Spatially resolved transcriptomics brings exciting breakthroughs to single-cell analysis by providing physical locations along with gene expression. However, as a cost of the extremely high spatial resolution, the cellular level spatial transcriptomic data suffer significantly from missing values. While a standard solution is to perform imputation on the missing values, most existing methods either overlook spatial information or only incorporate localized spatial context without the ability to capture long-range spatial information. Using multi-head self-attention mechanisms and positional encoding, transformer models can readily grasp the relationship between tokens and encode location information. In this paper, by treating single cells as spatial tokens, we study how to leverage transformers to facilitate spatial tanscriptomics imputation. In particular, investigate the following two key questions: (1) $\textit{how to encode spatial information of cells in transformers}$, and (2) $\textit{ how to train a transformer for transcriptomic imputation}$. By answering these two questions, we present a transformer-based imputation framework, SpaFormer, for cellular-level spatial transcriptomic data. Extensive experiments demonstrate that SpaFormer outperforms existing state-of-the-art imputation algorithms on three large-scale datasets while maintaining superior computational efficiency.
stMCDI: Masked Conditional Diffusion Model with Graph Neural Network for Spatial Transcriptomics Data Imputation
School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Hefei Institutes of Physical Science, Chinese Academy of Science（中国科学院合肥物质科学研究院）
GenoBERT: A Language Model for Accurate Genotype Imputation
School of Computing Sciences and Computer Engineering, University of Southern Mississippi（南密西西比大学计算科学与计算机工程学院）；Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University（杜兰大学医学院德明医学系杜兰生物医学信息学与基因组学中心）；Kennesaw State University（肯尼索州立大学）；College of Science, Xi’an Shiyou University（西安石油大学理学院）；Geisel School of Medicine, Dartmouth College（达特茅斯学院盖泽尔医学院）；Environmental Laboratory, U.S. Army Engineer Research and Development Center（美国陆军工程兵研究与发展中心环境实验室）
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）
Leveraging Natural Language Processing to Unravel the Mystery of Life: A Review of NLP Approaches in Genomics, Transcriptomics, and Proteomics
The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel-Aviv University（谢蒙尼生物医学与癌症研究学院，乔治·S·威斯生命科学学院，特拉维夫大学）
Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation
BioMedical Machine Learning Laboratory, School of Biomedical Engineering, UNSW Sydney（新南威尔士大学生物医学工程学院生物医学机器学习实验室）；School of Biotechnology and Biomolecular Sciences, UNSW Sydney（新南威尔士大学生物技术与分子科学学院）；School of Computing, Macquarie University（麦考瑞大学计算机学院）；Strands（23Strands）；Shenzhen Key Laboratory for High Performance Data Mining, Shenzhen Institute of Advanced Technology（深圳先进技术研究院高性能数据挖掘重点实验室）；School of Biomedical Engineering, UNSW Sydney（新南威尔士大学生物医学院）；Center of Excellence in Precision Medicine and Digital Health, Faculty of Dentistry, Chulalongkorn University（朱拉隆功大学牙科学院精准医疗与数字健康卓越中心）；UNSW AI Institute, University of New South Wales（新南威尔士大学人工智能研究所）；Shanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院上海免疫学研究所）
ChromFound: Towards A Universal Foundation Model for Single-Cell Chromatin Accessibility Data
Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化院）；Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）；Computer, Electrical and Mathematical Sciences and Engineering Division, KAUST（卡塔尔大学计算机、电子与数学科学与工程系）；Center of Excellence for Smart Health, KAUST（卡塔尔大学智能健康卓越中心）；Center of Excellence on GenAI, KAUST（卡塔尔大学生成人工智能卓越中心）；Zhongshan Hospital, Fudan University（复旦大学中山医院）
