# G-perturb

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Che Cheng](https://cerebralvalley.ai/u/che830621)
- **GitHub:** https://github.com/kiki830621/G-perturb
- **Demo video:** https://youtu.be/zBD30nhal64
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/206

G-perturb re-ranks drug targets from a genome-scale CD4+ T-cell Perturb-seq screen by whether each effect is dependable, not just by how large it is. A large transcriptional effect seen in a single guide or a single donor can be measurement noise, and each false lead costs a validation experiment. I treat every perturbation effect as a measurement in a crossed guide-by-donor-by-condition design and use generalizability theory, a framework from psychometrics, to give each target one dependability coefficient, then rank by effect size weighted by dependability. On the released 44.6 GB pseudobulk this reorders the shortlist: 50 of the top 100 targets by raw effect drop out of the top 100 by dependability. Read within activation states, the coefficient reconstructs the T-cell-receptor module (CD3 complex, ZAP70, LAT) as reliable only in activated cells, with no gene labels supplied. A design study shows the screen is limited by the number of guides, not donors. Every methodological decision was recorded and adversarially red-teamed by a competing model before any result was seen; the analysis regenerates end to end, and the manuscript is submitted to bioRxiv.

---

Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/206. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt