Bioinformatics Advance Access originally published online on February 26, 2004
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Bioinformatics 20(11) © Oxford University Press 2004; all rights reserved.
Improving false discovery rate estimation
Department of Biostatistics, MS 262 St Jude Children's Research Hospital, 332 N. Lauderdale Street, Memphis, TN 38105-2794, USA
Received on June 20, 2003; revised on January 27, 2004; accepted on January 28, 2004
Advance Access Publication February 26, 2004
Motivation: Recent attempts to account for multiple testing in the analysis of microarray data have focused on controlling the false discovery rate (FDR). However, rigorous control of the FDR at a preselected level is often impractical. Consequently, it has been suggested to use the q-value as an estimate of the proportion of false discoveries among a set of significant findings. However, such an interpretation of the q-value may be unwarranted considering that the q-value is based on an unstable estimator of the positive FDR (pFDR). Another method proposes estimating the FDR by modeling p-values as arising from a beta-uniform mixture (BUM) distribution. Unfortunately, the BUM approach is reliable only in settings where the assumed model accurately represents the actual distribution of p-values.
Methods: A method called the spacings LOESS histogram (SPLOSH) is proposed for estimating the conditional FDR (cFDR), the expected proportion of false positives conditioned on having k ‘significant’ findings. SPLOSH is designed to be more stable than the q-value and applicable in a wider variety of settings than BUM.
Results: In a simulation study and data analysis example, SPLOSH exhibits the desired characteristics relative to the q-value and BUM.
Availability: The Web site www.stjuderesearch.org/statistics/splosh.html has links to freely available S-plus code to implement the proposed procedure.
Contact: stanley.pounds{at}stjude.org
* To whom correspondence should be addressed.
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