Target prioritization 
¶
Re-construct Schmidt et al. (2022) and extend it toward immunotherapy target prioritization.
A genome-wide phenotypic CRISPRa screen was used to find transcriptional cell states that correlate with causal drivers of proteomic IFNG expression. The human pipeline calls positive IFNG hits and joins them with Perturb-seq cell states. Agent-tracked scripts then map those hits to mechanisms of action, score druggability and drug-repurposing candidates, and re-rank targets by enrichment in IFNG-high transcriptional states.
Project: Schmidt22 · Repo: laminlabs/schmidt22
Upstream workflow¶
To understand how the Perturb-seq joint result was obtained, look at the lineage:
Explore it here together with code and data artifacts. Executable steps are in build.yml.
Link |
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IFNG hits ( |
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Perturb-seq joint analysis |
Agentic analyses¶
Step |
Transform |
Outputs |
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1. MoA + immunotherapy map |
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2. Druggability + repurposing |
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3. Cell-state prioritization |
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4. Top-20 therapeutic shortlist |