Commit dd7fdbe7 authored by smorabit's avatar smorabit
Browse files

added enrichment tutorial

parent bef3e8d0

R/WGCNA_functions.R

+27 −17
Original line number Diff line number Diff line
@@ -919,12 +919,12 @@ ModuleConnectivity <- function(seurat_obj, harmonized=TRUE, wgcna_name=NULL, ... 


#' RunEnrichr

#'

#' Computes intramodular connectivity (kME) based on module eigengenes.

#' Run Enrichr gene set enrichment tests on scWGCNA modules

#'

#' @param seurat_obj A Seurat object

#' @param dbs List of EnrichR databases

#' @param dbs character vector of EnrichR databases

#' @param max_genes Max number of genes to include per module, ranked by kME.

#' @param wgcna_name

#' @param wgcna_name The name of the scWGCNA experiment in the seurat_obj@misc slot

#' @keywords scRNA-seq

#' @export

#' @examples
@@ -975,23 +975,30 @@ RunEnrichr <- function( 


#' OverlapModulesDEGs

#'

#' Computes intramodular connectivity (kME) based on module eigengenes.

#' Performs Fisher's Exact Test for overlap between DEGs and scWGCNA modules.

#'

#' @param seurat_obj A Seurat object

#' @param dbs List of EnrichR databases

#' @param max_genes Max number of genes to include per module, ranked by kME.

#' @param wgcna_name

#' @param deg_df DEG table formatted like the output from Seurat's FindMarkers

#' @param fc_cutoff log fold change cutoff for DEGs to be included in the overlap test

#' @param group_col the name of the column in deg_df containing the cell grouping information

#' @param wgcna_name The name of the scWGCNA experiment in the seurat_obj@misc slot

#' @keywords scRNA-seq

#' @export

#' @examples

#' OverlapModulesDEGs

OverlapModulesDEGs <- function(

  seurat_obj, deg_df, wgcna_name = NULL, fc_cutoff = 0.5, ...

  seurat_obj,

  deg_df,

  fc_cutoff = 0.5,

  group_col = 'cluster',

  wgcna_name = NULL,

  ...

){



  # get data from active assay if wgcna_name is not given

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



  deg_df$group <- deg_df[,group_col]

  cell_groups <- deg_df$group %>% unique



  # get modules,
@@ -1015,8 +1022,10 @@ OverlapModulesDEGs <- function( 


  # run overlaps between module gene lists and DEG lists:

  overlap_df <- do.call(rbind, lapply(mods, function(cur_mod){

    #print(cur_mod)

    cur_module_genes <- modules %>% subset(module == cur_mod) %>% .$gene_name

    cur_overlap_df <- do.call(rbind, lapply(cell_groups, function(cur_group){

      #print(cur_group)

      cur_DEGs <- deg_df %>% subset(group == cur_group & p_val_adj <= 0.05 & avg_log2FC > fc_cutoff) %>% .$gene

      cur_overlap <- testGeneOverlap(newGeneOverlap(

          cur_module_genes,
@@ -1034,6 +1043,7 @@ OverlapModulesDEGs <- function( 
    cur_overlap_df

  }))





  # adjust for multiple comparisons:

  overlap_df$fdr <- p.adjust(overlap_df$pval, method='fdr')
@@ -1061,7 +1071,7 @@ OverlapModulesDEGs <- function( 
#' @param seurat_obj A Seurat object

#' @param dbs List of EnrichR databases

#' @param max_genes Max number of genes to include per module, ranked by kME.

#' @param wgcna_name

#' @param wgcna_name The name of the scWGCNA experiment in the seurat_obj@misc slot

#' @keywords scRNA-seq

#' @export

#' @examples
@@ -1477,7 +1487,7 @@ MotifScan <- function( 
#' Overlap modules with TF target genes

#'

#' @param seurat_obj A Seurat object

#' @param wgcna_name

#' @param wgcna_name The name of the scWGCNA experiment in the seurat_obj@misc slot

#' @keywords scRNA-seq

#' @export

#' @examples
@@ -1604,22 +1614,23 @@ MotifTargetScore <- function( 
 }







 #' RunModuleUMAP

 #'

#' Run UMAP on co-expression matrix using hub genes as features.

