camera.DGEList: Competitive Gene Set Tests for Digital Gene Expression Data

In OliverVoogd/edgeR: Empirical Analysis of Digital Gene Expression Data in R

Competitive Gene Set Tests for Digital Gene Expression Data

Description

Test whether a set of genes is highly ranked relative to other genes in terms of differential expression, accounting for inter-gene correlation.

Usage

## S3 method for class 'DGEList'
camera(y, index, design, contrast = ncol(design), weights = NULL,
       use.ranks = FALSE, allow.neg.cor=FALSE, inter.gene.cor=0.01, sort = TRUE, ...)

Arguments

y	a DGEList object containing dispersion estimates.
index	an index vector or a list of index vectors. Can be any vector such that y[index,] selects the rows corresponding to the test set. 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 of the linear model coefficients for which the test is required. Can be an integer specifying a column of design, or else a numeric vector of same length as the number of columns of design.
weights	numeric matrix of observation weights of same size as y, or a numeric vector of array weights with length equal to ncol(y), or a numeric vector of gene weights with length equal to nrow(y).
use.ranks	do a rank-based test (TRUE) or a parametric test (FALSE)?
allow.neg.cor	should reduced variance inflation factors be allowed for negative correlations?
inter.gene.cor	numeric, optional preset value for the inter-gene correlation within tested sets. If NA or NULL, then an inter-gene correlation will be estimated for each tested set.
sort	logical, should the results be sorted by p-value?
...	other arguments are not currently used

Details

The camera gene set test was proposed by Wu and Smyth (2012) for microarray data. This function makes the camera 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 camera for more description of the test and for a complete list of possible arguments.

Value

A data.frame giving the gene set results, with most significant sets to the top. See camera for details.

Author(s)

Yunshun Chen, Gordon Smyth

References

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, and Smyth, GK (2012). Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Research 40, e133. doi: 10.1093/nar/gks461

See Also

roast.DGEList, camera.

Examples

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)

camera(y, iset1, design)
camera(y, iset2, design)

camera(y, list(set1=iset1,set2=iset2), design)

OliverVoogd/edgeR documentation built on July 28, 2022, 10:13 p.m.