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

In unistbig/compareDEtools: Comparison of multiple DE analysis methods in R

Description

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 with quantile normalization (from the limma package) followed by differential expression analysis with limma.

Usage

voom.sw.limma.createRmd(data.path, result.path, codefile,
  norm.method = "TMM")

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"

Details

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

BuKyung Baik

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

Liu, R., Holik, A. Z., Su, S., Jansz, N., Chen, K., Leong, H. S., Blewitt, M. E., Asselin-Labat, M.-L., Smyth, G. K., Ritchie, M. E. (2015). Why weight? Combining voom with estimates of sample quality improves power in RNA-seq analyses. Nucleic Acids Research 43, e97.

Examples

tmpdir <- normalizePath(tempdir(), winslash = "/")
SyntheticDataSimulation(simul.data= 'KIRC', dataset = file.path(tmpdir, "mydata.rds"), n.var = 500,
                                    samples.per.cond = 3, n.diffexp = 100, dispType='same', mode='D',
                                    fraction.upregulated=0.5, dataset.parameters=generateDatasetParameter())
compcodeR::runDiffExp(data.file = file.path(tmpdir, "mydata.rds"), result.extent = "voom.sw.limma",
           Rmdfunction = "voom.sw.limma.createRmd",
           output.directory = tmpdir,)

unistbig/compareDEtools documentation built on May 1, 2020, 9:41 p.m.