icWGCNA

Overview

Iterative Correcting Weighted Gene Co-expression Network Analysis function for constructing a gene network from a gene expression matrix. The algorithm:

1. Constructs a signed wgcna network
2. Drops correlated modules based on membership kurtosis
3. Regresses out the strongest community from the expression data
4. Repeats steps 1-3 until a maximum number of communities or iterations is reached

Some differences from standard WGNCA (Horvath/Langfelder):

• Makes heavy use of Rfast to compute adjacencies and topological overlap measure (TOM), which enables iterative network creation on > 20K features.
• Always uses signed adjacency in order to avoid possible distortions of community signatures (eigengenes).
• Iteratively regresses out strongest community in order to facilitate discovery of communities possibly obscured by larger module(s).
• Clustering does not focus on merging communities but dropping to identify strongest module(s), keeping communities with higher membership kurtosis.
• In addition to Pearson correlation, there is an option for Spearman correlation as the base constructing adjacency matrix measure in order to enable robust application in RNA-seq and micro-array data. Future updates may include mutual information and other measures of similarity.

Installation

Install the released version of icWGCNA from Github:

remotes::install_github(repo = "Systems-Methods/icWGCNA")

Or install the development version from BMS BioGit with:

remotes::install_github(repo = "Systems-Methods/icWGCNA", 
                        ref = "develop")

Or:

remotes::install_git(
  'https://github.com/Systems-Methods/icWGCNA.git'
)

Example

Example Data

First we can use the {UCSCXenaTools} package to download an example mRNASeq dataset.

luad <- UCSCXenaTools::getTCGAdata(project = "LUAD", 
                                   mRNASeq = TRUE, 
                                   mRNASeqType = "normalized",
                                   clinical = FALSE, 
                                   download = TRUE)

ex <- as.matrix(data.table::fread(luad$destfiles), rownames = 1)

Main Results

Next we can run the main icwgcna() function

library(icWGCNA)

results <- icwgcna(ex,   
                   expo = 6,
                   Method = "pearson",
                   q = 0.3,
                   maxIt = 10,
                   maxComm = 100,
                   corCut = 0.8,
                   covCut = 0.33,
                   mat_mult_method = "Rfast")

Downstream Analysis

Finally, downstream analysis can be run on the Iterative Correcting Weighted Gene Co-expression Network Analysis results.

Compute community signatures (eigengenes)

eigengene_mat <- compute_eigengene_matrix(
  ex, 
  membership_matrix = results$community_membership, 
  cutoff = 5,
  pc_flag = TRUE
)

Compute panglaoDB collection enrichments for each community

pangDB <- data.table::fread(pangDB_link)
panglaoDB_enrichment <- compute_panglaoDB_enrichment(
  results$community_membership,
  K = 100,
  memb_cut = 0.65,
  pangDB = pangDB,
  prolif = prolif_names, 
  p_cut = 0.001
)

Compute MSigDB collection enrichments for each community

MSigDB_enrichment <- compute_MSigDB_enrichment(
  results$community_membership,
  K = 100,
  memb_cut = .65,
  cats = c("H", "C3", "C6", "C7", "C8"), 
  p_cut = 0.001
)

Compute xCell collection enrichments for each community

xCell_enrichment <- compute_xCell_enrichment(
  results$community_membership,
  K = 100,
  memb_cut = .65, 
  p_cut = 0.001
)

Display UMAP of Community Membership with text overlays

network_umap <- make_network_umap(
  results$community_membership,
  community_memb_cut_main = 0.7,
  community_n_main = 20,
  community_memb_cut_secondary = 0.8,
  community_n_secondary = 5,
  gene_memb_cut_main = 0.75,
  gene_memb_cut_secondary = 0.65,
  community_labels = NULL,
  umap_specs = umap::umap.defaults
)

network_umap$umap_w_annotation

Identify Top Genes, with Values, of all Communities

top_genes <- display_top_genes(
  results$community_membership,
  K = 10,
  output = "both"
)

Identify Top Gene of Communities that are unique (only belong to one community)

unique_top_genes <- find_unique_top_genes(
  results$community_membership,
  K = 10,
  maxIt = 10
)

Map icWGCNA Eigengenes on a Seurat Object

unique_top_genes <- map_eigengenes_on_seurat(
  SeuratObject::pbmc_small,
  results$community_membership,
  cutoff_method = "top_gene",
  top_genes_cutoff = 20
)

Code of Conduct

Please note that the icWGCNA project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.