PRiMeFlow: capturing complex expression heterogeneity in perturbation response modelling

Zichao Yan, Yan Wu, Mica Xu Ji, Chaitra Agrahar, Esther Wershof, Marcel Nassar, Mehrshad Sadria, Ridvan Eksi, Vladimir Trifonov, Ignacio L. Ibarra, Telmo Felgueira, Błażej Osiński, Rory Stark

Keywords: perturbation response modeling, virtual cell, flow matching, generative modeling, probabilistic model, scRNA-seq, perturb-seq, gene knockdown, virtual cell challenge

TL;DR: PRiMeFlow is an end-to-end flow matching model that operates directly in the full gene expression space, achieving state-of-the-art single-cell perturbation response predictions on PerturBench and the ARC Virtual Cell Challenge benchmarks.

Abstract: Predicting the effects of perturbations in-silico on cell state can identify drivers of cell behavior at scale and accelerate drug discovery. However, modeling challenges remain due to the inherent heterogeneity of single cell gene expression and the complex, latent gene dependencies. Here, we present PRiMeFlow, an end-to-end flow matching based approach to directly model the effects of genetic and small molecule perturbations in the gene expression space. The distribution-fitting approach taken by PRiMeFlow enables it to accurately approximate the empirical distribution of single-cell gene expression, which we demonstrate through extensive benchmarking inside PerturBench. Through ablation studies, we also validate important model design choices such as operating in gene expression space and parameterizing the velocity field with a U-Net architecture. Finally, by scaling PRiMeFlow to a broad perturbation data atlas spanning multiple datasets and employing a carefully designed pretraining-finetuning strategy, we demonstrate its outstanding performance on the H1 human embryonic stem cells from the ARC Virtual Cell Challenge benchmark.

Email Sharing: We authorize the sharing of all author emails with Program Chairs.

Data Release: We authorize the release of our submission and author names to the public in the event of acceptance.

Submission Number: 164