RevPert: predicting candidate drivers of transcriptomic state transitions via gallery-native reverse perturbation
Liang, S.; Yang, C.; Wang, J.; Li, y. · bioinformatics · 2026-09-06 · 原文
DOI：10.64898/2026.08.19.745674作者：4 位
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
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1. 人话版
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.
2. 领域脉络
本文类目：bioinformatics，属于其所在研究脉络的最新进展。
3. 机制拆解
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).
4. 证据与数字
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5. 反例与边界
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6. 跨领域连接与意外收获
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7. 可复用方法
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8. 术语表
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