estimateTagwiseDisp {edgeR}R Documentation

Estimate Empirical Bayes Tagwise Dispersion Values

Description

Estimates tagwise dispersion values by an empirical Bayes method based on weighted conditional maximum likelihood.

Usage

estimateTagwiseDisp(object, prior.df=10, trend="movingave", span=NULL, method="grid",
          grid.length=11, grid.range=c(-6,6), tol=1e-06, verbose=FALSE)

Arguments

object

object of class DGEList containing (at least) the elements counts (table of raw counts), group (factor indicating group), lib.size (numeric vector of library sizes) and pseudo.alt (numeric matrix of quantile-adjusted pseudocounts calculated under the alternative hypothesis of a true difference between groups; recommended to use the DGEList object provided as the output of estimateCommonDisp

prior.df

prior degrees of freedom.

trend

method for estimating dispersion trend. Possible values are "none", "movingave" and "loess".

span

width of the smoothing window, as a proportion of the data set.

method

method for maximizing the posterior likelihood. Possible values are "grid" for interpolation on grid points or "optimize" to call the function of the same name.

grid.length

for method="grid", the number of points on which the interpolation is applied for each tag.

grid.range

for method="grid", the range of the grid points around the trend on a log2 scale.

tol

for method="optimize", the tolerance for Newton-Rhapson iterations.

verbose

logical, if TRUE then diagnostic ouput is produced during the estimation process.

Details

This function implements the empirical Bayes strategy proposed by Robinson and Smyth (2007) for estimating the tagwise negative binomial dispersions. The experimental design is assumed to be a oneway layout with one or more experimental groups. The empirical Bayes posterior is implemented as a conditional likelihood with tag-specific weights.

The prior values for the dispersions are determined by a global trend. The individual tagwise dispersions are then squeezed towards this trend. The prior degrees of freedom determines the weight given to the prior. The larger the prior degrees of freedom, the more the tagwise dispersions are squeezed towards the global trend. If the number of libraries is large, the prior becomes less important and the tagwise dispersion are determined more by the individual tagwise data.

If trend="none", then the prior dispersion is just a constant, the common dispersion. Otherwise, the trend is determined by a moving average (trend="movingave") or loess smoother applied to the tagwise conditional log-likelihood. method="loess" applies a loess curve of degree 0 as implemented in loessByCol.

method="optimize" is not recommended for routine use as it is very slow. It is included for testing purposes.

Value

An object of class DGEList with the same components as for estimateCommonDisp plus the following:

prior.n

estimate of the prior weight, i.e. the smoothing parameter that indicates the weight to put on the common likelihood compared to the individual tag's likelihood; prior.n of 10 means that the common likelihood is given 10 times the weight of the individual tag/gene's likelihood in the estimation of the tag/genewise dispersion

tagwise.dispersion

tag- or gene-wise estimates of the dispersion parameter

Author(s)

Mark Robinson, Davis McCarthy, Yunshun Chen and Gordon Smyth

References

Robinson, MD, and Smyth, GK (2007). Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 23, 2881-2887. http://bioinformatics.oxfordjournals.org/content/23/21/2881

See Also

estimateCommonDisp is usually run before estimateTagwiseDisp.

movingAverageByCol and loessByCol implement the moving average or loess smoothers.

Examples

# See exactTest
[Package edgeR version 3.4.2 Index]