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Last data update: 2014.03.03

R: Projecting supplementary tables on object of class...

sup.moa

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, ks.stat=FALSE, ks.B = 1000, ks.cores = NULL)

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.
`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.

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"))

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(mogsa)
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/mogsa/sup.moa.Rd_%03d_medium.png", width=480, height=480)
> ### Name: sup.moa
> ### Title: Projecting supplementary tables on object of class 'moa-class'.
> ### Aliases: sup.moa
> ### Keywords: data projection supplementary data
> 
> ### ** 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)
     agilent hgu133 hgu133p2 hgu95
[1,]     300    298      268   288
[2,]     150    150      150   150
>     # 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"))
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>

Projecting supplementary tables on object of class moa-class.