Perturbation modeling

Cross-study drug-perturbation transcriptomics on Tahoe-100M + LINCS Level 2 (and appended DRUG-seq): harmonize compounds and gene symbols into a MappedCollection, train a modlyn linear classifier, then enrich and interpret top genes per perturbation.

A key pattern: append a new study to an existing collection and retrain. After Tahoe + LINCS were harmonized, DRUG-seq was aligned to the same perturbation label and gene panel, versioned into the collection, and the modlyn classifier was retrained on the expanded MappedCollection — without rebuilding the upstream pipeline.

Instance: laminlabs/sc-demo

Modeling & interpretation

Retrain on the updated collection after each append so new compounds and cells enter the feature-selection model.

Step	Transform	Outputs
Train / retrain SimpleLogReg (modlyn)	train_feature_selection_model.py	weights · train summary
Gene-module enrichment	perform_enrichment_analysis.ipynb	top genes · enrichment · top terms
Interpretation report (agent)	create_report.py · agent run	report