View source: R/edgeR-slurm-lsf.R
edgeRcommand | R Documentation |
Export an DGEList, designMatrix, and contrastMatrix to files and return the command to run the edgeR script
edgeRcommand(
dgeList,
designMatrix,
contrastMatrix,
outdir = "edgeR_output",
outfilePrefix = "an-unnamed-project-",
mps = FALSE,
limmaVoom = FALSE,
appendGmt = NULL,
debug = FALSE,
rootPath = "/pstore/apps/bioinfo/geneexpression/",
contrastAnno = NULL
)
dgeList |
An |
designMatrix |
The design matrix to model the data |
contrastMatrix |
The contrast matrix matching the design matrix |
outdir |
Output directory of the edgeR script. Default value "edgeR_output". |
outfilePrefix |
Prefix of the output files, for instance a reasonable
name of the project, to identify the files uniquely. The files will be written in
|
mps |
Logical, whether molecular-phenotyping analysis is run. |
limmaVoom |
Logical, whether the limma-voom model is run instead of the edgeR model |
appendGmt |
|
debug |
Logical, if |
rootPath |
Character, the root path of the script |
contrastAnno |
A |
Following checks are done internally:
The design matrix must have the same number of rows as the columns of the count matrix.
The contrast matrix must have the same number of rows as the columns of the design matrix.
Row names of the design matrix match the column names of the expression matrix. In case of suspect, the program will stop and report.
The output file names start with the outfilePrefix, followed by '-' and customed file suffixes.
mat <- matrix(rnbinom(100, mu=5, size=2), ncol=10)
rownames(mat) <- sprintf("gene%d", 1:nrow(mat))
myFac <- gl(2,5, labels=c("Control", "Treatment"))
y <- edgeR::DGEList(counts=mat, group=myFac)
myDesign <- model.matrix(~myFac); colnames(myDesign) <- levels(myFac)
myContrast <- limma::makeContrasts(Treatment, levels=myDesign)
edgeRcommand(y, designMatrix=myDesign, contrastMatrix=myContrast,
outfilePrefix="test", outdir=tempdir())
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