Improving false discovery rate estimation.
BACKGROUND
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.


BACKGROUND
The Web site www.stjuderesearch.org/statistics/splosh.html has links to freely available S-plus code to implement the proposed procedure.
Department of Biostatistics, MS 262 St Jude Children's Research Hospital, 332 N. Lauderdale Street, Memphis, TN 38105-2794, USA. stanley.pounds@stjude.org
Stan
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Pounds
Cheng
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Cheng
D000465:Algorithms; D019985:Benchmarking / Q000379:methods; D003198:Computer Simulation; D005189:False Positive Reactions; D020869:Gene Expression Profiling / Q000379:methods* / Q000592:standards; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000592:standards; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity