Stan Pounds1, Stephan W Morris. 1. Department of Biostatistics, St. Jude Children's Research Hospital, 332 N. Lauderdale St., Memphis, TN 38105-2794, USA. stanley.pounds@stjude.org
Abstract
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: 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. AVAILABILITY: An S-plus function library is available from http://www.stjuderesearch.org/statistics.
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: 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. AVAILABILITY: An S-plus function library is available from http://www.stjuderesearch.org/statistics.
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