Selective Th2

I'm a backend/infrastructure engineer at Uber with no wet-lab background. This week I used Claude Science and Claude Code to run a real analysis on the Zhu & Dann 2025 genome-scale CRISPRi Perturb-seq screen in primary human CD4+ T cells (Marson lab) — asking a question the original study's single Th2-minus-Th1 axis structurally can't answer: which gene knockdowns selectively suppress the Th2 (allergy) program without merely skewing cells toward Th1? That distinction is the whole target definition for asthma and allergy drugs. I streamed the full 16.8 GB differential-expression matrix directly from public S3 — reading only what I needed, never downloading it — decomposed the Ota-2021 signature into independent Th2 and Th1 arms, and found an exciting signal: a clean selective quadrant, 420 candidate suppressors, and GATA3 on top. Then I tried to destroy it. I built a multi-agent review layer in Claude Code — an investigator agent proposing mechanisms, a skeptic agent adversarially refuting them, and a 6-agent audit of my own methods. Under competitive scoring the headline arm-correlation collapsed (from +0.54 to +0.18, a scoring artifact); a permutation null put the empirical FDR near 1 (no better than random gene sets); and GATA3 re-scored as a Th1-skewer, exactly as canonical biology predicts. The result is an honest, calibrated negative — plus a reproducible streaming pipeline and an agentic audit layer that caught the over-claim before it went out. It matters because most hits in high-dimensional screens are artifacts; the reusable contribution is the infrastructure for catching them, and a demonstration that a software engineer can do trustworthy computational biology precisely because the tools help you distrust your own results.