module_GFA: module_GFA

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 GFA package

Usage

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module_GFA(
  ceRExp,
  mRExp,
  StrengthCut = 0.9,
  iter.max = 5000,
  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.

StrengthCut

Desired minimum strength (absolute value of association with interval [0 1]) for each bicluster.

iter.max

The total number of Gibbs sampling steps (default 1000).

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

Bunte K, Lepp\'aaho E, Saarinen I, Kaski S. Sparse group factor analysis for biclustering of multiple data sources. Bioinformatics. 2016, 32(16):2457-63.

Lepp\'aaho E, Ammad-ud-din M, Kaski S. GFA: exploratory analysis of multiple data sources with group factor analysis. J Mach Learn Res. 2017, 18(39):1-5.

Examples

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data(BRCASampleData)
modulegenes_GFA <- module_GFA(ceRExp[seq_len(20), seq_len(15)],
    mRExp[seq_len(20), seq_len(15)], iter.max = 2600)

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