/GeneCooc

Primary LanguageROtherNOASSERTION

GeneCooc

Project Status: Active - The project has reached a stable, usable state and is being actively developed.

GeneCooc is an ultra-fast R package for gene co-expression module discovery facilitates cell state identification.

Installation

You can install the development version of GeneCooc from GitHub with:

# install.packages("devtools")
# install CelliD (Seurat V4)
devtools::install_github("RausellLab/CelliD", ref="e306b43")
# install GeneCooc
devtools::install_github("JarningGau/GeneCooc")

Note: GeneCooc is compitable with Seurat V4, we did not test the codes on Seurat V5.

Quick start

  1. Calculate the gene to cell distance and rank the genes by this distance for each cell.
seu <- CalGeneRankings(seu, min.expr.cells = 100)
  1. Calculate the gene affinity matrix. The gene affinity is measured by co-occurance ratio.
seu <- CalAffinityMatrix(seu, K = 200, min.freq = 10)
  1. Find modules.

The major modules are divided by louvain cluster on gene-gene coexpression graph defined by gene affinity matrix. Then minor modules are divided using the dynamic tree cut on a hierarchical tree for each major module.

seu <- FindModules(seu)
  1. Trim the minor modules by archytype analysis.
seu <- TrimModules(seu)
  1. Merge the similary minor modules automaticall。
seu <- AutoMergeModules(seu)
  1. Scoring each gene module.
seu <- CalModuleScore(seu)