edgeR (version 3.14.0)

estimateGLMTrendedDisp: Estimate Trended Dispersion for Negative Binomial GLMs

Description

Estimates the abundance-dispersion trend by Cox-Reid approximate profile likelihood.

Usage

"estimateGLMTrendedDisp"(y, design=NULL, method="auto", ...)
"estimateGLMTrendedDisp"(y, design=NULL, offset=NULL, AveLogCPM=NULL, method="auto", weights=NULL, ...)

Arguments

y

a matrix of counts or a DGEList object.)

design

numeric design matrix, as for glmFit.

method

method (low-level function) used to estimated the trended dispersions. Possible values are "auto" (default, switch to "bin.spline" method if the number of genes is great than 200 and "power" method otherwise),"bin.spline", "bin.loess" (which both result in a call to dispBinTrend), "power" (call to dispCoxReidPowerTrend), or "spline" (call to dispCoxReidSplineTrend).

offset

numeric scalar, vector or matrix giving the linear model offsets, as for glmFit.

AveLogCPM

numeric vector giving average log2 counts per million for each gene.

weights

optional numeric matrix giving observation weights

...

other arguments are passed to lower-level functions dispBinTrend, dispCoxReidPowerTrend or dispCoxReidSplineTrend.

Value

When the input object is a DGEList, estimateGLMTrendedDisp produces a DGEList object, which contains the estimates of the trended dispersion parameter for the negative binomial model according to the method applied.When the input object is a numeric matrix, it returns a vector of trended dispersion estimates calculated by one of the lower-level functions dispBinTrend, dispCoxReidPowerTrend and dispCoxReidSplineTrend.

Details

Estimates the dispersion parameter for each gene with a trend that depends on the overall level of expression for that gene. This is done for a DGE dataset for general experimental designs by using Cox-Reid approximate conditional inference for a negative binomial generalized linear model for each gene with the unadjusted counts and design matrix provided.

The function provides an object-orientated interface to lower-level functions.

References

Cox, DR, and Reid, N (1987). Parameter orthogonality and approximate conditional inference. Journal of the Royal Statistical Society Series B 49, 1-39.

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

See Also

dispBinTrend, dispCoxReidPowerTrend and dispCoxReidSplineTrend for details on how the calculations are done.

Examples

ngenes <- 250
nlibs <- 4
y <- matrix(rnbinom(ngenes*nlibs,mu=10,size=10),ngenes,nlibs)
d <- DGEList(counts=y,group=c(1,1,2,2),lib.size=c(1000:1003))
design <- model.matrix(~group, data=d$samples)
disp <- estimateGLMTrendedDisp(d, design, min.n=25, df=3)
plotBCV(disp)