module_ProNet: module_ProNet

In miRSM: Inferring miRNA sponge modules in heterogeneous data

Description

Identification of gene modules from matched ceRNA and mRNA expression data using ProNet package

Usage

module_ProNet(
  ceRExp,
  mRExp,
  cor.method = "pearson",
  pos.p.value.cutoff = 0.01,
  cluster.method = "MCL",
  num.ModuleceRs = 2,
  num.ModulemRs = 2
)

Arguments

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.

Value

GeneSetCollection object: a list of module genes.

Author(s)

Junpeng Zhang (https://www.researchgate.net/profile/Junpeng_Zhang3)

References

Clauset A, Newman ME, Moore C. Finding community structure in very large networks. Phys Rev E Stat Nonlin Soft Matter Phys., 2004, 70(6 Pt 2):066111.

Enright AJ, Van Dongen S, Ouzounis CA. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res., 2002, 30(7):1575-84.

Kalinka AT, Tomancak P. linkcomm: an R package for the generation, visualization, and analysis of link communities in networks of arbitrary size and type. Bioinformatics, 2011, 27(14):2011-2.

Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 2003, 4:2.

Examples

data(BRCASampleData)
modulegenes_ProNet <- module_ProNet(ceRExp[, seq_len(10)],
    mRExp[, seq_len(10)])

miRSM documentation built on April 16, 2021, 6 p.m.