RunICAnet: Using independent components to construct weighted molecular...
In WWXkenmo/ICAnet: What the Package Does (One Line, Title Case)

RunICAnet

R Documentation

Using independent components to construct weighted molecular network to detect function module for single cell clustering

Description

ICAnet used independent components to construct weighted PPI, and running walk-trap algorthm to detect module on it. The resulted modules are used for the following analysis

Usage

RunICAnet(
  obj,
  ica.filter,
  W.top = 2.5,
  PPI.net = NULL,
  species = 9606,
  score = 600,
  max.step = 10,
  small.size = 3,
  nMC = 100,
  aucMaxRank = 3000,
  cores = 6,
  ModuleSignificance = TRUE,
  scale = TRUE
)

Arguments

`obj`	a Seurat object
`ica.filter`	the filtered/unfiltered ica-components set
`W.top`	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)
`PPI.net`	a matrix object which indicating the PPI network, a boolean network is required
`max.step`	Integer number. The maximum step run in the walk-trapped based community detect.
`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
`nMC`	the number of permutations, which is used for calculate the pvalue of each module (default: 100)
`aucMaxRank`	Integer number. The number of highly-expressed genes to include when computing AUCell
`cores`	the number of cores used for computation
`ModuleSignificance`	the boolean variable to indicate whether perform module significant test (default: FALSE)
`scale`	the boolean variable to indicate whether perform scaling over each batch of scRNA-seq gene expression data