scRDiT: Generating single-cell RNA-seq data by diffusion transformers
  and accelerating sampling
Shengze Dong Zhuorui Cui Ding Liu Jinzhi Lei
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