Single-cell RNA sequencing (scRNA-seq) data are important for studying the
biology of development or diseases at single-cell level. To better understand
the properties of the data, to build controlled benchmark data for testing
downstream methods, and to augment data when collecting sufficient real data is
challenging, generative models have been proposed to computationally generate
synthetic scRNA-seq data. However, 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 in computer vision at high fidelity, providing a new
opportunity for scRNA-seq generation.
In this study, we developed scDiffusion, a diffusion-based 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, surpassing state-of-the-art models
in multiple metrics. 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.

本研究使用 scDiffusion 模型基于扩散过程生成具有可控条件的高质量单细胞 RNA 测序数据，并展示其能够产生接近真实数据的单细胞基因表达数据，超过现有模型在多个度量指标上的性能，对罕见细胞类型等特定细胞类型进行条件生成，并通过梯度插值生成小鼠胚胎细胞的连续发育轨迹，以此证明 scDiffusion 是增强真实 scRNA-seq 数据和深入研究细胞命运的强大工具。