CisTransCell: Single-Cell Perturbation Prediction via Gene Function, Regulatory Control, and Cellular Context
This work addresses the challenge of predicting perturbation effects for unseen genes or gene combinations, which is important for understanding gene function and disease mechanisms in single-cell biology.
CisTransCell predicts single-cell transcriptional responses to genetic perturbations in zero-shot settings by integrating gene function, regulatory control, and cellular context. It achieves strong performance on benchmark datasets.
Predicting cellular transcriptional responses to genetic perturbations is a central problem in single-cell biology, especially in the zero-shot setting where the perturbed gene or gene combination is unseen during training. A major difficulty is that perturbation effects are not determined by expression state alone: they depend on how the perturbed gene product influences other genes and proteins, how those downstream factors act on cis-regulatory elements, and which regulatory programs are active in the current cell state. To better capture this biological complexity, we propose CisTransCell, a cell-conditioned multi-modal framework for single-cell perturbation prediction that augments each gene with two complementary priors: a regulatory-sequence prior that captures how the gene is controlled, and a coding-sequence prior that captures what the gene product does. By integrating these priors with cellular expression state, CisTransCell models perturbation response as a cascade from gene function to regulatory control to downstream transcriptional change. Experiments on benchmark single-cell perturbation datasets show that CisTransCell achieves strong performance in zero-shot perturbation prediction.