sup.moa {mogsa} | R Documentation |
moa-class
.
Projecting supplementary tables on moa-class
sup.moa(X, sup, nf = 2, factors = NULL, ks.stat=FALSE, ks.B = 1000, ks.cores = NULL, p.adjust.method = "none")
X |
An object of 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 |
p.adjust.method |
The method of p value adjustment, passed to |
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"))