module_ProNet {miRSM} | R Documentation |
Identification of gene modules from matched ceRNA and mRNA expression data using ProNet package
module_ProNet(ceRExp, mRExp, cor.method = "pearson", pos.p.value.cutoff = 0.01, cluster.method = "MCL", num.ModuleceRs = 2, num.ModulemRs = 2)
ceRExp |
A SummarizedExperiment object. ceRNA expression data: rows are samples and columns are ceRNAs. |
mRExp |
A SummarizedExperiment object. mRNA expression data: rows are samples and columns are mRNAs. |
cor.method |
The method of calculating correlation selected, including 'pearson' (default), 'kendall', 'spearman'. |
pos.p.value.cutoff |
The significant p-value cutoff of positive correlation |
cluster.method |
The clustering method selected in ProNet package, including 'FN', 'MCL' (default), 'LINKCOMM', 'MCODE'. |
num.ModuleceRs |
The minimum number of ceRNAs in each module. |
num.ModulemRs |
The minimum number of mRNAs in each module. |
GeneSetCollection object: a list of module genes.
Junpeng Zhang (https://www.researchgate.net/profile/Junpeng_Zhang3)
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data(BRCASampleData) modulegenes_ProNet <- module_ProNet(ceRExp[, seq_len(10)], mRExp[, seq_len(10)])