PopPert: Population-level Joint-Distribution Modeling for Single-Cell Perturbation Prediction

• URL: http://arxiv.org/abs/2609.01357v1
• Date: Tue, 01 Sep 2026 14:59:03 GMT
• Title: PopPert: Population-level Joint-Distribution Modeling for Single-Cell Perturbation Prediction
• Abstract summary: We propose PopPert, a framework that explicitly parameterizes population-level joint gene expression distributions.<n>Given a control population distribution and a perturbation condition, PopPert predicts perturbation-induced changes in distribution parameters.<n>PopPert achieves superior overall performance in differential expression recovery, perturbation effect estimation, and population-level distribution matching.
• Score: 17.491072730946158
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Predicting transcriptional responses to specific perturbations is critical for understanding cellular regulatory mechanisms and accelerating drug discovery. Single-cell RNA sequencing destroys each measured cell, yielding only unpaired populations of control and perturbed cells. However, existing methods typically model perturbation prediction at the single-cell level and assume cell-to-cell correspondence, which conflicts with the unpaired nature of the observed data. To address this challenge, we propose PopPert, a framework that explicitly parameterizes population-level joint gene expression distributions for collective transcriptional state modeling. Given a control population distribution and a perturbation condition, PopPert predicts perturbation-induced changes in distribution parameters, eliminating the need for cell-level correspondence and reducing sensitivity to single-cell noise. To effectively capture gene co-expression patterns, PopPert leverages a low-rank Gaussian Copula to model cross-gene statistical dependencies and construct the joint gene expression distribution, additionally allowing sampling of synthetic perturbed single-cell profiles. Across multiple single-cell benchmarks spanning both genetic and chemical perturbations, PopPert achieves superior overall performance in differential expression recovery, perturbation effect estimation, and population-level distribution matching. These results establish population-level joint distribution learning as an effective paradigm for predicting transcriptional responses from unpaired single-cell populations. Code for PopPert is publicly available at https://github.com/whd1125/PopPert.

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