# CTRA - CD4+ T Cell Regulator Atlas

- **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:** [JIN KIM](https://cerebralvalley.ai/u/Jinky)
- **GitHub:** https://github.com/jink-ucla/Claude_Hackathon
- **Demo video:** https://youtu.be/sAe4PzEq3rE?si=AER5FTJo6CDBHCGh
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/297

I built an integrated, honestly-scored prioritization of gene-regulatory "master regulators" in primary human CD4+ T cells from the Marson/Pritchard 2025 genome-scale CRISPRi Perturb-seq screen (~22M cells, 4 donors, 3 stimulation states). Eight functional-genomics evidence layers shipped with the screen — trans-effect breadth, on-target CRISPRi QC, cross-guide and cross-donor reproducibility, gene-regulatory-network module membership, autoimmune-disease enrichment, polarization/aging regulator role, and K562 cross-cell-type specificity — are joined into ONE master table of 33,983 perturbed-gene × culture-condition rows, with a transparent additive regulator_score (0–9) that rewards convergent evidence. Because the paper's core thesis is that active regulators change with stimulation state, the table is stratified by Rest/Stim8hr/Stim48hr, never pooled.

What I found: the ranking recovers known CD4+ biology unsupervised — top regulators are BATF (9), STAT3/STAT6/GATA3/RASA2 (8), and the broadest trans-effect hubs are the proximal TCR machinery (CD3E, LAT, ZAP70, PLCG1; >5,000 downstream genes each). Context-specificity is quantified with a model-free reimplementation of the cz-benchmarks Perturbation Expression Prediction metric: the same perturbation's effect transfers across stimulation states only modestly (median Spearman rho ~ 0.20, ~20x a random baseline) and degrades with context distance — a direct confirmation of the paper's thesis and a floor any model (including scLDM.CD4) must beat. Autoimmune-disease-enriched GRN modules are most numerous at Stim8hr, several of them novel programs.

Why it matters: this turns a 22-million-cell screen into a triage-ready shortlist of context-specific, disease-linked CD4+ regulators — with honest scoring (a documented heuristic, not a black box) and 36 automated raw-vs-built integrity checks that all pass, so every number is reproducible and auditable. It's a template for turning any genome-scale Perturb-seq resource into a prioritized, disease-anchored regulator map.

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Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/297. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt