mogsa: multiple omics data integration and gene set analysis

In mengchen18/mogsa: Multiple omics data integrative clustering 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,
  factors = NULL,
  proc.row = NULL,
  w.data = NULL,
  w.row = NULL,
  statis = FALSE,
  ks.stat = FALSE,
  ks.B = 1000,
  ks.cores = NULL,
  p.adjust.method = "fdr"
)

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.
factors	The index of principal components used in the projection, used when non-consecutive PC to be included in the analysis.
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.
p.adjust.method	The method of p value adjustment, passed to p.adjust function.

Details

A wrapper function of moa and sup.moa.

Value

An object of class mgsa-class.

Note

This function will be changed to a generic function for "S4-style" programming.

Author(s)

Chen Meng

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)
  mgsa1x <- mogsa(x = NCI60_4arrays, sup=NCI60_4array_supdata, factors = c(1,3,6),
                 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)

mengchen18/mogsa documentation built on June 7, 2020, 6:05 p.m.