moduleeigengene bugfix

Package: hdWGCNA

Title: hdWGCNA

Version: 0.1.1.9009

Version: 0.1.1.9010

Authors@R: c(

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

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

dev.off()

```

Set up human spatial dataset

```{r eval=FALSE}

seurat_vis <- readRDS('~/swaruplab/smorabit/analysis/scWGCNA/data/maynard_prefrontal.rds')

patch <- VisDimPlot(

  seurat_vis,

  group.by = 'layer_guess_reordered_short',

  sample_col = 'sample_name',

  dpi=600,

  ncol = 2,

  text_size=15

)

pdf(paste0(fig_dir, 'hex_ananotation_human.pdf'), width=6, height=6)

patch

dev.off()

```

Project modules into Human spatial dataset

```{r eval=FALSE}

seurat_vis <- ProjectModules(

  seurat_vis,

  seurat_ref = seurat_zhou,

  group.by.vars = "sample_name",

  gene_mapping=hg38_mm10_genes,

  genome1_col="hg38_name", # genome of reference data

  genome2_col="hg38_id", # genome of query data

  wgcna_name = "ASC",

  wgcna_name_proj = 'ASC_Projected'

)

# load mouse <-> human gene name table:

hg38_mm10_genes <- read.table(

  "/dfs7/swaruplab/smorabit/resources/hg38_mm10_orthologs_2021.txt",

  sep='\t',

  header=TRUE

)

colnames(hg38_mm10_genes) <-c('hg38_id', 'mm10_id', 'mm10_name', 'hg38_name')

hg38_mm10_genes <- dplyr::select(hg38_mm10_genes, c(hg38_name, hg38_id))

hg38_mm10_genes %<>% subset(hg38_id %in% rownames(seurat_vis))

hg38_ids <- unique(hg38_mm10_genes$hg38_id)

hg38_genes <- unique(hg38_mm10_genes$hg38_name)

hg38_mm10_genes <- hg38_mm10_genes[match(hg38_genes, hg38_mm10_genes$hg38_name),]

dim(hg38_mm10_genes)

length(hg38_ids)

length(hg38_genes)

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

# Dot Plot

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

MEs <- GetMEs(seurat_vis)

modules <- GetModules(seurat_vis)

mods <- levels(modules$module)

mods <- mods[mods!='grey']

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

# make dotplot

p <- DotPlot(

  seurat_vis,

  group.by='layer_guess_reordered',

  features = rev(mods)

) + coord_flip() + RotatedAxis() +

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

  theme(

    plot.title = element_text(hjust = 0.5),

    axis.line.x = element_blank(),

    axis.line.y = element_blank(),

    panel.border = element_rect(colour = "black", fill=NA, size=1)

  )

pdf(paste0(fig_dir, 'maynard_ASC_hMEs_dotplot.pdf'), width=9, height=5)

p

dev.off()

# remove from seurat obj

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

seurat_vis@meta.data <- seurat_vis@meta.data[,!(colnames(seurat_vis@meta.data) %in% colnames(MEs))]

for(cur_mod in mods){

  print(cur_mod)

  p <- SampleFeaturePlot(

    seurat_vis,

    feature=cur_mod,

    sample_col = "sample_name",

    ncol = 4,

    raster=TRUE,

    plot_max = 'q99',

    plot_min = 0,

    colfunc = inferno,

    rev_colors=TRUE,

    dpi=400,

  )

  pdf(paste0(fig_dir, 'ME_spatial_featureplots/', cur_mod, '_featureplot.pdf'), width=10, height=3)

  print(p)

  dev.off()

}

```

Project into mouse spatial

```{r eval=FALSE}

# load 10X genomics visium dataset

seurat_vis_mouse <- readRDS("~/swaruplab/smorabit/analysis/scWGCNA/data/10x_visium_scWGCNA.rds")

image_df <- seurat_vis_mouse@images$anterior1@coordinates

seurat_vis_mouse$row <- image_df$row

seurat_vis_mouse$imagerow <- image_df$imagerow

seurat_vis_mouse$col <- image_df$col

seurat_vis_mouse$imagecol <- image_df$imagecol

# process the Visium dataset with Seurat:

seurat_vis <- seurat_vis %>%

  NormalizeData() %>%

  FindVariableFeatures() %>%

  ScaleData() %>%

  RunPCA() %>%

  RunUMAP(dims=1:30) %>%

  FindNeighbors(dims=1:30) %>%

  FindClusters(res=0.75)

p1 <- DimPlot(seurat_vis_mouse, reduction = "umap", label = TRUE) +

  umap_theme() +

  NoLegend()

p2 <- SpatialDimPlot(seurat_vis_mouse, label = TRUE, label.size = 3) +

  NoLegend()

png(paste0(fig_dir, 'mouse_vis_umap.png'), units='in', res=400, width=10, height=5)

p1 | p2

dev.off()

