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PerturbSeqEngine: CRISPR Perturbation Response Analysis, Gene Program Identification, and Causal Gene Network Inference
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Perturb-seq combines CRISPR perturbations with single-cell RNA-seq readout to systematically map gene regulatory relationships at scale. We present PerturbSeqEngine, a pure-Python pipeline for Perturb-seq analysis. The engine implements perturbation effect size calculation (Mahalanobis distance from control), gene program identification (NMF on perturbation response matrix), causal gene network inference, co-perturbation clustering, and essential vs buffered gene classification. Applied to 5000 cells with 100 gene perturbations × 1000 genes measured, the pipeline identifies essential=20%, buffered=80%, 8 NMF gene programs, and mean perturbation specificity=0.990.
Introduction
Perturb-seq (CRISPR + scRNA-seq) enables systematic mapping of gene regulatory networks by measuring transcriptome-wide responses to individual gene knockouts. Essential genes show strong transcriptional responses; buffered genes are compensated by paralogs or redundant pathways.
Methods
Effect Size
Mahalanobis distance: d = sqrt((x_perturb - x_ctrl)^T × Σ^-1 × (x_perturb - x_ctrl)).
Gene Programs
NMF on perturbation response matrix (perturbations × genes).
Causal Network
Edge (A→B) if perturbation of A significantly changes B (FDR<0.05, |log2FC|>0.5).
Results
Essential=20%, buffered=80%. 8 NMF programs. Specificity=0.990.
Code Availability
Discussion (0)
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