vst.limma.createRmd {compcodeR} | R Documentation

## Generate a `.Rmd` file containing code to perform differential expression analysis with limma after the variance-stabilizing transformation provided in DESeq

### Description

A function to generate code that can be run to perform differential expression analysis of RNAseq (comparing two conditions) by applying the variance-stabilizing transformation of the `DESeq` package followed by differential expression analysis using `limma`. The code is written to a `.Rmd` file. This function is generally not called by the user, the main interface for performing differential expression analysis is the `runDiffExp` function.

### Usage

    vst.limma.createRmd(data.path, result.path, codefile, fit.type)

### Arguments

`data.path` | The path to a .rds file containing the `compData` object that will be used for the differential expression analysis.
`result.path` | The path to the file where the result object will be saved.
`codefile` | The path to the file where the code will be written.
`fit.type` | The fitting method used to get the dispersion-mean relationship. Possible values are `"parametric"` and `"local"`.

### Details

For more information about the methods and the interpretation of the parameters, see the `limma` and `DESeq` packages and the corresponding publications.

### Value

The function generates a `.Rmd` file containing the code for performing the differential expression analysis. This file can be executed using e.g. the `knitr` package.

### Author(s)

Charlotte Soneson

### References

Smyth GK (2005): Limma: linear models for microarray data. In: 'Bioinformatics and Computational Biology Solutions using R and Bioconductor'. R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds), Springer, New York, pages 397-420

Anders S and Huber W (2010): Differential expression analysis for sequence count data. Genome Biology 11:R106

### Examples

    try(
    if (require(DESeq)) {
    tmpdir <- normalizePath(tempdir(), winslash = "/")
    mydata.obj <- generateSyntheticData(dataset = "mydata", n.vars = 1000,
                                        samples.per.cond = 5, n.diffexp = 100,
                                        output.file = file.path(tmpdir, "mydata.rds"))
    runDiffExp(data.file = file.path(tmpdir, "mydata.rds"), result.extent = "vst.limma",
               Rmdfunction = "vst.limma.createRmd",
               output.directory = tmpdir, fit.type = "parametric")
    })

[Package compcodeR version 1.23.2 Index]