roast.DGEList {edgeR} | R Documentation |
Rotation gene set testing for Negative Binomial generalized linear models.
## S3 method for class 'DGEList' roast(y, index = NULL, design = NULL, contrast = ncol(design), geneid = NULL, set.statistic = "mean", gene.weights = NULL, ...) ## S3 method for class 'DGEList' mroast(y, index = NULL, design = NULL, contrast = ncol(design), geneid = NULL, set.statistic = "mean", gene.weights = NULL, adjust.method = "BH", midp = TRUE, sort = "directional", ...) ## S3 method for class 'DGEList' fry(y, index = NULL, design = NULL, contrast = ncol(design), geneid = NULL, 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 |
adjust.method |
method used to adjust the p-values for multiple testing. See |
midp |
logical, should mid-p-values be used in instead of ordinary p-values when adjusting for multiple testing? |
sort |
character, whether to sort output table by directional p-value ( |
... |
other arguments are currently ignored. |
The roast gene set test was proposed by Wu et al (2010) for microarray data.
This function makes the roast test available for digital gene expression 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.
See roast
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)
.
mroast
performs roast
tests for a multiple of gene sets.
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, 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.
Wu, D, Lim, E, Francois Vaillant, F, Asselin-Labat, M-L, Visvader, JE, and Smyth, GK (2010). ROAST: rotation gene set tests for complex microarray experiments. Bioinformatics 26, 2176-2182. http://bioinformatics.oxfordjournals.org/content/26/17/2176
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)