Generate a .Rmd file containing code to perform differential expression analysis with limma after square root-transforming the counts per million (cpm)
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
sqrtcpm.limma.createRmd(data.path, result.path, codefile, norm.method)
The path to a .rds file containing the
The path to the file where the result object will be saved.
The path to the file where the code will be written.
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
For more information about the methods and the interpretation of the parameters, see the
limma packages and the corresponding publications.
The function generates a
.Rmd file containing the code for performing the differential expression analysis. This file can be executed using e.g. the
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
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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")
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