Estimate Common Dispersion for Negative Binomial GLMs
Estimates a common negative binomial dispersion parameter for a DGE dataset with a general experimental design.
## S3 method for class 'DGEList'
estimateGLMCommonDisp(y, design=NULL, method="CoxReid",
subset=10000, verbose=FALSE, ...)
## Default S3 method:
estimateGLMCommonDisp(y, design=NULL, offset=NULL,
method="CoxReid", subset=10000, AveLogCPM=NULL,
verbose=FALSE, weights=NULL,...)y |
object containing read counts, as for |
design |
numeric design matrix, as for |
offset |
numeric vector or matrix of offsets for the log-linear models, as for |
method |
method for estimating the dispersion.
Possible values are |
subset |
maximum number of rows of |
AveLogCPM |
numeric vector giving average log2 counts per million for each gene. |
verbose |
logical, if |
weights |
optional numeric matrix giving observation weights |
... |
other arguments are passed to lower-level functions.
See |
This function calls dispCoxReid, dispPearson or dispDeviance depending on the method specified.
See dispCoxReid for details of the three methods and a discussion of their relative performance.
The default method returns a numeric vector of length 1 containing the estimated common dispersion.
The DGEList method returns the same DGEList y as input but with common.dispersion as an added component.
The output object will also contain a component AveLogCPM if it was not already present in y.
Gordon Smyth, Davis McCarthy, Yunshun Chen
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. https://doi.org/10.1093/nar/gks042
estimateGLMTrendedDisp for trended dispersions or estimateGLMTagwiseDisp for genewise dispersions in the context of a generalized linear model.
estimateCommonDisp for the common dispersion or estimateTagwiseDisp for genewise dispersions in the context of a multiple group experiment (one-way layout).
# True dispersion is 1/size=0.1 y <- matrix(rnbinom(1000,mu=10,size=10),ncol=4) d <- DGEList(counts=y,group=c(1,1,2,2)) design <- model.matrix(~group, data=d$samples) d1 <- estimateGLMCommonDisp(d, design, verbose=TRUE) # Compare with classic CML estimator: d2 <- estimateCommonDisp(d, verbose=TRUE) # See example(glmFit) for a different example
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