sqrtcpm.limma.createRmd: Generate a '.Rmd' file containing code to perform...

In compcodeR: RNAseq data simulation, differential expression analysis and performance comparison of differential expression methods

Description

A function to generate code that can be run to perform differential expression analysis of RNAseq data (comparing two conditions) using limma, after preprocessing the counts by computing the counts per million (cpm) and applying a square-root transformation. 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

sqrtcpm.limma.createRmd(data.path, result.path, codefile, norm.method)

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.
norm.method	The between-sample normalization method used to compensate for varying library sizes and composition in the differential expression analysis. The normalization factors are calculated using the calcNormFactors function from the edgeR package. Possible values are "TMM", "RLE", "upperquartile" and "none".

Details

For more information about the methods and the interpretation of the parameters, see the edgeR and limma 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

Robinson MD, McCarthy DJ and Smyth GK (2010): edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-140

Robinson MD and Oshlack A (2010): A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biology 11:R25

Examples

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 = "sqrtcpm.limma", 
           Rmdfunction = "sqrtcpm.limma.createRmd", 
           output.directory = tmpdir, norm.method = "TMM")

compcodeR documentation built on Nov. 10, 2020, 2 a.m.