Improving false discovery rate estimation.

Bioinformatics 2004 Jul 26;20(11):1737-45. Epub 2004 Feb 26.

Department of Biostatistics, MS 262 St Jude Children's Research Hospital, 332 N. Lauderdale Street, Memphis, TN 38105-2794, USA.

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.

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Source
https://academic.oup.com/bioinformatics/article-lookup/doi/1
Publisher Site
http://dx.doi.org/10.1093/bioinformatics/bth160DOI Listing
July 2004
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