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
Network-based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis
University of Minnesota Twin Cities（明尼苏达大学双城分校）；Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences（中国科学院广州生物医药与健康研究院）；University of Kansas Medical Center（堪萨斯大学医学中心）
A Hierarchical Sheaf Spectral Embedding Framework for Single-Cell RNA-seq Analysis
Department of Mathematics, Michigan State University（密歇根州立大学数学系）；Department of Biochemistry and Molecular Biology, Michigan State University（密歇根州立大学生物化学与分子生物学系）；Department of Electrical and Computer Engineering, Michigan State University（密歇根州立大学电气与计算机工程系）
