CytoWave improves prediction of cell gene responses to changes
CytoWave: Perturbation-Centric Pretraining for Single-Cell Response Prediction
Machine Learning
Summary
Predicting how individual cells change their gene activity when affected by different experiments or treatments is very important but hard because data can vary widely. The authors created CytoWave, a method that learns from large collections of gene data across species to better predict these changes. They train it in two parts: first by learning normal cell states, then by learning how cells respond to specific changes. Tests show CytoWave predicts changes well even on new types of data it did not see before.
What this means in practice
- •For biotech drug developers: Predict cellular gene expression changes from gene perturbations to help design and test new drugs.
- •For biomedical data engineers: Develop models that generalize well across diverse biological and experimental datasets for cell response prediction.
Authors
Yuwei Miao, Jingquan Yan, Hehuan Ma, Thao M. Dang, Lin Xu, Siyuan Zhang, Junzhou Huang
Abstract
Predicting gene expression under perturbations from single-cell data is a fundamental task for modeling cellular responses, with applications in discovering gene function and designing therapeutics. However, existing methods often struggle to generalize across datasets due to differences in experimental protocols, biological contexts, and species. We present \textbf{CytoWave}, a perturbation-centric pretraining framework for single-cell genetic perturbation response prediction. We leverage large-scale cross-species perturbation datasets unified via orthology and train CytoWave in a two-stage manner. In the first stage, we learn reference control cell states via masked gene expression recovery on unperturbed cells. In the second stage, we pretrain the model to predict perturbed gene expression given control states and perturbation signals, together with a contrastive objective that aligns latent representations of perturbation responses with perturbation signals. We perform strict dataset-held-out evaluation across multiple condition combinations and case studies of pathway activity changes, demonstrating consistent and effective performance of CytoWave in predicting single-cell genetic perturbation responses.