  Single-cell RNA sequencing (scRNA-seq) data are important for studying the
laws of life at single-cell level. However, it is still challenging to obtain
enough high-quality scRNA-seq data. To mitigate the limited availability of
data, generative models have been proposed to computationally generate
synthetic scRNA-seq data. Nevertheless, the data generated with current models
are not very realistic yet, especially when we need to generate data with
controlled conditions. In the meantime, the Diffusion models have shown their
power in generating data at high fidelity, providing a new opportunity for
scRNA-seq generation.
  In this study, we developed scDiffusion, a generative model combining
diffusion model and foundation model to generate high-quality scRNA-seq data
with controlled conditions. We designed multiple classifiers to guide the
diffusion process simultaneously, enabling scDiffusion to generate data under
multiple condition combinations. We also proposed a new control strategy called
Gradient Interpolation. This strategy allows the model to generate continuous
trajectories of cell development from a given cell state.
  Experiments showed that scDiffusion can generate single-cell gene expression
data closely resembling real scRNA-seq data. Also, scDiffusion can
conditionally produce data on specific cell types including rare cell types.
Furthermore, we could use the multiple-condition generation of scDiffusion to
generate cell type that was out of the training data. Leveraging the Gradient
Interpolation strategy, we generated a continuous developmental trajectory of
mouse embryonic cells. These experiments demonstrate that scDiffusion is a
powerful tool for augmenting the real scRNA-seq data and can provide insights
into cell fate research.
