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 3 4 5 6 7 8 9 10 11 12  ## S3 method for class 'DGEList'
fry(y, index = NULL, design = NULL, contrast = ncol(design), geneid = NULL,
sort = "directional", ...)
## S3 method for class 'DGEList'
roast(y, index = NULL, design = NULL, contrast = ncol(design), geneid = NULL,
set.statistic = "mean", gene.weights = NULL, nrot = 1999, ...)
## S3 method for class 'DGEList'
mroast(y, index = NULL, design = NULL, contrast = ncol(design), geneid = NULL,
set.statistic = "mean", gene.weights = NULL, nrot = 1999,
adjust.method = "BH", midp = TRUE, sort = "directional", ...)

y 

index 
index vector specifying which rows (probes) of 
design 
the design matrix. Defaults to 
contrast 
contrast for which the test is required.
Can be an integer specifying a column of 
geneid 
gene identifiers corresponding to the rows of 
set.statistic 
summary set statistic. Possibilities are 
gene.weights 
numeric vector of directional (positive or negative) genewise weights.
For 
nrot 
number of rotations used to compute the pvalues. 
adjust.method 
method used to adjust the pvalues for multiple testing. See 
midp 
logical, should midpvalues be used in instead of ordinary pvalues when adjusting for multiple testing? 
sort 
character, whether to sort output table by directional pvalue ( 
... 
other arguments are currently ignored. 
These functions perform selfcontained gene set tests against the null hypothesis that none of the genes in the set are differentially expressed.
fry
is the recommended function in the edgeR context.
The roast gene set test was proposed by Wu et al (2010) for microarray data and the roast
and mroast
methods documented here extend the test to digital gene expression data.
The roast method uses residual space rotations instead of permutations to obtain pvalues, a technique that take advantage of the full generality of linear models.
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, and the normal deviates are then passed to the limma roast
function.
See roast
for more description of the test and for a complete list of possible arguments.
mroast
is similar but performs roast
tests for multiple of gene sets instead of just one.
The fry
method documented here similarly generalizes the fry gene set test for microarray data.
fry
is recommended over roast
or mroast
for count data because, in this context, it is equivalent to mroast
but with an infinite number of rotations.
roast
produces an object of class Roast
. See roast
for details.
mroast
and fry
produce a data.frame. See mroast
for details.
Yunshun Chen and Gordon Smyth
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.
Wu, D, Lim, E, Francois Vaillant, F, AsselinLabat, ML, Visvader, JE, and Smyth, GK (2010). ROAST: rotation gene set tests for complex microarray experiments. Bioinformatics 26, 21762182. http://bioinformatics.oxfordjournals.org/content/26/17/2176
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]+10
# 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)
roast(y, iset1, design, contrast=2)
mroast(y, iset1, design, contrast=2)
mroast(y, list(set1=iset1, set2=iset2), design, contrast=2)

Loading required package: limma
Active.Prop P.Value
Down 0.0 0.9904952
Up 0.3 0.0100050
UpOrDown 0.3 0.0200000
Mixed 0.3 0.0360000
NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
set1 10 0 0.3 Up 0.021 0.021 0.042 0.042
NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
set1 10 0.0 0.3 Up 0.025 0.049 0.028 0.028
set2 10 0.2 0.1 Down 0.642 0.642 0.009 0.017
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