Single-Cell RNA-seq Synthesis with Latent Diffusion Model

TL;DR

This paper introduces SCLD, a novel diffusion model-based method for synthesizing large-scale, high-quality single-cell RNA-seq data, improving downstream analysis and enabling targeted subpopulation generation.

Contribution

The paper presents SCLD, a diffusion model framework with guidance mechanisms for high-quality, scalable, and targeted synthesis of scRNA-seq samples, outperforming existing methods.

Findings

State-of-the-art cell classification accuracy

Improved data distribution similarity

Effective synthesis of specific cell subpopulations

Abstract

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.