sup.moa: Projecting supplementary tables on object of class...
In mogsa: Multiple omics data integrative clustering and gene set analysis

Description Usage Arguments Details Value Author(s) References Examples

Projecting supplementary tables on moa-class

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

`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.

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

An object of class moa.sup-class.

Chen Meng

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.

    # 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"))