scMeanFlow: Fast, High-Quality, and Controllable scRNA-seq Synthesis via One-Step MeanFlow Matching

Generative modeling of single-cell RNA sequencing (scRNA-seq) data shows great potential in alleviating sample scarcity and enhancing downstream analyses. However, existing deep generative models generally face critical limitations: they either operate on continuous approximations that ignore the inherently discrete and over-dispersed nature of raw counts, or rely on multi-step iterative sampling that severely restricts inference scalability. Furthermore, flexible condition injection mechanisms remain insufficiently explored. To address these challenges simultaneously, we propose scMeanFlow, an efficient two-stage generative framework. First, a Negative Binomial Autoencoder encodes discrete count vectors into a compact continuous latent space while explicitly preserving count-level statistical properties via a probabilistic decoder. Second, operating within this latent space, a MeanFlow network learns the interval-averaged velocity field for genuine one-step generation without ordinary differential equation (ODE) solvers, while Feature-wise Linear Modulation (FiLM) injects condition information through feature-wise affine modulation. Random condition dropout during training enables a single trained model to support unconditional, single-condition, and multi-condition synthesis. Extensive experiments on four real scRNA-seq datasets demonstrate that scMeanFlow achieves state-of-the-art generation quality. Notably, its inference speed is approximately 7x faster than the strongest flow-based baseline and over 45x faster than diffusion-based methods. Furthermore, scMeanFlow effectively improves rare cell type classification through data augmentation. Overall, scMeanFlow provides an efficient, count-aware, and controllable solution for single-cell data synthesis.