module_GFA {miRSM} | R Documentation |
Identification of gene modules from matched ceRNA and mRNA expression data using GFA package
module_GFA(ceRExp, mRExp, StrengthCut = 0.9, iter.max = 5000, 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. |
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. |
GeneSetCollection object: a list of module genes.
Junpeng Zhang (https://www.researchgate.net/profile/Junpeng_Zhang3)
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.
data(BRCASampleData) modulegenes_GFA <- module_GFA(ceRExp[seq_len(20), seq_len(15)], mRExp[seq_len(20), seq_len(15)], iter.max = 2600)