Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/geneset-DGEList.R
Romer gene set enrichment tests for Negative Binomial generalized linear models.
1 2 |
y |
|
index |
list of indices specifying the rows of |
design |
design matrix. Defaults to |
contrast |
contrast for which the test is required. Can be an integer specifying a column of |
... |
other arguments are passed to |
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | 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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