edge (version 2.4.2)

estimateTagwiseDisp: Estimate Empirical Bayes Tagwise Dispersion Values

Description

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

Usage

## S3 method for class 'DGEList':
estimateTagwiseDisp(y, prior.df=10, trend="movingave", span=NULL, method="grid", 
           grid.length=11, grid.range=c(-6,6), tol=1e-06, verbose=FALSE, ...)
## S3 method for class 'default':
estimateTagwiseDisp(y, group=NULL, lib.size=NULL, dispersion, AveLogCPM=NULL, 
           prior.df=10, trend="movingave", span=NULL, method="grid", grid.length=11, 
           grid.range=c(-6,6), tol=1e-06, verbose=FALSE, ...)

Arguments

y

matrix of counts or a DGEList object.

prior.df

prior degrees of freedom.

trend

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

span

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

method

method for maximizing the posterior likelihood. Possible values are "grid" (default) 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.

group

vector or factor giving the experimental group/condition for each library.

lib.size

numeric vector giving the total count (sequence depth) for each library.

dispersion

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

AveLogCPM

numeric vector giving average log2 counts per million for each tag

...

other arguments that are not currently used.

Value

• estimateTagwiseDisp.DGEList adds the following components to the input DGEList object:
• prior.df
  prior degrees of freedom.
• prior.n
  estimate of the prior weight.
• tagwise.dispersion
  numeric vector of the tagwise dispersion estimates.
• span
  width of the smoothing window, in terms of proportion of the data set.
• estimateTagwiseDisp.default returns a numeric vector of the tagwise dispersion estimates.

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

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

# True dispersion is 1/5=0.2
y <- matrix(rnbinom(250*4,mu=20,size=5),nrow=250,ncol=4)
dge <- DGEList(counts=y,group=c(1,1,2,2))
dge <- estimateCommonDisp(dge)
dge <- estimateTagwiseDisp(dge)