  The single-cell RNA sequencing (scRNA-seq) technology enables researchers to
study complex biological systems and diseases with high resolution. The central
challenge is synthesizing enough scRNA-seq samples; insufficient samples can
impede downstream analysis and reproducibility. While various methods have been
attempted in past research, the resulting scRNA-seq samples were often of poor
quality or limited in terms of useful specific cell subpopulations. To address
these issues, we propose a novel method called Single-Cell Latent Diffusion
(SCLD) based on the Diffusion Model. This method is capable of synthesizing
large-scale, high-quality scRNA-seq samples, including both 'holistic' or
targeted specific cellular subpopulations within a unified framework. A
pre-guidance mechanism is designed for synthesizing specific cellular
subpopulations, while a post-guidance mechanism aims to enhance the quality of
scRNA-seq samples. The SCLD can synthesize large-scale and high-quality
scRNA-seq samples for various downstream tasks. Our experimental results
demonstrate state-of-the-art performance in cell classification and data
distribution distances when evaluated on two scRNA-seq benchmarks.
Additionally, visualization experiments show the SCLD's capability in
synthesizing specific cellular subpopulations.

Genomics;Artificial Intelligence;Machine Learning