cpGModule function

Identify cell phenotype activated gene module

Mining activated gene modules in specific cell phenotype.

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.

Returns

A data frame contains four columns:

• Genes: Gene ID.
• AS: Activity score.
• Pvalue: Significant P-value.
• FDR: False discovery rate.

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.

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)