romer.DGEList: Rotation Gene Set Enrichment for Digital Gene Expression Data
In hiraksarkar/edgeR_fork: Empirical Analysis of Digital Gene Expression Data in R

Description Usage Arguments Details Value Author(s) References See Also Examples

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

1 2	## S3 method for class 'DGEList' romer(y, index, design=NULL, contrast=ncol(design), ...)

`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.

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.

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.

Yunshun Chen and Gordon Smyth

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.

romer

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)