cpGModule: Identify cell phenotype activated gene module

In scapGNN: Graph Neural Network-Based Framework for Single Cell Active Pathways and Gene Modules Analysis

Identify cell phenotype activated gene module

Description

Mining activated gene modules in specific cell phenotype.

Usage

cpGModule(
  network.data,
  cellset,
  nperm = 100,
  cut.pvalue = 0.01,
  cut.fdr = 0.05,
  parallel.cores = 2,
  rwr.gamma = 0.7,
  normal_dist = TRUE,
  verbose = TRUE
)

Arguments

network.data	Network data constructed by the ConNetGNN function.
cellset	A vector of cell id. The specified cell set, which will be used as the restart set.
nperm	Number of random permutations. Default: 100.
cut.pvalue	The threshold of P-value, and genes below this threshold are regarded as gene modules activated by the cell set. Default: 0.01.
cut.fdr	The threshold of false discovery rate (FDR), and genes below this threshold are regarded as gene modules activated by the cell set. Default: 0.05.
parallel.cores	Number of processors to use when doing the calculations in parallel (default: 2). If parallel.cores=0, then it will use all available core processors unless we set this argument with a smaller number.
rwr.gamma	Restart parameter. Default: 0.7.
normal_dist	Whether to use pnorm to calculate P values. Default: TRUE.Note that if normal_dist is FALSE, we need to increase nperm (we recommend 100).
verbose	Gives information about each step. Default: TRUE.

Details

cpGModule

The cpGModule function takes a user-defined cell set as a restart set to automatically identify activated gene modules. A perturbation analysis was used to calculate a significant P-value for each gene. The Benjamini & Hochberg (BH) method was used to adjust the P-value to obtain the FDR. Genes with a significance level less than the set threshold are considered as cell phenotype activated gene modules.

Value

A data frame contains four columns:

Genes

Gene ID.

AS

Activity score.

Pvalue

Significant P-value.

FDR

False discovery rate.

Examples

require(parallel)
require(stats)

# Load the result of the ConNetGNN function.
data(ConNetGNN_data)
data(Hv_exp)

# Construct the cell set corresponding to 0h.
index<-grep("0h",colnames(Hv_exp))
cellset<-colnames(Hv_exp)[index]
cpGModule_data<-cpGModule(ConNetGNN_data,cellset,nperm=10,parallel.cores=1)

scapGNN documentation built on Aug. 8, 2023, 9:06 a.m.