sup.moa: Projecting supplementary tables on object of class...

In mengchen18/mogsa: Multiple omics data integrative clustering and gene set analysis

Description

Projecting supplementary tables on moa-class

Usage

sup.moa(
  X,
  sup,
  nf = 2,
  factors = NULL,
  ks.stat = FALSE,
  ks.B = 1000,
  ks.cores = NULL,
  p.adjust.method = "fdr"
)

Arguments

X	An object of class moa-class
sup	A list of data.frames contains supplementary data.
nf	The number of principal components used in the projection.
factors	The index of principal components used in the projection, used when non-consecutive PC to be included in the analysis.
ks.stat	The logical indicates if the p-value should be calculated using K-S statistic (the method used in "ssgsea" in GSVA package). Default is FALSE, which means using the z-score method.
ks.B	An integer to indicate the number of bootstrapping samples to calculated the p-value of KS statistic.
ks.cores	An integer indicate the number of cores to be used in bootstrapping. It is passed to function mclapply in the parallel package.
p.adjust.method	The method of p value adjustment, passed to p.adjust function.

Details

Projecting supplementary tables on moa-class, for details see reference.

Value

An object of class moa.sup-class.

Author(s)

Chen Meng

References

Herve Abdi, Lynne J. Williams, Domininique Valentin and Mohammed Bennani-Dosse. STATIS and DISTATIS: optimum multitable principal component analysis and three way metric multidimensional scaling. WIREs Comput Stat 2012. Volume 4, Issue 2, pages 124-167 Haenzelmann, S., Castelo, R. and Guinney, J. GSVA: Gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics, 14:7, 2013. Barbie, D.A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature, 462(5):108-112, 2009.

Examples

    # library(mogsa)
    # loading gene expression data and supplementary data
    data(NCI60_4array_supdata)
    data(NCI60_4arrays)
    # check the dimension of each supplementary data to see how many gene set annotated the data
    sapply(NCI60_4array_supdata, dim)
    # run analysis
    ana <- moa(NCI60_4arrays, proc.row = "center_ssq1", w.data = "inertia", statis = TRUE)
    plot(ana, value="eig")
    # projectin supplementary data
    smoa <- sup.moa(ana, sup=NCI60_4array_supdata, nf=3)
    # heatmap visualize the gene set scores
    heatmap(slot(smoa, "score"))

mengchen18/mogsa documentation built on June 7, 2020, 6:05 p.m.