# load mouse <-> human gene name table:

hg38_mm10_genes <- read.table(

  "/dfs7/swaruplab/smorabit/resources/hg38_mm10_orthologs_2021.txt",

  sep='\t',

  header=TRUE

)

colnames(hg38_mm10_genes) <-c('hg38_id', 'mm10_id', 'mm10_name', 'hg38_name')

hg38_mm10_genes <- dplyr::select(hg38_mm10_genes, c(hg38_name, mm10_name, hg38_id, mm10_id))

hg38_mm10_genes <- subset(hg38_mm10_genes, mm10_name != '' & hg38_name != '')

# TODO

# need to make sure that there's only one entry for each gene in hg38_mm10_genes

mm10_genes <- unique(hg38_mm10_genes$mm10_name)

hg38_genes <- unique(hg38_mm10_genes$hg38_name)

hg38_mm10_genes <- hg38_mm10_genes[match(mm10_genes, hg38_mm10_genes$mm10_name),]

 seurat_mg <- readRDS(file='/dfs7/swaruplab/smorabit/analysis/scWGCNA/microglia/data/AD_MG_scWGCNA.rds')

seurat_vis_mouse <- ProjectModules(

  seurat_vis_mouse,

  seurat_ref = seurat_mg,

  #group.by.vars = "sample_name",

  gene_mapping=hg38_mm10_genes,

  genome1_col="hg38_name", # genome of reference data

  genome2_col="mm10_name", # genome of query data

  wgcna_name = "MG",

  wgcna_name_proj = 'MG_Projected'

)

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

# Dot Plot

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

MEs <- GetMEs(seurat_vis_mouse)

modules <- GetModules(seurat_vis_mouse)

mods <- levels(modules$module)

mods <- mods[mods!='grey']

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

#

# # make dotplot

# p <- DotPlot(

#   seurat_vis_mouse,

#   group.by='layer_guess_reordered',

#   features = rev(mods)

# ) + coord_flip() + RotatedAxis() +

#   scale_color_gradient2(high='red', mid='grey95', low='blue') + xlab('') + ylab('') +

#   theme(

#     plot.title = element_text(hjust = 0.5),

#     axis.line.x = element_blank(),

#     axis.line.y = element_blank(),

#     panel.border = element_rect(colour = "black", fill=NA, size=1)

#   )

#

# pdf(paste0(fig_dir, 'maynard_ASC_hMEs_dotplot.pdf'), width=9, height=5)

# p

# dev.off()

#

# # remove from seurat obj

# seurat_vis@meta.data <- cbind(seurat_vis@meta.data, MEs)

#

# seurat_vis@meta.data <- seurat_vis@meta.data[,!(colnames(seurat_vis@meta.data) %in% colnames(MEs))]

#

for(cur_mod in mods){

  print(cur_mod)

  p <- SampleFeaturePlot(

    seurat_vis_mouse,

    feature=cur_mod,

    sample_col = "orig.ident",

    ncol = 1,

    raster=TRUE,

    plot_max = 'q99',

    plot_min = 0,

    colfunc = inferno,

    rev_colors=TRUE,

    dpi=400,

  )

  pdf(paste0(fig_dir, 'mouse_ME_spatial_featureplots/', cur_mod, '_featureplot.pdf'), width=5, height=5)

  print(p)

  dev.off()

}

```

# hdWGCNA 0.1.1.9009 (2022-07-30)

## Added

- None

## Changes

- By default, `ModuleEigengenes` does not compute MEs for the grey module. User can change behavior with the `exclude_grey` flag.

# hdWGCNA 0.1.1.9009 (2022-07-23)

## Added

- None

#' @param scale.model.use model to scale data when running ScaleData choices are "linear", "poisson", or "negbinom"

#' @param pc_dim Which PC to use as the module eigengene? Default to 1.

#' @param assay Assay in seurat_obj to compute module eigengenes. Default is DefaultAssay(seurat_obj)

#' @param exclude_grey logical determining whether to compute MEs for the grey module

#' @param wgcna_name name of the WGCNA experiment

#' @keywords scRNA-seq

#' @export

  verbose=TRUE,

  assay = NULL,

  pc_dim = 1,

  exclude_grey = TRUE,

  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}

  # get list of modules:

  mods <- levels(modules$module)

  mods_loop <- mods

  # exclude grey:

  if(exclude_grey){

    mods_loop <- mods[mods != 'grey']

  }

  # loop over modules:

  for(cur_mod in mods){

  for(cur_mod in mods_loop){

    print(cur_mod)

  MEs <- GetMEs(seurat_obj, harmonized, wgcna_name)

  modules$module <- factor(

    as.character(modules$module),

    levels=colnames(MEs)

    levels=mods

  )

  seurat_obj <- SetModules(seurat_obj, modules, wgcna_name)

      </button>

      <span class="navbar-brand">

        <a class="navbar-link" href="https://smorabit.github.io/hdWGCNA/index.html">hdWGCNA</a>

        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">0.1.1.9009</span>

        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">0.1.1.9010</span>

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