For convenience, we re-name the scWGCNA modules to indicate that they are from
		the inhibitory neuron group. More information about renaming modules can be
		found in the [module customization tutorial](articles/customization.html).
		found in the [module customization tutorial](customization.html).

		```{r eval=FALSE}

vignettes/projecting_modules.Rmd

+10 −44

Original line number	Diff line number	Diff line
		@@ -111,6 +111,9 @@ seurat_query <- ProjectModules(
		As you can see it only takes running one function to project co-expression modules
		from one dataset into another using scWGCNA. Optionally, we can also compute the
		hub gene expression scores and the intramodular connectivity for the projected modules.
		Note that if we do not run the `ModuleConnectivity` function on the query dataset,
		the kME values in the module assignment table `GetModules(seurat_query)` are the
		kME values from the reference dataset.


		<details> <summary> Code </summary>
		@@ -123,54 +126,11 @@ seurat_query <- ModuleExprScore(
		)

		# compute intramodular connectivity
		seurat_query <- ModuleConnectivity(seurat_query)
		seurat_query <- ModuleConnectivity(seurat_query, assay="RNA", slot="data")

		```
		</details>

		```{r eval=FALSE, echo=FALSE}

		################################################################################
		# TODO: update ModuleConnectivity function to allow the user to
		# select the assay rather than getting straight from the params.
		# also allow to do it on the metacells object.
		################################################################################

		seurat_query <- readRDS(file=paste0(data_dir, 'Swarup_2021.rds'))
		seurat_query <- subset(seurat_query, Diagnosis == 'Control')

		# Project modules from query to reference dataset
		seurat_query <- ProjectModules(
		seurat_obj = seurat_query,
		seurat_ref = seurat_ref,
		scale_genes = TRUE,
		# vars.to.regress = c(), # optionally regress covariates when running ScaleData
		wgcna_name_proj="Zhou_projected", # name of the new scWGCNA experiment in the query dataset
		wgcna_name = "train" # name of the scWGCNA experiment in the ref dataset
		)

		modules <- GetModules(seurat_query)
		MEs <- GetMEs(seurat_query)
		genes_use <- GetWGCNAGenes(seurat_query)
		params <- GetWGCNAParams(seurat_query)

		# kME values are the same because the modules table is just the same
		datExpr <- t(GetAssayData(
		seurat_query,
		assay="RNA",
		slot="data"
		))[,genes_use]
		dim(datExpr)

		kMEs <- WGCNA::signedKME(
		datExpr,
		MEs,
		outputColumnName = "kME",
		corFnc = "bicor"
		)

		```

		We can extract the projected module eigengenes using the `GetMEs` function.

		```{r eval=FALSE}
		@@ -179,4 +139,10 @@ projected_hMEs <- GetModules(seurat_query)

		```

		The projected modules can be used in most of the downstream scWGCNA analysis
		tasks and visualization functions, such as [module trait correlation](module_trait_correlation.html),
		or.

		## Visualization

		Compare the hub gene UMAP between the projected and the query (use gganimate!!)

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