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）
scRNA-seq clustering is a critical task for analyzing single-cell RNA sequencing (scRNA-seq) data, as it groups cells with similar gene expression profiles. Transformers, as powerful foundational models, have been applied to scRNA-seq clustering. Their self-attention mechanism automatically assigns higher attention weights to cells within the same cluster, enhancing the distinction between clusters. Existing methods for scRNA-seq clustering, such as graph transformer-based models, treat each cell as a token in a sequence. Their computational and space complexities are $\mathcal{O}(n^2)$ with respect to the number of cells, limiting their applicability to large-scale scRNA-seq datasets.To address this challenge, we propose a Bipartite Graph Transformer-based clustering model (BGFormer) for scRNA-seq data. We introduce a set of learnable anchor tokens as shared reference points to represent the entire dataset. A bipartite graph attention mechanism is introduced to learn the similarity between cells and anchor tokens, bringing cells of the same class closer together in the embedding space. BGFormer achieves linear computational complexity with respect to the number of cells, making it scalable to large datasets. Experimental results on multiple large-scale scRNA-seq datasets demonstrate the effectiveness and scalability of BGFormer.
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）
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）
scDD: Latent Codes Based scRNA-seq Dataset Distillation with Foundation Model Knowledge
National Institute of Health Data Science, Peking University（北京大学健康数据科学国家研究院）；Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医学技术研究院）；State Key Laboratory of General Artificial Intelligence, Peking University（北京大学通用人工智能国家重点实验室）；AI Research Institute, China Mobile Communications Corporation（中国移动通信集团人工智能研究院）；Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机技术研究院）
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）
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）
Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-seq Data Analysis
School of Mathematical Sciences, Peking University（北京大学数学科学学院）；Center for Machine Learning Research, Peking University（北京大学机器学习研究中心）；Laboratory of Mathematics and Its Applications (LMAM), Peking University（北京大学数学及其应用实验室）；Center for Quantitative Biology, Peking University（北京大学定量生物学中心）；National Engineering Laboratory for Big Data Analysis and Applications, Peking University（北京大学大数据分析与应用国家工程实验室）；AI for Science Institute, Beijing（北京科学智能研究院）
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）
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）
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）
Structured Sparse Non-negative Matrix Factorization with L20-Norm for scRNA-seq Data Analysis
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University of Science and Technology of China（中国科学技术大学）；Shenzhen Research Institute of Big Data（深圳市大数据研究院）；Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）瓦谢尔计算生物研究院）
CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq
Stanford University（斯坦福大学）；California Institute of Technology（加州理工学院）；University of California, San Francisco（加州大学旧金山分校）；Chan-Zuckerberg Biohub（陈·扎克翰生物中心）
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）
scSiameseClu: A Siamese Clustering Framework for Interpreting single-cell RNA Sequencing Data
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）；College of Life Science, Northeast Agricultural University（东北农业大学生命科学学院）；SKL-IOTSC and Department of CIS, University of Macau（澳门大学SKL-IOTSC和CIS系）；Jilin University（吉林大学）
One Method Does Not Fit All: A Large-Scale Benchmark of Imputation Methods for Single-Cell RNA Sequencing Data
RPTU University Kaiserslautern-Landau, Department of Computer Science（莱茵-瓦尔登堡大学凯撒斯劳滕-兰道分校计算机科学系）；German Research Center for Artificial Intelligence (DFKI GmbH)（德国人工智能研究中心（DFKI GmbH））；Graduate School of Informatics, Osaka Metropolitan University（大阪 Metropolitan 大学信息研究生院）；intelligentX GmbH (intelligentX.com)（intelligentX GmbH（intelligentX.com））
scAGC: Learning Adaptive Cell Graphs with Contrastive Guidance for Single-Cell Clustering
Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, China（上海可信计算实验室，华东师范大学）；Department of Computational Biology, Mohamed bin Zayed University of AI, Abu Dhabi, UAE（计算生物学系，马尔代夫人工智能大学）；Department of Computer Science, Stevens Institute of Technology, Hoboken, USA（计算机科学系，史蒂文斯理工学院）；College of Electronics and Information Engineering, Tongji University, Shanghai, China（电子信息工程学院，同济大学）
scASDC: Attention Enhanced Structural Deep Clustering for Single-cell RNA-seq Data
School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）；College of Nursing Health Sciences, Yunnan Open University（云南开放大学护理健康科学学院）；Hefei Institutes of Physical Science, Chinese Academy of Sciences（中国科学院合肥物质科学研究院）
