Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/genesetDGEList.R
Rotation gene set testing 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 passed to 
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 RNASeq data.
The negative binomial count data is converted to approximate normal deviates by computing midp 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)
.
Numeric matrix giving pvalues 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 pvalues 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, 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, 236244. http://www.statsci.org/smyth/pubs/residual.html
Routledge, RD (1994). Practicing safe statistics with the midp. Canadian Journal of Statistics 22, 103110.
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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