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romer.DGEList

Rotation Gene Set Enrichment for Digital Gene Expression Data


Description

Romer gene set enrichment tests for Negative Binomial generalized linear models.

Usage

## 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. Defaults to y$design or, failing that, to model.matrix(~y$samples$group).

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 romer.default. For example, the number of rotations nrot can be increased from the default of 9999 to increase the resolution of the p-values.

Details

The ROMER procedure described by Majewski et al (2010) is implemented in romer in the limma package. This romer method for DGEList objects makes the romer procedure available for count 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. The normal deviates are then passed to the romer function in limma. See romer for more description of the test and for a complete list of possible arguments.

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, 116, 731-719. 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

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]+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)

edgeR

Empirical Analysis of Digital Gene Expression Data in R

v3.32.1
GPL (>=2)
Authors
Yunshun Chen, Aaron TL Lun, Davis J McCarthy, Matthew E Ritchie, Belinda Phipson, Yifang Hu, Xiaobei Zhou, Mark D Robinson, Gordon K Smyth
Initial release
2021-01-14

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