The abstract is read at the publisher; the summary is JClub's.
DE-Rigor-Agent, an assumption-aware LLM agent for bulk RNA-seq, selected appropriate differential expression methods across 140 simulated datasets, matching incompatibilities and avoiding inflated FDRs.
- Why it matters: DE tools vary in assumptions and data needs, and LLM agents may run popular methods without verifying suitability, risking incorrect inference or weak power.
- What they did: The agent runs three stages—input standardization, method filtering, and execution—using expert criteria plus dataset-specific diagnostics to evaluate six methods before running retained ones.
- The result: Across 14 challenging simulation scenarios, its filtering aligned with introduced incompatibilities across random seeds with little extra compute, improving rigor and transparency by documenting retain/exclude reasons.
The findingWhy it mattersWhat they didThe result
AI summary of the abstract · may contain errors · not medical advice · read the paper.