estimateDispersions {DEXSeq} | R Documentation |
This function estimates the for each counting bin
of the ExonCountSet object a dispersion value. It stores
these values in fData(ecs)$dispersionBeforeSharing
.
## S4 method for signature 'ExonCountSet' estimateDispersions( object, formula=count ~ sample + condition * exon, initialGuess=.01, nCores=1, minCount=10, maxExon=70, quiet=FALSE, file="")
object |
An ExonCountSet object. |
formula |
Formula used in the GLM to estimate the dispersion values.
The terms in the formula must be design
columns of the ExonCountSet object, the l.h.s. must be |
initialGuess |
An initial guess for the dispersion values to initiate the optimization. |
nCores |
Number of cores to be used to estimate the dispersions.
The |
minCount |
Counting bins with less than 'minCount' counts (summed over all samples) are skipped in the tests. This reduced computation time, as counting bins with very few countsare unlikely to give a significant signal anyway. For skipped counting bins, the 'testable' column in fData is set to FALSE. |
maxExon |
Genes with more than 'maxExon' counting bins will be skipped in the test. This may be necessary because genes with very many counting bins may take extremely long in dispersion estimation and testing for differential exon usage. |
quiet |
If TRUE, no progress report is shown. In case the session is not an interactive session and progress report is wanted, include a file name in the parameter "file". |
file |
A file name to write the progress reports. If file="", output will be written in the standart output connection. |
For the dispersion estimation, we use the Cox-Reid conditional maximum
likelihood method of Gordon Smyth et al., which they devised for the edgeR
package.
An ExonCountSet object with dispersion featureData(object)$dispersion_CR_est
)
parameters filled).
## Not run: data("pasillaExons", package="pasilla") pasillaExons <- estimateSizeFactors( pasillaExons ) pasillaExons <- estimateDispersions( pasillaExons ) ## End(Not run)