Since its selection as the method of the year in 2013, single-cell technologies have become mature enough to provide answers to complex research questions. With the growth of single-cell profiling technologies, there has also been a significant increase in data collected from single-cell profilings, resulting in computational challenges to process these massive and complicated datasets. To address these challenges, deep learning (DL) is positioning as a competitive alternative for single-cell analyses besides the traditional machine learning approaches. Here we present a processing pipeline of single-cell RNA-seq data, survey a total of 25 DL algorithms and their applicability for a specific step in the processing pipeline. Specifically, we establish a unified mathematical representation of all variational autoencoder, autoencoder, and generative adversarial network models, compare the training strategies and loss functions for these models, and relate the loss functions of these models to specific objectives of the data processing step. Such presentation will allow readers to choose suitable algorithms for their particular objective at each step in the pipeline. We envision that this survey will serve as an important information portal for learning the application of DL for scRNA-seq analysis and inspire innovative use of DL to address a broader range of new challenges in emerging multi-omics and spatial single-cell sequencing.
Partial domain adaptation enables cross domain cell type annotation between scRNA-seq and snRNA-seq
Shandong Jianzhu University（山东建筑大学）；University of Electronic Science and Technology of China（电子科技大学）；Chongqing Jiaotong University（重庆交通大学）；Westlake University（西湖大学）
Bipartite Graph Attention-based Clustering for Large-scale scRNA-seq Data
Institute of Intelligent Information Processing, Shanxi University（山西大学智能信息处理研究院）；Alliance Manchester Business School, The University of Manchester（曼彻斯特大学阿联酋商业学院）
Application of Deep Learning on Single-Cell RNA-sequencing Data Analysis: A Review
Weill Cornell Medicine（威尔康奈尔医学院）；Cornell University（康奈尔大学）；Institute for Computational Biomedicine（计算生物医学研究所）；Caryl and Israel Englander Institute for Precision Medicine（卡里尔与以色列·英格兰德精准医学研究所）；Temple University（天普大学）
scInterpreter: Training Large Language Models to Interpret scRNA-seq Data for Cell Type Annotation
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences（中国科学院动物研究所干细胞与生殖生物学国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
A Hybrid Computational Intelligence Framework for scRNA-seq Imputation: Integrating scRecover and Random Forests
University of Technology Sydney, Australia（悉尼科技大学）；University of Sydney, Australia（悉尼大学）；Asia Pacific International College (APIC), Parramatta, NSW, Australia（亚太国际学院）；Lincoln Institute of Higher Education (LIHE), Sydney, Australia（林肯高等教育研究所）；The Institute of International Studies (TIIS), Sydney, Australia（国际研究学院）
SpaDiT: Diffusion Transformer for Spatial Gene Expression Prediction using scRNA-seq
the School of Information Science；Engineering , Yunnan University , 650091 , Kunming, Yunnan , China；College of Nursing Health Sciences , Yunnan Open University , 650599 , Kunming , China
scGTN: Deep Siamese Graph Transformer Network for Single-cell RNA Sequencing Clustering
Sichuan University（四川大学）；University of International Business and Economics（对外经济贸易大学）；Great Bay University（大湾区大学）；The Education University of Hong Kong（香港教育大学）
Graph Structure Learning for Tumor Microenvironment with Cell Type Annotation from non-spatial scRNA-seq data
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；Research & Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；School of Engineering, The University of Sydney（悉尼大学工程学院）；College of Life Sciences, Northwest University（西北大学生命科学学院）；Research Office, City University of Hong Kong (Dongguan)（香港城市大学（东莞）研究办公室）
scKDGM: KAN-guided Dynamic Graph Masked Learning for Single-Cell RNA-seq Clustering
College of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）；Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences（中国科学院新疆物理化学研究所）
scCDCG: Efficient Deep Structural Clustering for single-cell RNA-seq via Deep Cut-informed Graph Embedding
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of Chinese Academy of Sciences（中国科学院大学）；Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（中国科学院大学杭州先进研究所）；State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences（中国科学院动物研究所干细胞与生殖生物学重点实验室）；Institute for Stem Cell and Regenerative Medicine, Chinese Academy of Science（中国科学院干细胞与再生医学研究所）；Beijing Institute for Stem Cell and Regenerative Medicine, Chinese Academy of Science（中国科学院北京干细胞与再生医学研究所）
scRNA-seq of preeclamptic trophoblasts identifies EBI3, COL17A1, miR-27a-5p, and miR-193b-5p as hypoxia markers: validation of neuradapt as a superior mimetic to cobalt chloride
HSE University（高等经济大学）；Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences（俄罗斯科学院谢米亚金-奥夫钦尼科夫生物有机化学研究所）
Integrating gene regulatory priors into Transformer attention with scTransformer for interpretable scRNA-seq analysis
Department of Information Engineering, University of Padova（信息工程系，帕多瓦大学）；Department of Neuroscience, University of Padova（神经科学系，帕多瓦大学）；Padova Neuroscience Center（帕多瓦神经科学中心）；Information Systems Institute, University of Applied Sciences Western Switzerland, HES-SO Valais（应用科学西瑞士信息系统研究所，HES-SO瓦莱大学）；Department of Comparative Biomedicine and Food Science, University of Padova（比较生物医学与食品科学系，帕多瓦大学）；Padua Center for Network Medicine, University of Padova（帕维亚网络医学中心，帕多瓦大学）
Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability
UNSW BioMedical Machine Learning Lab (BML), School of Biomedical Engineering, UNSW Sydney（UNSW生物医学机器学习实验室（BML），生物医学工程学院，UNSW悉尼）；School of Biomedical Engineering, UNSW（生物医学工程学院，UNSW）；Department of Computer Science, Zhejiang University（浙江大学计算机科学系）；Institute for Intelligent Systems Research and Innovation, Deakin University（智能系统研究与创新研究所，德金大学）；School of Engineering & Technology, Duy Tan University（工程与技术学院，杜益大学）；School of Computing, Macquarie University（计算学院，麦觉大学）；Center of Excellence in Precision Medicine and Digital Health, Faculty of Dentistry, Chulalongkorn University（精准医学与数字健康中心，牙科学院，朱拉隆功大学）
