estimateDisp: Estimate Common, Trended and Tagwise Negative Binomial...

In edgeR: Empirical Analysis of Digital Gene Expression Data in R

Description

Maximizes the negative binomial likelihood to give the estimate of the common, trended and tagwise dispersions across all tags.

Usage

## S3 method for class 'DGEList'
estimateDisp(y, design=NULL, prior.df=NULL, trend.method="locfit", tagwise=TRUE,
            span=NULL, min.row.sum=5, grid.length=21, grid.range=c(-10,10), robust=FALSE, 
            winsor.tail.p=c(0.05,0.1), tol=1e-06, ...)
## S3 method for class 'SummarizedExperiment'
estimateDisp(y, design=NULL, prior.df=NULL, trend.method="locfit", tagwise=TRUE,
            span=NULL, min.row.sum=5, grid.length=21, grid.range=c(-10,10), robust=FALSE, 
            winsor.tail.p=c(0.05,0.1), tol=1e-06, ...)
## Default S3 method:
estimateDisp(y, design=NULL, group=NULL, lib.size=NULL, offset=NULL, prior.df=NULL,
            trend.method="locfit", tagwise=TRUE, span=NULL, min.row.sum=5, grid.length=21, 
            grid.range=c(-10,10), robust=FALSE, winsor.tail.p=c(0.05,0.1), tol=1e-06, weights=NULL, ...)

Arguments

y	matrix of counts, or a DGEList object, or a SummarizedExperiment object.
design	numeric design matrix. Defaults to model.matrix(~group) if group is specified and otherwise to a single column of ones.
prior.df	prior degrees of freedom. It is used in calculating prior.n.
trend.method	method for estimating dispersion trend. Possible values are "locfit" (default), "none", "movingave", "loess" and "locfit.mixed", which uses a polynomial of degree 1 for lowly expressed genes.
tagwise	logical, should the tagwise dispersions be estimated?
span	width of the smoothing window, as a proportion of the data set.
min.row.sum	numeric scalar giving a value for the filtering out of low abundance tags. Only tags with total sum of counts above this value are used. Low abundance tags can adversely affect the dispersion estimation, so this argument allows the user to select an appropriate filter threshold for the tag abundance.
grid.length	the number of points on which the interpolation is applied for each tag.
grid.range	the range of the grid points around the trend on a log2 scale.
robust	logical, should the estimation of prior.df be robustified against outliers?
winsor.tail.p	numeric vector of length 1 or 2, giving left and right tail proportions of the deviances to Winsorize when estimating prior.df.
tol	the desired accuracy, passed to optimize
group	vector or factor giving the experimental group/condition for each library. Defaults to a vector of ones with length equal to the number of libraries.
lib.size	numeric vector giving the total count (sequence depth) for each library.
offset	offset matrix for the log-linear model, as for glmFit. Defaults to the log-effective library sizes.
weights	optional numeric matrix giving observation weights
...	other arguments that are not currently used.

Details

This function calculates a matrix of likelihoods for each tag at a set of dispersion grid points, and then applies weighted likelihood empirical Bayes method to obtain posterior dispersion estimates. If there is no design matrix, it calculates the quantile conditional likelihood for each tag and then maximizes it. In this case, it is similar to the function estimateCommonDisp and estimateTagwiseDisp. If a design matrix is given, it calculates the adjusted profile log-likelihood for each tag and then maximizes it. In this case, it is similar to the functions estimateGLMCommonDisp, estimateGLMTrendedDisp and estimateGLMTagwiseDisp.

Note that the terms ‘tag’ and ‘gene’ are synonymous here.

Value

estimateDisp.DGEList adds the following components to the input DGEList object:

design	the design matrix.
common.dispersion	estimate of the common dispersion.
trended.dispersion	estimates of the trended dispersions.
tagwise.dispersion	tagwise estimates of the dispersion parameter if tagwise=TRUE.
AveLogCPM	numeric vector giving log2(AveCPM) for each row of y.
trend.method	method for estimating dispersion trend as given in the input.
prior.df	prior degrees of freedom. It is a vector when robust method is used.
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.
span	width of the smoothing window used in estimating dispersions.

estimateDisp.SummarizedExperiment converts the input SummarizedExperiment object into a DGEList object, and then calls estimateDisp.DGEList. The output is a DGEList object.

estimateDisp.default returns a list containing common.dispersion, trended.dispersion, tagwise.dispersion (if tagwise=TRUE), span, prior.df and prior.n.

Note

The estimateDisp function doesn't give exactly the same estimates as the traditional calling sequences.

Author(s)

Yunshun Chen, Gordon Smyth

References

Chen, Y, Lun, ATL, and Smyth, GK (2014). Differential expression analysis of complex RNA-seq experiments using edgeR. In: Statistical Analysis of Next Generation Sequence Data, Somnath Datta and Daniel S. Nettleton (eds), Springer, New York, pages 51-74. http://www.statsci.org/smyth/pubs/edgeRChapterPreprint.pdf

Phipson, B, Lee, S, Majewski, IJ, Alexander, WS, and Smyth, GK (2016). Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression. Annals of Applied Statistics 10, 946-963. http://projecteuclid.org/euclid.aoas/1469199900

See Also

estimateCommonDisp, estimateTagwiseDisp, estimateGLMCommonDisp, estimateGLMTrendedDisp, estimateGLMTagwiseDisp

Examples

# True dispersion is 1/5=0.2
y <- matrix(rnbinom(1000, mu=10, size=5), ncol=4)
group <- factor(c(1,1,2,2))
design <- model.matrix(~group)
d <- DGEList(counts=y, group=group)
d1 <- estimateDisp(d)
d2 <- estimateDisp(d, design)

Example output

Loading required package: limma
Design matrix not provided. Switch to the classic mode.

edgeR documentation built on Jan. 16, 2021, 2:03 a.m.