stageR: a general stage-wise method for controlling the gene-level false discovery rate in differential expression...

Area of Science:

• Genomics
• Bioinformatics
• Statistical Genetics

Background:

• RNA sequencing studies with complex designs generate numerous hypotheses per gene.
• Conventional methods struggle to control the false discovery rate (FDR) at the gene level.

Purpose of the Study:

• To introduce stageR, a novel two-stage testing paradigm for RNA sequencing data.
• To provide robust gene-level FDR control and enhance statistical power in complex experimental designs.
• To enable powerful gene-level tests while preserving transcript-level biological interpretation.

Main Methods:

• StageR employs a two-stage testing strategy, aggregating individual transcript-level tests into gene-level assessments.
• It allows for post hoc evaluation of significant genes after initial screening.
• The framework is adaptable to various high-throughput experiments where hypotheses can be aggregated.

Main Results:

• StageR effectively controls the gene-level FDR in complex RNA sequencing analyses.
• The method significantly increases statistical power, particularly for detecting interaction effects.
• It maintains the ability to interpret findings at the transcript resolution.

Conclusions:

• StageR offers a unified and powerful framework for analyzing complex high-throughput experiments, particularly RNA sequencing.
• This approach addresses limitations of conventional methods by providing reliable gene-level FDR control and improved power.
• It facilitates more accurate and interpretable results in transcript-resolution analyses.