edge (version 2.4.2)

roast.DGEList: Rotation Gene Set Tests for Digital Gene Expression Data

Description

Rotation gene set testing for Negative Binomial generalized linear models.

Usage

## S3 method for class 'DGEList':
roast(y, index=NULL, design=NULL, contrast=ncol(design), ...)
## S3 method for class 'DGEList':
mroast(y, index=NULL, design=NULL, contrast=ncol(design), ...)
## S3 method for class 'DGEList':
fry(y, index=NULL, design=NULL, contrast=ncol(design), ...)

Arguments

y

DGEList object.

index

index vector specifying which rows (genes) of y are in the test set. This can be a vector of indices, or a logical vector of the same length as statistics, or any vector such as y[iset,] contains the values for the gene set to be tested. Defaults to all genes. For mroast a list of index vectors.

design

design matrix

contrast

contrast for which the test is required. Can be an integer specifying a column of design, or the name of a column of design, or else a contrast vector of length equal to the number of columns of design.

...

other arguments are passed to roast.default or mroast.default.

Value

• roast produces an object of class Roast. See roast for details. mroast and fry produce a data.frame. See mroast for details.

Details

The roast gene set test was proposed by Wu et al (2010) for microarray data. This function makes the roast test available for digital gene expression data. The negative binomial count data is converted to approximate normal deviates by computing mid-p quantile residuals (Dunn and Smyth, 1996; Routledge, 1994) under the null hypothesis that the contrast is zero. See roast for more description of the test and for a complete list of possible arguments. The design matrix defaults to the model.matrix(~y$samples$group). mroast performs roast tests for a multiple of gene sets.

References

Dunn, PK, and Smyth, GK (1996). Randomized quantile residuals. J. Comput. Graph. Statist., 5, 236-244. http://www.statsci.org/smyth/pubs/residual.html Routledge, RD (1994). Practicing safe statistics with the mid-p. Canadian Journal of Statistics 22, 103-110. Wu, D, Lim, E, Francois Vaillant, F, Asselin-Labat, M-L, Visvader, JE, and Smyth, GK (2010). ROAST: rotation gene set tests for complex microarray experiments. Bioinformatics 26, 2176-2182. http://bioinformatics.oxfordjournals.org/content/26/17/2176

See Also

roast, camera.DGEList

Examples

mu <- matrix(10, 100, 4)
group <- factor(c(0,0,1,1))
design <- model.matrix(~group)

# First set of 10 genes that are genuinely differentially expressed
iset1 <- 1:10
mu[iset1,3:4] <- mu[iset1,3:4]+10

# Second set of 10 genes are not DE
iset2 <- 11:20

# Generate counts and create a DGEList object
y <- matrix(rnbinom(100*4, mu=mu, size=10),100,4)
y <- DGEList(counts=y, group=group)

# Estimate dispersions
y <- estimateDisp(y, design)

roast(y, iset1, design, contrast=2)
mroast(y, iset1, design, contrast=2)
mroast(y, list(set1=iset1, set2=iset2), design, contrast=2)