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.