"camera"(y, index, design, contrast = ncol(design), weights = NULL, use.ranks = FALSE, allow.neg.cor=FALSE, inter.gene.cor=0.01, trend.var = FALSE, sort = TRUE, ...) interGeneCorrelation(y, design)
y[index,]selects the rows corresponding to the test set. The list can be made using
design, or else a numeric vector of same length as the number of columns of
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
TRUE) or a parametric test (
NULL, then an inter-gene correlation will be estimated for each tested set.
camerareturns a data.frame with a row for each set and the following columns:
inter.gene.corwas not preset).
interGeneCorrelationreturns a list with components:
interGeneCorrelationimplement methods proposed by Wu and Smyth (2012).
cameraperforms a competitive test in the sense defined by Goeman and Buhlmann (2007). It tests whether the genes in the set are highly ranked in terms of differential expression relative to genes not in the set. It has similar aims to
geneSetTestbut accounts for inter-gene correlation. See
roastfor an analogous self-contained gene set test.
The function can be used for any microarray experiment which can be represented by a linear model.
The design matrix for the experiment is specified as for the
lmFit function, and the contrast of interest is specified as for the
This allows users to focus on differential expression for any coefficient or contrast in a linear model by giving the vector of test statistic values.
camera estimates p-values after adjusting the variance of test statistics by an estimated variance inflation factor.
The inflation factor depends on estimated genewise correlation and the number of genes in the gene set.
interGeneCorrelation to estimate the mean pair-wise correlation within each set of genes.
camera can alternatively be used with a preset correlation specified by
inter.gene.cor that is shared by all sets.
This usually works best with a small value, say
camera will estimate the inter-gene correlation for each set.
In this mode,
camera gives rigorous error rate control for all sample sizes and all gene sets.
However, in this mode, highly co-regulated gene sets that are biological interpretable may not always be ranked at the top of the list.
camera will rank biologically interpetable sets more highly.
This gives a useful compromise between strict error rate control and interpretable gene set rankings.
Goeman, JJ, and Buhlmann, P (2007). Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics 23, 980-987.
There is a topic page on 10.GeneSetTests.
y <- matrix(rnorm(1000*6),1000,6) design <- cbind(Intercept=1,Group=c(0,0,0,1,1,1)) # First set of 20 genes are genuinely differentially expressed index1 <- 1:20 y[index1,4:6] <- y[index1,4:6]+1 # Second set of 20 genes are not DE index2 <- 21:40 camera(y, index1, design) camera(y, index2, design) camera(y, list(set1=index1,set2=index2), design, inter.gene.cor=NA) camera(y, list(set1=index1,set2=index2), design, inter.gene.cor=0.01)
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