mogsa {mogsa} | R Documentation

## 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, 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)

[Package mogsa version 1.26.0 Index]