T-cell Evidence Workbench
An evidence-first bioinformatics workbench for auditing and prioritizing primary human CD4+ T-cell CRISPRi Perturb-seq results. A deterministic Python pipeline audits 33,983 perturbation-condition rows across four donors and three culture conditions, applies six registered source-QC gates, and produces 1,652 eligible rows plus 224 cross-condition candidates. The Next.js analyst interface connects every shortlist, comparison, robustness scenario, and finding card to inspectable evidence and provenance. It prioritizes candidates for expert follow-up; it does not claim independent biological validation.
Proof points
- 33,983 Rows Audited
- 224 Cross-Condition Genes
- 6 Registered QC Gates
Technologies
Python, Pandas, Bioinformatics, Perturb-seq, Next.js, TypeScript, React Flow, ELK, Pytest
Links