mogsa-package: Multiple omics clustering and gene set analysis

In mengchen18/mogsa: Multiple omics data integrative clustering and gene set analysis

Description

Modern "omics" technologies enable quantitative monitoring of the abundance of various biological molecules in a high-throughput manner, accumulating an unprecedented amount of quantitative information on a genomic scale. Gene set analysis is a particularly useful method in high throughput data analysis since it can summarize single gene level information into the biological informative gene set levels. This package provide a method do the gene set analysis based on multiple omics data that describing the same set of observations/samples.

Details

Package:	mogsa
Type:	Package
Version:	1.3.1
Date:	2016-01-19
License:	GPL-2
Depends:	methods

The main function in the package is "mogsa", see the function help manu for more details.

Author(s)

Chen Meng Maintainer: Chen Meng <[e-mail]>

References

Chen Meng, Dominic Helm, Martin Frejno, and Bernhard Kuster. moCluster: Identifying Joint Patterns Across Multiple Omics Data Sets. Journal of Proteome Research 2016.

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)

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