#'

#' @param seurat_obj A Seurat object

#' @param n_hubs

#' @param exclude_grey

#' @param wgcna_name

#' @param

#' @param wgcna_name The name of the scWGCNA experiment in the seurat_obj@misc slot

#' @param ... Additional parameters supplied to uwot::umap

#' @keywords scRNA-seq

#' @export

#' @examples

#' RunModuleUMAP

RunModuleUMAP <- function(

  seurat_obj,

  n_hubs = 50,

  exclude_grey = TRUE,

  harmonized=TRUE,

  wgcna_name = NULL,

  n_neighbors= 25,

  metric = "cosine",
@@ -1634,8 +1645,7 @@ RunModuleUMAP <- function( 
  # get the TOM

  TOM <- GetTOM(seurat_obj, wgcna_name)



  # get modules, MEs:

  MEs <- GetMEs(seurat_obj, harmonized, wgcna_name)

  # get modules,

  modules <- GetModules(seurat_obj, wgcna_name)

  mods <- levels(modules$module)
@@ -1725,7 +1735,7 @@ RunModuleUMAP <- function( 
#' 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

#' @param wgcna_name The name of the scWGCNA experiment in the seurat_obj@misc slot

#' @keywords scRNA-seq

#' @export

#' @examples

R/plotting.R

+53 −32
Original line number Diff line number Diff line
@@ -414,11 +414,13 @@ ModuleFeaturePlot<- function( 


#' EnrichrBarPlot

#'

#' Makes barplots from Enrichr data

#' Makes barplots from RunEnrhcr output.

#'

#' @param seurat_obj A Seurat object

#' @param dbs List of EnrichR databases

#' @param max_genes Max number of genes to include per module, ranked by kME.

#' @param outdir directory to place output .pdf files

#' @param n_terms the number of terms to plot in each barplot

#' @param plot_size the size of the output .pdf files (width, height)

#' @param logscale logical controlling whether to plot the enrichment on a log scale

#' @param wgcna_name The name of the scWGCNA experiment in the seurat_obj@misc slot

#' @keywords scRNA-seq

#' @export
@@ -427,7 +429,7 @@ ModuleFeaturePlot<- function( 
EnrichrBarPlot <- function(

  seurat_obj, outdir = "enrichr_plots",

  n_terms = 25, plot_size = c(6,15),

  wgcna_name=NULL, logscale=FALSE, ...

  logscale=FALSE, wgcna_name=NULL,  ...

){



  # get data from active assay if wgcna_name is not given
@@ -513,17 +515,20 @@ EnrichrBarPlot <- function( 
#' Makes barplots from Enrichr data

#'

#' @param seurat_obj A Seurat object

#' @param dbs List of EnrichR databases

#' @param max_genes Max number of genes to include per module, ranked by kME.

#' @param database name of the enrichr database to plot.

#' @param mods names of modules to plot. All modules are plotted if mods='all' (default)

#' @param n_terms number of enriched terms to plot for each module

#' @param break_ties logical controlling whether or not to randomly select terms with equal enrichments to precisely enforce n_terms.

#' @param logscale logical controlling whether to plot the enrichment on a log scale.

#' @param wgcna_name The name of the scWGCNA experiment in the seurat_obj@misc slot

#' @keywords scRNA-seq

#' @export

#' @examples

#' EnrichrBarPlot

#' EnrichrDotPlot

EnrichrDotPlot <- function(

  seurat_obj, database, mods="all", outdir = "enrichr_plots",

  seurat_obj, database, mods="all",

  n_terms = 3, break_ties=TRUE,

  wgcna_name=NULL, logscale=TRUE, ...

   logscale=TRUE, wgcna_name=NULL, ...

){



  # get data from active assay if wgcna_name is not given
@@ -758,7 +763,7 @@ HubGeneNetworkPlot <- function( 
  return_graph=FALSE,

  edge.alpha=0.25,

  vertex.label.cex=0.5,

  hub.vertex.size=4,Ω

  hub.vertex.size=4,

  other.vertex.size=1,

  wgcna_name=NULL,

  ...
@@ -899,11 +904,13 @@ HubGeneNetworkPlot <- function( 
#' Makes a igraph network plot using the module UMAP

#'

#' @param seurat_obj A Seurat object

#' @param

#' @param

#' @param

#' @param

#' @param

#' @param sample_edges logical determining whether we downsample edges for plotting (TRUE), or take the strongst edges.

#' @param edge_prop proportion of edges to plot. If sample_edges=FALSE, the strongest edges are selected.

#' @param label_hubs the number of hub genes to label in each module

#' @param edge.alpha scaling factor for edge opacity

#' @param vertex.label.cex font size for labeled genes

#' @param return_graph logical determining whether to plot thr graph (FALSE) or return the igraph object (TRUE)

#' @param wgcna_name The name of the scWGCNA experiment in the seurat_obj@misc slot

#' @keywords scRNA-seq

#' @export

#' @examples
@@ -915,8 +922,6 @@ ModuleUMAPPlot <- function( 
  label_hubs = 5, # how many hub genes to label?

  edge.alpha=0.25,

  vertex.label.cex=0.5,

  hub.vertex.size=4,

  other.vertex.size=1,

  return_graph = FALSE, # this returns the igraph object instead of plotting

  wgcna_name=NULL,

  ...
@@ -1060,17 +1065,21 @@ ModuleUMAPPlot <- function( 
#'

#' Makes barplots from Enrichr data

#'

#' @param seurat_obj A Seurat object

#' @param dbs List of EnrichR databases

#' @param max_genes Max number of genes to include per module, ranked by kME.

