bugfixes

Package: hdWGCNA

Title: hdWGCNA

Version: 0.1.1.9008

Version: 0.1.1.9009

Authors@R: c(

    person("Sam", "Morabito", , "smorabit@uci.edu", role = c("aut", "cre"),

           comment = c(ORCID = "0000-0002-7768-4856")),

```{r eval=FALSE}

library(Seurat)

library(tidyverse)

library(cowplot)

library(patchwork)

library(WGCNA)

library(Matrix)

library(viridis)

library(harmony)

library(RColorBrewer)

library(ggpubr)

library(tictoc)

library(RColorBrewer)

library(Hmisc)

library(corrplot)

library(enrichR)

library(GeneOverlap)

library(grid)

library(gridExtra)

library(igraph)

library(ggrepel)

#library(hdWGCNA)

enableWGCNAThreads(nThreads = 8)

theme_set(theme_cowplot())

set.seed(12345)

# spatial plotting functions

source('/dfs7/swaruplab/smorabit/analysis/scWGCNA/bin/spatial_functions.R')

source("/pub/smorabit/hdWGCNA/bin/spatial_functions.R")

#devtools::install_github('smorabit/hdWGCNA', ref='dev')

library(hdWGCNA)

setwd('/dfs7/swaruplab/smorabit/analysis/scWGCNA/')

# output directories

data_dir <- "data/"

fig_dir <- 'figures/'

# re-load scWGCNA dataset:

load(file='data/Zhou_color_scheme.rda')

# reload process ed with all cell types

# seurat_obj <-  readRDS(file=paste0(data_dir, 'Zhou_scWGCNA_all_celltypes.rds'))

load('/dfs7/swaruplab/smorabit/analysis/AD_NucSeq_2019/batch_correction/liger/update/celltype-analysis/data/color_scheme.rda')

cp <- unlist(color_scheme_snRNA_celltype)

cp[names(cp) == 'EX'] <- 'turquoise'

setwd('/dfs7/swaruplab/smorabit/analysis/scWGCNA/project_modules/')

seurat_zhou <- readRDS(file= '/dfs7/swaruplab/smorabit/analysis/scWGCNA/brain_iterative/data/Zhou_scWGCNA_all_celltypes.rds')

```

Project Bulk RNA-seq modules into single-cell dataset

```{r eval=FALSE}

# load Morabito & miyoshi et al dataset

seurat_obj <- readRDS("/dfs7/swaruplab/smorabit/analysis/scWGCNA/data/Swarup_2021.rds")

# load consensus modules from

consensus_modules <- read.csv("/dfs7/swaruplab/smorabit/analysis/scWGCNA/data/hmg_cWGCNA_modules.csv")

consensus_modules <- consensus_modules %>%

  dplyr::select(c(-Ensembl.Gene.ID)) %>%

  dplyr::rename(c(gene_name = GeneSymbol, module = ModuleLabels, color = Initially.Assigned.Module.Color)) %>%

   dplyr::select(c(gene_name, module, color))

consensus_modules <- subset(consensus_modules, gene_name %in% rownames(seurat_obj))

# remove duplicate gene names

consensus_modules <- consensus_modules[match(unique(consensus_modules$gene_name), consensus_modules$gene_name),]

seurat_obj <- FindVariableFeatures(seurat_obj, nfeatures=2000)

seurat_obj <- ScaleData(seurat_obj)

seurat_obj <- ProjectModules(

  seurat_obj,

  modules = consensus_modules,

  group.by.vars = "Batch",

  seurat_ref = NULL,

  wgcna_name = "None",

  wgcna_name_proj = 'HMG_2020'

)

saveRDS(seurat_obj, file = 'data/Swarup_2021_hmg_projected.rds')

plot_list <- ModuleFeaturePlot(seurat_obj, order='shuffle', raster=TRUE, raster_dpi=400, alpha=1, restrict_range=FALSE, raster_scale=0.25)

pdf("figures/featureplot_hmg_projected.pdf",height=9, width=16)

wrap_plots(plot_list, ncol=6)

dev.off()

# get hMEs from seurat object

MEs <- GetMEs(seurat_obj, harmonized=TRUE)

mods <- colnames(MEs); mods <- mods[mods != 'grey']

# add hMEs to Seurat meta-data:

seurat_obj@meta.data <- cbind(seurat_obj@meta.data, MEs)

# plot with Seurat's DotPlot function

p <- DotPlot(seurat_obj, features=mods, group.by = 'annotation', dot.min=0.25)

# flip the x/y axes, rotate the axis labels, and change color scheme:

p <- p +

  coord_flip() +

  RotatedAxis() +

  scale_color_gradient2(high='red', mid='grey95', low='blue')

# plot output

pdf("figures/dotplot_hmg_projected.pdf",height=6, width=12)

p

dev.off()

