module_ProNet: module_ProNet

Description Usage Arguments Value Author(s) References Examples

View source: R/miRSM.R

Description

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

Usage

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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

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

zhangjunpeng411/miRSM documentation built on Nov. 28, 2020, 7 a.m.