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The false discovery rate: a key concept in large-scale genetic studies
James J Chen1, Paula K Roberson, Michael J Schell
1Division of Personalized Nutrition and Medicine, National Center for Toxicological Research, Food and Drug Administration, HFT-20, Jefferson, AR 72079, USA. JamesJ.Chen@fda.hhs.gov
Cancer Control : Journal of the Moffitt Cancer Center
|December 17, 2009
Summary
When performing multiple statistical tests, the false discovery rate (FDR) approach offers a more practical significance cutoff than traditional methods. This method controls the expected proportion of false positives, allowing more discoveries in large-scale experiments.
Area of Science:
- Biostatistics
- Genomics
- Experimental Biology
Background:
- Statistical tests are crucial for hypothesis decisions in experimental research, often comparing groups like normal vs. tumor gene expression.
- P-values assess group differences, with rejection of the null hypothesis occurring below a significance level.
- Multiple tests increase the risk of false-positive findings if single-test significance levels are used.
Purpose of the Study:
- To provide an overview of the multiple testing framework.
- To describe the false discovery rate (FDR) approach for determining significance cutoffs with numerous tests.
Main Methods:
- Overview of the multiple testing framework.
- Description of the false discovery rate (FDR) approach.
- Illustration of an FDR-controlling procedure with a numerical example.
Main Results:
- The false discovery rate (FDR) is defined as the expected proportion of falsely rejected null hypotheses among all rejections.
- An FDR-controlling procedure is presented and demonstrated.
Conclusions:
- The family-wise error rate (FWE) is suitable for a small number of tests.
- For a large number of tests, the FDR approach is more effective and less stringent than FWE.
- FDR allows for more significant difference claims while accepting a small rate of false positives.
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