PopPert: Population-level Joint-Distribution Modeling for Single-Cell Perturbation Prediction
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 distrib...
| Authors | Handong Wang, Jiaxin Qi, Haochen Feng et al. |
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| Primary category | q-bio.GN |
| Published | 1 September 2026 |
| Last updated at source | 1 September 2026 |
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