```

# hdWGCNA 0.1.1.9009 (2022-07-23)

## Added

- None

## Changes

- Bugfix in `ProjectModules`.

- Bugfix in `MetacellsByGroups`

# hdWGCNA 0.1.1.9008 (2022-07-21)

## Added

- New tutorial for consensus co-expression network analysis.

  wgcna_name=NULL, ...

){

  print('in here')

  # set as active assay if wgcna_name is not given

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

#' @examples

#' ProjectModules

ProjectModules <- function(

  seurat_obj, seurat_ref,

  seurat_obj,

  seurat_ref,

  modules=NULL,

  group.by.vars=NULL,

  gene_mapping=NULL, # table mapping genes from species 1 to species 2

    }

  }

  # cast "modules" to a factor

  if(!is.factor(modules$module)){

    modules$module <- as.factor(modules$module)

  }

  # are we mapping gene names?

  if(!is.null(gene_mapping)){

    modules <- TransferModuleGenome(modules, gene_mapping, genome1_col, genome2_col)

  }

  print(head(modules))

  # get genes that overlap between WGCNA genes & seurat_obj genes:

  gene_names <- modules$gene_name

  genes_use <- intersect(gene_names, rownames(seurat_obj))

  # subset modules by genes in this seurat object:

  modules <- modules %>% subset(gene_name %in% genes_use)

  print(head(modules))

  # setup new seurat obj for wgcna:

  if(!(wgcna_name_proj %in% names(seurat_obj@misc))){

  }

  # project modules:

  print('project modules')

  seurat_obj <- ModuleEigengenes(

    seurat_obj,

    group.by.vars=group.by.vars,

    wgcna_name = wgcna_name_proj,

    ...

  )

  print('done')

  seurat_obj

  groupings <- groupings[order(groupings)]

  # remove groups that are too small:

  group_counts <- table(seurat_obj$metacell_grouping) >= min_cells

  group_counts <- table(seurat_obj$metacell_grouping) < min_cells

  if(any(group_counts)){

    warning(paste0("Removing the following groups that did not meet min_cells: ", paste(names(group_counts)[group_counts], collapse=', ')))

  }

  groupings <- groupings[table(seurat_obj$metacell_grouping) >= min_cells]

  if(length(groupings) == 0 ){

  )

  names(metacell_list) <- groupings

  print('done making metacells')

  # remove NULL

  remove <- which(sapply(metacell_list, is.null))

  if(length(remove) > 1){

    metacell_list <- metacell_list[-remove]

  }

  # get the run stats:

  run_stats <- as.data.frame(do.call(rbind, lapply(metacell_list, function(x){x@misc$run_stats})))

  rownames(run_stats) <- 1:nrow(run_stats)

  for(i in 1:length(group.by)){

    run_stats[[group.by[i]]] <- do.call(rbind, strsplit(run_stats$name, '#'))[,i]

    run_stats[[group.by[i]]] <- do.call(rbind, strsplit(as.character(run_stats$name), '#'))[,i]

  }

  # combine metacell objects

  print(length(metacell_list))

  if(length(metacell_list) > 1){

    metacell_obj <- merge(metacell_list[[1]], metacell_list[2:length(metacell_list)])

  } else{

    metacell_obj <- metacell_list[[1]]

  }

  print('metacell shape')

  # set idents for metacell object:

  Idents(metacell_obj) <- metacell_obj@meta.data[[ident.group]]