baySeq.createRmd {compcodeR} | R Documentation |
.Rmd
file containing code to perform differential expression analysis with baySeqA function to generate code that can be run to perform differential expression analysis of RNAseq data (comparing two conditions) using the baySeq
package. 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.
baySeq.createRmd(data.path, result.path, codefile, norm.method, equaldisp, sample.size = 5000, estimation = "QL", pET = "BIC")
data.path |
The path to a .rds file containing the |
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. Possible values are |
equaldisp |
Logical parameter indicating whether or not equal dispersion should be assumed across all conditions. |
sample.size |
The size of the sample used to estimate the priors (default 5000). |
estimation |
The approach used to estimate the priors. Possible values are |
pET |
The method used to re-estimate the priors. Possible values are |
For more information about the methods and the interpretation of the parameters, see the baySeq
package 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 knitr
package.
Charlotte Soneson
Hardcastle TJ (2012): baySeq: Empirical Bayesian analysis of patterns of differential expression in count data. R package
Hardcastle TJ and Kelly KA (2010): baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics 11:422
try( if (require(baySeq)) { 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")) ## Note! In the interest of speed, we set sample.size=10 in this example. ## In a real analysis, much larger sample sizes are recommended (the default is 5000). runDiffExp(data.file = file.path(tmpdir, "mydata.rds"), result.extent = "baySeq", Rmdfunction = "baySeq.createRmd", output.directory = tmpdir, norm.method = "edgeR", equaldisp = TRUE, sample.size = 10) })