mogsa-package {mogsa} | R Documentation

## Multiple omics 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 <chen.meng@tum.de>

### 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)

[Package mogsa version 1.26.0 Index]