scDiffusion: conditional generation of high-quality single-cell data using diffusion model
CoRR(2024)
Abstract
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.
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