Journal
BIOINFORMATICS
Volume 19, Issue 10, Pages 1236-1242Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btg148
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Funding
- NCI NIH HHS [R01 CA-87064, CA-21765] Funding Source: Medline
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Motivation: The occurrence of false positives and false negatives in a microarray analysis could be easily estimated if the distribution of p-values were approximated and then expressed as a mixture of null and alternative densities. Essentially any distribution of p-values can be expressed as such a mixture by extracting a uniform density from it. Results: A model is introduced that frequently describes very accurately the distribution of a set of p-values arising from an array analysis. The model is used to obtain an estimated distribution that is easily expressed as a mixture of null and alternative densities. Given a threshold of significance, the estimated distribution is partitioned into regions corresponding to the occurrences of false positives, false negatives, true positives, and true negatives.
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