#' @param wgcna_name The name of the scWGCNA experiment in the seurat_obj@misc slot

#' @param overlap_df the Module/DEG overlap table from OverlapModulesDEGs

#' @param plot_var the name of the overlap statistic to plot

#' @param logscale logical controlling whether to plot the result on a log scale, useful for odds ratio

#' @param neglog logical controlling wehether to plot the result as a negative log, useful for p-value / FDR

#' @param plot_significance logical controlling whether to plot the significance levels on top of the dots

#' @keywords scRNA-seq

#' @export

#' @examples

#' OverlapDotPlot

OverlapDotPlot <- function(

  overlap_df, plot_var = 'odds_ratio',

  logscale=TRUE, neglog=FALSE, plot_significance=TRUE, ...

  logscale=TRUE,

  neglog=FALSE,

  plot_significance=TRUE,

  ...

){



  label <- plot_var
@@ -1106,19 +1115,25 @@ OverlapDotPlot <- function( 
  p

}



#' OverlapBarPlot

#'

#' Plots the results from OverlapModulesDEGs as a bar plot

#'

#' @param seurat_obj A Seurat object

#' @param dbs List of EnrichR databases

#' @param max_genes Max number of genes to include per module, ranked by kME.

#' @param wgcna_name The name of the scWGCNA experiment in the seurat_obj@misc slot

#' @param overlap_df the Module/DEG overlap table from OverlapModulesDEGs

#' @param plot_var the name of the overlap statistic to plot

#' @param logscale logical controlling whether to plot the result on a log scale, useful for odds ratio

#' @param neglog logical controlling wehether to plot the result as a negative log, useful for p-value / FDR

#' @param label_size the size of the module labels in the bar plot

#' @keywords scRNA-seq

#' @export

#' @examples

#' OverlapBarPlot

OverlapBarPlot <- function(

  overlap_df, plot_var = 'odds_ratio',

  logscale=TRUE, neglog=FALSE, ...

  overlap_df,

  plot_var = 'odds_ratio',

  logscale=FALSE, neglog=FALSE,

  label_size=2,

  ...

){



  label <- plot_var
@@ -1135,9 +1150,9 @@ OverlapBarPlot <- function( 
    yint = log(yint)

  }

  if(neglog){

    overlap_df[[plot_var]] <- -1 * overlap_df[[plot_var]]

    label <- paste0('-', label)

    yint = -1 * yint

    overlap_df[[plot_var]] <- -1 * log(overlap_df[[plot_var]])

    label <- paste0('-log(', label, ')')

    yint = -1 * log(yint)

  }



  groups <- overlap_df$group %>% as.character %>% unique
@@ -1165,6 +1180,12 @@ OverlapBarPlot <- function( 
        p <- p + geom_hline(yintercept=yint, linetype='dashed', color='gray')

      }



      # add the labels:

      p <- p +

        geom_text(

          aes(label=module, x=module, y=get(plot_var)), color='black', size=label_size, hjust='inward'

        )



      plot_list[[cur_group]] <- p

  }

_pkgdown.yml

+16 −4
Original line number Diff line number Diff line
@@ -40,19 +40,31 @@ navbar: 
    - text: All vignettes

      href: articles/index.html







reference:

- title: Metacells

  desc: Functions for constructing metacells from single-cell data

  contents:

  - '`ConstructMetacells`'

  - '`MetacellsByGroups`'

- title: Plotting

  desc: Functions for generating plots with scWGCNA

- title: Network Visualization

  desc: Functions for visualizing the scWGCNA co-expression network

  contents:

    - '`ModuleNetworkPlot`'

    - '`HubGeneNetworkPlot`'

    - '`RunModuleUMAP`'

    - '`ModuleUMAPPlot`'

- title: Enrichment Analysis

  desc: Functions for Enrichr analysis and DEG overlap analysis

  contents:

    - '`RunEnrichr`'

    - '`EnrichrBarPlot`'

    - '`EnrichrDotPlot`'

    - '`OverlapModulesDEGs`'

    - '`OverlapBarPlot`'

    - '`OverlapDotPlot`'

- title: Plotting

  desc: Functions for generating plots with scWGCNA

  contents:

  - '`ModuleFeaturePlot`'

- title: Seurat wrappers

  desc: Wrapper functions to run Seurat commands on the metacell data

docs/articles/enrichment_analysis.html

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