RevPert: ranking candidate drivers of transcriptomic state transitions via gallery-native reverse perturbation
Liang, S.; Yang, C.; Wang, J.; Li, y.
Show abstract
Cellular state transitions underlie adaptation, ageing and disease, yet prioritizing catalogued genetic perturbations whose expression signatures match an observed transcriptomic shift remains difficult. Most models predict phenotype from a nominated intervention, whereas genetic inverse benchmarks are largely restricted to within-screen identity recovery. Here we introduce RevPert, a gallery-native reverse perturbation model that ranks a fixed genetic catalog for a query contrast {Delta}Y* = YB - YA by combining signed Pearson connectivity with a learned residual. Across Replogle Essential Perturb-seq (four lines) and LINCS-KO screens (ten lines), RevPert recovered held-out interventions at leading performance relative to matched baselines. Applied to public drug-resistance contrasts in HCC and CML, dual-arm ranking placed pre-specified disease anchors far higher on the expected arms than ranking the same signatures by differential-expression magnitude alone (Essential residual model for HCC; a transductive GWPS residual for CML). RevPert therefore couples within-screen reverse ranking to a screen-external signed-geometry check; the latter calibrates literature anchors and is not claimed as held-out recovery.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states 95%
- scCausalVI disentangles single-cell perturbation responses with causality-aware generative model 94%
- Engineering of highly active and diverse nuclease enzymes by combining machine learning and ultra-high-throughput screening 93%
Similar papers in this journal
Similar papers in this journal
- PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations 94%
- A tissue-aware machine learning framework enhances the mechanistic understanding and genetic diagnosis of Mendelian and rare diseases 93%
- CellRegMap: A statistical framework for mapping context-specific regulatory variants using scRNA-seq 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.