module_GFA: module_GFA

In miRSM: Inferring miRNA sponge modules in heterogeneous data

Description

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

Usage

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

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

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