# DE-Rigor-Agent

In bulk RNA-seq differential expression, general-purpose LLM agents tend to pick DESeq2 or edgeR without checking whether those methods' statistical assumptions fit the data at hand. DE-Rigor-Agent splits the analysis into input standardization, method filtering and method execution, and before running anything it evaluates six common methods against expert-curated method cards and dataset-specific diagnostics, recording why each is kept or excluded. Across 140 simulated datasets its filtering decisions largely matched the incompatibilities built into the simulations and stayed stable across random seeds, while two Codex baselines chose DESeq2 or edgeR for nearly all datasets.

In bulk RNA-seq differential expression, general-purpose LLM agents tend to pick DESeq2 or edgeR without checking whether those methods' statistical assumptions fit the data at hand. DE-Rigor-Agent splits the analysis into input standardization, method filtering and method execution, and before running anything it evaluates six common methods against expert-curated method cards and dataset-specific diagnostics, recording why each is kept or excluded. Across 140 simulated datasets its filtering decisions largely matched the incompatibilities built into the simulations and stayed stable across random seeds, while two Codex baselines chose DESeq2 or edgeR for nearly all datasets.