  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.
