sup.moa {mogsa} | R Documentation

## Projecting supplementary tables on object of class `moa-class`.

### 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"))

[Package mogsa version 1.26.0 Index]