scRDiT: Generating single-cell RNA-seq data by diffusion transformers and accelerating sampling

• URL: http://arxiv.org/abs/2404.06153v1
• Date: Tue, 9 Apr 2024 09:25:16 GMT
• Title: scRDiT: Generating single-cell RNA-seq data by diffusion transformers and accelerating sampling
• Authors: Shengze Dong, Zhuorui Cui, Ding Liu, Jinzhi Lei,
• Abstract summary: Single-cell RNA sequencing (scRNA-seq) is a groundbreaking technology extensively utilized in biological research. Our study introduces a generative approach termed scRNA-seq Diffusion Transformer (scRDiT) This method generates virtual scRNA-seq data by leveraging a real dataset.
• Score: 9.013834280011293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Motivation: Single-cell RNA sequencing (scRNA-seq) is a groundbreaking technology extensively utilized in biological research, facilitating the examination of gene expression at the individual cell level within a given tissue sample. While numerous tools have been developed for scRNA-seq data analysis, the challenge persists in capturing the distinct features of such data and replicating virtual datasets that share analogous statistical properties. Results: Our study introduces a generative approach termed scRNA-seq Diffusion Transformer (scRDiT). This method generates virtual scRNA-seq data by leveraging a real dataset. The method is a neural network constructed based on Denoising Diffusion Probabilistic Models (DDPMs) and Diffusion Transformers (DiTs). This involves subjecting Gaussian noises to the real dataset through iterative noise-adding steps and ultimately restoring the noises to form scRNA-seq samples. This scheme allows us to learn data features from actual scRNA-seq samples during model training. Our experiments, conducted on two distinct scRNA-seq datasets, demonstrate superior performance. Additionally, the model sampling process is expedited by incorporating Denoising Diffusion Implicit Models (DDIM). scRDiT presents a unified methodology empowering users to train neural network models with their unique scRNA-seq datasets, enabling the generation of numerous high-quality scRNA-seq samples. Availability and implementation: https://github.com/DongShengze/scRDiT

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