cpGModule | R Documentation |
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
)
network.data |
Network data constructed by the |
cellset |
A vector of cell id. The specified cell set, which will be used as the restart set. |
nperm |
Number of random permutations. Default: |
cut.pvalue |
The threshold of P-value, and genes below this threshold are regarded as gene modules activated by the cell set. Default: |
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: |
parallel.cores |
Number of processors to use when doing the calculations in parallel (default: |
rwr.gamma |
Restart parameter. Default: |
normal_dist |
Whether to use pnorm to calculate P values. Default: |
verbose |
Gives information about each step. Default: |
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.
A data frame contains four columns:
Gene ID.
Activity score.
Significant P-value.
False discovery rate.
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)
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