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

Area of Science:

• Bioinformatics
• Statistical genomics
• Computational biology

Background:

• High-throughput biological data analysis identifies differential features (genes, proteins) between conditions.
• False discovery rate (FDR) control is crucial for analysis reliability, typically using p-values.
• P-value validity often requires strict data distribution assumptions or numerous replicates, limiting applicability.

Purpose of the Study:

• Introduce Clipper, a novel statistical framework for FDR control.
• Develop a method for reliable feature identification in high-throughput data analysis.
• Overcome limitations of p-value-based FDR control in biological studies.

Main Methods:

• Clipper framework for general statistical analysis.
• False discovery rate (FDR) control without p-values.
• Distribution-agnostic statistical approach.

Main Results:

• Clipper provides robust FDR control across diverse high-throughput data types.
• The framework demonstrates superior performance compared to existing methods.
• Successful application in identifying differential features without stringent assumptions.

Conclusions:

• Clipper offers a versatile and powerful alternative for FDR control in biological data analysis.
• The method enhances reliability and applicability in genomics and proteomics.
• Enables robust feature discovery even with limited replicates or non-standard data distributions.