RunICAnetTF: Using independent components to perform TF-target enrichment...
In WWXkenmo/ICAnet: What the Package Does (One Line, Title Case)

RunICAnetTF

R Documentation

Using independent components to perform TF-target enrichment to detect TF-regulon for single cell clustering

Description

ICAnet used independent components to perform TF-target enrichment test, and select significant TF-regulons for the following analysis

Usage

RunICAnetTF(
  obj,
  ica.filter,
  W.top.TFs = 2.5,
  W.top.genes = 2.5,
  Motif_Net = NULL,
  TF_motif_annot = NULL,
  small.size = 3,
  aucMaxRank = 3000,
  cores = 6,
  verbose = FALSE,
  nMC = 100,
  cutoff = 0.01
)

Arguments

`obj`	a Seurat object
`ica.filter`	the filtered/unfiltered ica-components set
`W.top.TFs`	the threshold to determine the activated TFs, the TFs which has absolute attributes value large than threshold*standard derivation from mean are the activated TFs (default: 2.5)
`W.top.genes`	the threshold to determine the activated genes, the genes which has absolute attributes value large than threshold*standard derivation from mean are the activated genes (default: 2.5)
`Motif_Net`	the matrix which indicating the boolean network of motif-target
`TF_motif_annot`	Annotation to human/mouse/fly transcripton factors for the motifs in each motif collection (e.g. mc8nr or mc9nr)
`small.size`	integer number to determine the minimum size of module. The module which has the number of gene member less than this value will be filtered
`aucMaxRank`	Integer number. The number of highly-expressed genes to include when computing AUCell
`cores`	the number of cores used for computation
`verbose`	a boolean variable, whether show the running process (default: FALSE)
`nMC`	the number of permutations, which is used for calculate the pvalue of each module (default: 100)
`cutoff`	the significant level to filter the TF-regulons (default: 0.01)