P2p improves prediction of cell responses to gene changes
P2P: Cross-View Population Denoising for Unpaired Single-Cell Perturbation Response Prediction
Emerging TechnologiesArtificial IntelligenceMachine Learning
Summary
Predicting how groups of cells react to gene changes is important but tricky because experiments destroy cells, so you never see the same cell before and after a change. The authors developed P2P, a method that looks at groups of cells rather than individuals, capturing reliable overall effects instead of noisy single-cell data. Their approach uses special neural networks to summarize control cells and represent genetic changes, improving accuracy in predicting gene activity changes across multiple datasets. This method helps better understand how cells respond to genetic perturbations by focusing on consistent group-level patterns.
What this means in practice
- •For biotech development teams: Improve prediction of gene expression changes in cell populations to aid drug target validation and genetic screening.
- •For computational biology units: Generate more accurate simulations of cellular response to genetic perturbations for hypothesis testing and experimental planning.
Authors
Haojie Yang, Ran Su
Abstract
AIVC (AI Virtual Cell) is a learned simulator of cellular behavior across conditions. Predicting how a cell population responds transcriptionally to a genetic perturbation is a core task. Perturb-seq records that response by destructive sequencing, so a control cell and a perturbed cell are never observed as a pair, and cells under one condition remain heterogeneous and noisy. Regression on individual cells absorbs sampling variation into the estimated effect, whereas interpretation requires the reproducible population effect. P2P (Perturbation-to-Perturbation) takes a stochastic cell-set view as its supervision unit. Two views drawn from the same condition share a reproducible population effect and differ by view-specific variation. A permutation-invariant set encoder summarizes the control population, a structured encoder represents perturbation tokens, cellular context, dose, and combination interactions, and a gate blends empirical condition-effect memory with a neural residual. A heteroscedastic head predicts the population mean and gene-wise response variance. Under one protocol and five seeds, P2P attains the lowest expression RMSE and the highest Effect Pearson, DEG F1, and DEG average precision on each of Adamson, Norman, Replogle K562, and Replogle RPE1 relative to GenePert, LinearPert, SLIM, Scouter, and scPILOT. On Replogle K562, Effect Pearson rises from 0.643 to 0.702 and DEG F1 rises from 0.067 to 0.178 relative to Scouter, the strongest baseline on both metrics.