A unified and practical taxonomy for false discovery rate control in omics research
Researchers in genomics and proteomics often struggle with choosing the right statistical methods to avoid false discoveries. A recent Nature Biotechnology Comment proposes a 2 × 2 matrix to help scientists navigate these options. This framework helps researchers match their data and study design with the most rigorous statistical procedures available.
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Background
In fields like genomics and mass spectrometry, researchers test thousands of biological features at once, creating a massive risk of false positives. While many statistical procedures exist to control the false discovery rate, they rely on different assumptions, making it confusing for scientists to choose the right one.
Details and Impact
To solve this, the authors propose a practical taxonomy organized around a 2 × 2 matrix. According to the authors, this matrix is designed to guide researchers in sequencing-based and mass-spectrometry-based omics. It helps them select either the most appropriate family of false discovery rate procedures or alternative, more rigorous methods tailored to their specific experimental setup.
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Importance 14/100
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- Nature Biotechnology ↗
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