Rotation Gene Set Tests for Digital Gene Expression Data

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Description

Rotation gene set testing for Negative Binomial generalized linear models.

Usage

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## S3 method for class 'DGEList'
romer(y, index, design=NULL, contrast=ncol(design), ...)

Arguments

y

DGEList object.

index

list of indices specifying the rows of y in the gene sets. The list can be made using ids2indices.

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 passed to romer.default.

Details

The ROMER procedure described by Majewski et al (2010) is implemented in romer in the limma package. This function makes the romer test available for digital gene expression data such as RNA-Seq 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 romer 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).

Value

Numeric matrix giving p-values and the number of matched genes in each gene set. Rows correspond to gene sets. There are four columns giving the number of genes in the set and p-values for the alternative hypotheses up, down or mixed. See romer for details.

Author(s)

Yunshun Chen and Gordon Smyth

References

Majewski, IJ, Ritchie, ME, Phipson, B, Corbin, J, Pakusch, M, Ebert, A, Busslinger, M, Koseki, H, Hu, Y, Smyth, GK, Alexander, WS, Hilton, DJ, and Blewitt, ME (2010). Opposing roles of polycomb repressive complexes in hematopoietic stem and progenitor cells. Blood, published online 5 May 2010. http://www.ncbi.nlm.nih.gov/pubmed/20445021

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.

See Also

romer

Examples

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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]+20

# 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)

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

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