estimateGLMCommonDisp {edgeR} | R Documentation

## Estimate Common Dispersion for Negative Binomial GLMs

### Description

Estimates a common negative binomial dispersion parameter for a DGE dataset with a general experimental design.

### Usage

    ## 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,...)

### Arguments

`y` | object containing read counts, as for `glmFit`.
`design` | numeric design matrix, as for `glmFit`.
`offset` | numeric vector or matrix of offsets for the log-linear models, as for `glmFit`.
`method` | method for estimating the dispersion. Possible values are `"CoxReid"`, `"Pearson"` or `"deviance"`.
`subset` | maximum number of rows of `y` to use in the calculation. Rows used are chosen evenly spaced by AveLogCPM using `systematicSubset`.
`AveLogCPM` | numeric vector giving average log2 counts per million for each gene.
`verbose` | logical, if `TRUE` estimated dispersion and BCV will be printed to standard output.
`weights` | optional numeric matrix giving observation weights
`...` | other arguments are passed to lower-level functions. See `dispCoxReid`, `dispPearson` and `dispDeviance` for details.

### Details

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.

### Value

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`.

### Author(s)

Gordon Smyth, Davis McCarthy, Yunshun Chen

### References

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

### See Also

`dispCoxReid`, `dispPearson`, `dispDeviance`

`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).

### Examples

    #  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

[Package edgeR version 3.24.3 Index]