HighProbability determines which alternative hypotheses are sufficiently probable: Genomic applications include detection of differential gene expression

Details HighProbability determines which alternative hypotheses are sufficiently probable: Genomic applications include detection of differential gene expression HighProbability determines which alternative hypotheses are sufficiently probable: Genomic applications include detection of differential gene expression

2004-02-29

arxiv.org/abs/q-bio/0402049v1

Biology

Quantitative Biology

Quantitative Methods

Submitted for publication; check http://www.davidbickel.com for updates

Scientific paper

Many genomic experiments, notably microarray experiments seeking to detect differential gene expression, involve calculating a large number of p-values. This leads to the multiple testing problem: when the number of null hypotheses is large, the probability of accepting at least one false alternative hypothesis is often much greater than the significance level of the tests, which tends to mislead investigators. Software called HighProbability provides a simple, fast, reliable solution to the multiple testing problem, with applications to many areas of bioinformatics. For example, in a microarray study, HighProbability can determine which genes are probably differentially expressed. Given a set of p-values not adjusted for multiple testing, HighProbability determines which ones are low enough to imply a high probability of the truth of their alternative hypotheses. The set of p-values may be determined by conventional hypothesis testing or by random permutations using existing R or S-PLUS software. HighProbability is freely available under license through http://www.davidbickel.com . Coded in S, HighProbability currently requires an installation of R or S-PLUS, but the algorithm is short enough for fast implementation in non-S languages as well.

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