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bioRxiv · 10.1101/2020.11.19.390773

Clipper: p-value-free FDR control on high-throughput data from two conditions

Ge, X.·Chen, Y. E.·Song, D.·McDermott, M.·Woyshner, K.·Manousopoulou, A.·Wang, L. D.·Li, W.·Li, J. J.
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Abstract

High-throughput biological data analysis commonly involves identifying features such as genes, genomic regions, and proteins, whose values differ between two conditions, from numerous features measured simultaneously. The most widely-used criterion to ensure the analysis reliability is the false discovery rate (FDR), which is primarily controlled based on p-values. However, obtaining valid p-values relies on either reasonable assumptions of data distribution or large numbers of replicates under both conditions. Clipper is a general statistical framework for FDR control without relying on p-values or specific data distributions. Clipper outperforms existing methods for a broad range of applications in high-throughput data analysis.

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Ge, X., Chen, Y. E., Song, D., McDermott, M., Woyshner, K., Manousopoulou, A., Wang, L. D., Li, W., Li, J. J.. 2020-11-21. Clipper: p-value-free FDR control on high-throughput data from two conditions. https://doi.org/10.1101/2020.11.19.390773

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