dispCoxReidInterpolateTagwise(y, design, offset=NULL, dispersion, trend=TRUE, AveLogCPM=NULL, min.row.sum=5, prior.df=10, span=0.3, grid.npts=11, grid.range=c(-6,6), weights=NULL)
adjustedProfileLik
the offset
must be a matrix with the same dimension as the table of counts.getPriorN(object)
gives a value for prior.n
that is equivalent to giving the common likelihood 20 prior degrees of freedom in the estimation of the genewise dispersion.log2(dispersion)
, on either side of trendline for each gene for spline grid points.edgeR
context, dispCoxReidInterpolateTagwise
is a low-level function called by estimateGLMTagwiseDisp
.dispCoxReidInterpolateTagwise
calls the function maximizeInterpolant
to fit cubic spline interpolation over a genewise grid.
Note that the terms `tag' and `gene' are synonymous here. The function is only named `Tagwise' for historical reasons.
McCarthy, DJ, Chen, Y, Smyth, GK (2012). Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research 40, 4288-4297. http://nar.oxfordjournals.org/content/40/10/4288
estimateGLMTagwiseDisp
, maximizeInterpolant
y <- matrix(rnbinom(1000, mu=10, size=2), ncol=4)
design <- matrix(1, 4, 1)
dispersion <- 0.5
d <- dispCoxReidInterpolateTagwise(y, design, dispersion=dispersion)
d
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