cisEffectTune: Pre-test and tuning.
In sigaR: Statistics for Integrative Genomics Analyses in R

Description Usage Arguments Value Note Author(s) References See Also Examples

Decides which test to perform: loss vs. no-loss (tumor surpressor) or no-gain vs gain (proto-onco). Followed by a tuning algorithm that enhances the overal power of the FDR procedure by excluding genes with either unbalanced (many samples having a high call probability of, say, a loss) or imprecise (many call probabilities close to 0.5) soft calls, which is likely to increase the probability of detection for genes with a more favorable call probability distribution.

1 2	cisEffectTune(CNdata, GEdata, testStatistic, nGenes=250, nPerm=250, minCallProbMass=0.10, verbose=TRUE)

`CNdata`	Object of class `cghCall`, containing (among others) annotion and call probabilities. Features should be matched with those of the accompanying `ExpressionSet`-object (as may be done using the `matchAnn2Ann`-function).
`GEdata`	Object of class `ExpressionSet`. Features should be matched with those of the accompanying `cghCall`-object (as may be done using the `matchAnn2Ann`-function).
`testStatistic`	Test statistic to be used, either `"wcvm"` or `"wmw"`, the weighted Cramer-Von Mises and the weighted Mann-Whitney test statistic, respectively.
`nGenes`	Number of genes used for tuning.
`nPerm`	Number of permutation used for tuning.
`minCallProbMass`	A number inbetween 0 and 1. Genes with a marginal call probabilities in one of the classes smaller than `minCallProbMass` are discarded from further analysis. Effectively, this ensures identifiability of copy number effect on expression.
`verbose`	Boolean to suppress output, either `FALSE` and `TRUE`.

A numeric-object with the genes selected for testing. Numbering corresponds to genes of the pre-tuned, but matched data set.

This function is a rewritten version of the intCNGEan.tune function of the intCNGEan-package.

Wessel N. van Wieringen: w.vanwieringen@vumc.nl

Van Wieringen, W.N., Van de Wiel, M.A. (2009), "Non-parametric testing for DNA copy number induced differential mRNA gene expression", Biometrics, 65(1), 19-29.

matchAnn2Ann, cisEffectTest

# load data
data(pollackCN16)
data(pollackGE16)

# select genes that are likely to have a significant genomic cis-effect on expression levels
genes2test <- cisEffectTune(pollackCN16, pollackGE16, "wmw", nGenes=50, nPerm=50)