Generate a .Rmd file containing code to perform differential expression analysis with voom+limma

Source: R/generateRmdCodeDiffExp.R

A function to generate code that can be run to perform differential expression analysis of RNAseq data (comparing two conditions) by applying the voom transformation (from the limma package) followed by differential expression analysis with 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.

voom.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 of the edgeR package. Possible values are "TMM", "RLE", "upperquartile" and "none"

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.

Details

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

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

Law CW, Chen Y, Shi W and Smyth GK (2014): voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology 15, R29

Author

Charlotte Soneson

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 = "voom.limma",
           Rmdfunction = "voom.limma.createRmd",
           output.directory = tmpdir, norm.method = "TMM")
#> 
#> 
#> processing file: /private/var/folders/xz/lz5thm6s3vb1vdkk77vmkgbr0000gn/T/RtmpDwlx4a/tempcode21d0368f7e21.Rmd
#> 1/2                  
#> 2/2 [unnamed-chunk-1]
#> output file: /private/var/folders/xz/lz5thm6s3vb1vdkk77vmkgbr0000gn/T/RtmpDwlx4a/tempcode21d0368f7e21.md
#> [1] TRUE