View source: R/generateRmdCodeDiffExp.R
DESeq2.createRmd | R Documentation |
.Rmd
file containing code to perform differential expression analysis with DESeq2A function to generate code that can be run to perform differential expression analysis of RNAseq data (comparing two conditions) using the DESeq2 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.
DESeq2.createRmd(
data.path,
result.path,
codefile,
fit.type,
test,
beta.prior = TRUE,
independent.filtering = TRUE,
cooks.cutoff = TRUE,
impute.outliers = TRUE,
nas.as.ones = FALSE
)
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. |
fit.type |
The fitting method used to get the dispersion-mean relationship. Possible values are |
test |
The test to use. Possible values are |
beta.prior |
Whether or not to put a zero-mean normal prior on the non-intercept coefficients. Default is |
independent.filtering |
Whether or not to perform independent filtering of the data. With independent filtering=TRUE, the adjusted p-values for genes not passing the filter threshold are set to NA. |
cooks.cutoff |
The cutoff value for the Cook's distance to consider a value to be an outlier. Set to Inf or FALSE to disable outlier detection. For genes with detected outliers, the p-value and adjusted p-value will be set to NA. |
impute.outliers |
Whether or not the outliers should be replaced by a trimmed mean and the analysis rerun. |
nas.as.ones |
Whether or not adjusted p values that are returned as |
For more information about the methods and the interpretation of the parameters, see the DESeq2
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
Anders S and Huber W (2010): Differential expression analysis for sequence count data. Genome Biology 11:R106
try(
if (require(DESeq2)) {
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 = "DESeq2",
Rmdfunction = "DESeq2.createRmd",
output.directory = tmpdir, fit.type = "parametric",
test = "Wald")
})
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