edge (version 2.4.2)

estimateGLMTagwiseDisp: Empirical Bayes Tagwise Dispersions for Negative Binomial GLMs

Description

Compute an empirical Bayes estimate of the negative binomial dispersion parameter for each tag, with expression levels specified by a log-linear model.

Usage

## S3 method for class 'DGEList':
estimateGLMTagwiseDisp(y, design=NULL, prior.df=10,
            trend=!is.null(y$trended.dispersion), span=NULL, ...)
## S3 method for class 'default':
estimateGLMTagwiseDisp(y, design=NULL, offset=NULL, dispersion,
            prior.df=10, trend=TRUE, span=NULL, AveLogCPM=NULL,
            weights=NULL, \dots)

Arguments

y

matrix of counts or a DGEList object.

design

numeric design matrix, as for glmFit.

trend

logical. Should the prior be the trended dispersion (TRUE) or the common dispersion (FALSE)?

offset

offset matrix for the log-linear model, as for glmFit. Defaults to the log-effective library sizes.

dispersion

common or trended dispersion estimates, used as an initial estimate for the tagwise estimates.

prior.df

prior degrees of freedom.

span

width of the smoothing window, in terms of proportion of the data set. Default value decreases with the number of tags.

AveLogCPM

numeric vector giving average log2 counts per million for each tag

weights

optional numeric matrix giving observation weights

...

other arguments are passed to dispCoxReidInterpolateTagwise.

Value

• estimateGLMTagwiseDisp.DGEList produces a DGEList object, which contains the tagwise dispersion parameter estimate for each tag for the negative binomial model that maximizes the Cox-Reid adjusted profile likelihood. The tagwise dispersions are simply added to the DGEList object provided as the argument to the function.

  estimateGLMTagwiseDisp.default returns a vector of the tagwise dispersion estimates.

Details

This function implements the empirical Bayes strategy proposed by McCarthy et al (2012) for estimating the tagwise negative binomial dispersions. The experimental conditions are specified by design matrix allowing for multiple explanatory factors. The empirical Bayes posterior is implemented as a conditional likelihood with tag-specific weights, and the conditional likelihood is computed using Cox-Reid approximate conditional likelihood (Cox and Reid, 1987).

The prior degrees of freedom determines the weight given to the global dispersion trend. The larger the prior degrees of freedom, the more the tagwise dispersions are squeezed towards the global trend.

Note that the terms `tag' and `gene' are synonymous here. The function is only named `Tagwise' for historical reasons.

This function calls the lower-level function dispCoxReidInterpolateTagwise.

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

estimateGLMCommonDisp for common dispersion or estimateGLMTrendedDisp for trended dispersion in the context of a generalized linear model.

estimateCommonDisp for common dispersion or estimateTagwiseDisp for tagwise dispersions in the context of a multiple group experiment (one-way layout).

Examples

y <- matrix(rnbinom(1000,mu=10,size=10),ncol=4)
d <- DGEList(counts=y,group=c(1,1,2,2),lib.size=c(1000:1003))
design <- model.matrix(~group, data=d$samples) # Define the design matrix for the full model
d <- estimateGLMTrendedDisp(d, design, min.n=10)
d <- estimateGLMTagwiseDisp(d, design)
summary(d$tagwise.dispersion)