mogsa (version 1.6.4)

mogsa: multiple omics data integration and gene set analysis

Description

The main function called by users, omics data analysis and gene set annotation. A wrapper function of moa and sup.moa.

Usage

mogsa(x, sup, nf=NULL, proc.row=NULL, w.data=NULL, w.row=NULL, statis=FALSE, ks.stat=FALSE, ks.B = 1000, ks.cores = NULL)

Arguments

x

An object of class list or moa-class. A list would be a list of data frame.

sup

An object of class list or moa.sup-class. A list would be a list of supplementary data.

nf

The number of principal components used to reconstruct, only used when x is a an object of list.

proc.row

Preprocessing of rows. If x is a object of list, it is passed moa

w.data

Weights of datasets. If x is a object of list, it is passed moa

w.row

Weight of row. If x is a object of list, it is passed moa

statis

A logical indicates if statis algrithm should be used. If x is a object of list, it is passed moa

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. See sup.moa.

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.

Value

An object of class mgsa-class.

Details

A wrapper function of moa and sup.moa.

References

Preprint: Meng, C., Kuster, B., Peters, B., Culhane, AC., Moghaddas Gholami, A., moGSA: integrative single sample gene-set analysis of multiple omics data. doi: http://dx.doi.org/10.1101/046904 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.

See Also

moa and sup.moa

Examples

  # library(mogsa)
  # loading gene expression data and supplementary data
  data(NCI60_4array_supdata)
  data(NCI60_4arrays)

  # using a list of data.frame as input
  mgsa1 <- mogsa(x = NCI60_4arrays, sup=NCI60_4array_supdata, nf=9,
                 proc.row = "center_ssq1", w.data = "inertia", statis = TRUE)
  # using moa as input
  ana <- moa(NCI60_4arrays, proc.row = "center_ssq1", w.data = "inertia", statis = TRUE)
  smoa <- sup.moa(ana, sup=NCI60_4array_supdata, nf=3)
  mgsa2 <- mogsa(x = ana, sup=NCI60_4array_supdata, nf=9)
  mgsa3 <- mogsa(x = ana, sup=smoa)