Clipper: A General Statistical Framework for P-Value-Free FDR Control in Large-Scale Feature Screening
Speaker: Jingyi Jessica Li, UCLA
Abstract: Large-scale feature screening is ubiquitous in high-throughput biological data analysis: identifying the features (e.g., genes, mRNA transcripts, and proteins) that differ between conditions from numerous features measured simultaneously. The false discovery rate (FDR) is the most widely-used criterion to ensure the reliability of screened features. The most famous Benjamini-Hochberg procedure for FDR control requires valid high-resolution p-values, which are, however, often hardly achievable because of the reliance on reasonable distributional assumptions or large sample sizes. Motivated by the Barber-Candes procedure, Clipper is a general statistical framework for large-scale feature screening with theoretical FDR control and without p-value requirement. Extensive numerical studies have verified that Clipper is a versatile and effective tool for correcting the FDR inflation crisis in multiple bioinformatics applications.