ModuleEigengenes: ModuleEigengenes
In smorabit/hdWGCNA: hdWGCNA

ModuleEigengenes

R Documentation

ModuleEigengenes

Description

Computes module eigengenes for co-expression modules

Usage

ModuleEigengenes(
  seurat_obj,
  group.by.vars = NULL,
  modules = NULL,
  vars.to.regress = NULL,
  scale.model.use = "linear",
  verbose = TRUE,
  assay = NULL,
  pc_dim = 1,
  wgcna_name = NULL,
  ...
)

Arguments

`seurat_obj`	A Seurat object
`group.by.vars`	groups to harmonize by
`modules`	table containing module / gene assignments, as in GetModules(seurat_obj).
`vars.to.regress`	character vector of variables in seurat_obj@meta.data to regress when running ScaleData
`scale.model.use`	model to scale data when running ScaleData choices are "linear", "poisson", or "negbinom"
`verbose`	logical indicating whether to print messages
`assay`	Assay in seurat_obj to compute module eigengenes. Default is DefaultAssay(seurat_obj)
`pc_dim`	Which PC to use as the module eigengene? Default to 1.
`wgcna_name`	name of the WGCNA experiment

Details

ModuleEigengenes summarizes the gene expression signatures of entire co-expression modules. This is done by performing singular value decomposition (SVD) on a subset of the scaled expression matrix containing only features assigned to each module. The module eigengene (ME), defined as the first dimension of the SVD matrix, retains the most variation, and we use this vector as a summary of gene expression for the whole module.

The module gene expression matrix is first scaled using the Seurat ScaleData function. The user can optionally adjust for covariates of interest in this step using the vars.to.regress parameter. Additionally, the module eigengenes themselves can be adjusted for technical biases such as sequencing batch, dataset of origin, or other factors using the Harmony algorithm with the group.by.vars parameter.