[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-OmicsML--awesome-deep-learning-single-cell-papers":3,"tool-OmicsML--awesome-deep-learning-single-cell-papers":65},[4,23,32,40,49,57],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":22},2268,"ML-For-Beginners","microsoft\u002FML-For-Beginners","ML-For-Beginners 是由微软推出的一套系统化机器学习入门课程，旨在帮助零基础用户轻松掌握经典机器学习知识。这套课程将学习路径规划为 12 周，包含 26 节精炼课程和 52 道配套测验，内容涵盖从基础概念到实际应用的完整流程，有效解决了初学者面对庞大知识体系时无从下手、缺乏结构化指导的痛点。\n\n无论是希望转型的开发者、需要补充算法背景的研究人员，还是对人工智能充满好奇的普通爱好者，都能从中受益。课程不仅提供了清晰的理论讲解，还强调动手实践，让用户在循序渐进中建立扎实的技能基础。其独特的亮点在于强大的多语言支持，通过自动化机制提供了包括简体中文在内的 50 多种语言版本，极大地降低了全球不同背景用户的学习门槛。此外，项目采用开源协作模式，社区活跃且内容持续更新，确保学习者能获取前沿且准确的技术资讯。如果你正寻找一条清晰、友好且专业的机器学习入门之路，ML-For-Beginners 将是理想的起点。",85092,2,"2026-04-10T11:13:16",[13,14,15,16,17,18,19,20,21],"图像","数据工具","视频","插件","Agent","其他","语言模型","开发框架","音频","ready",{"id":24,"name":25,"github_repo":26,"description_zh":27,"stars":28,"difficulty_score":29,"last_commit_at":30,"category_tags":31,"status":22},5784,"funNLP","fighting41love\u002FfunNLP","funNLP 是一个专为中文自然语言处理（NLP）打造的超级资源库，被誉为\"NLP 民工的乐园”。它并非单一的软件工具，而是一个汇集了海量开源项目、数据集、预训练模型和实用代码的综合性平台。\n\n面对中文 NLP 领域资源分散、入门门槛高以及特定场景数据匮乏的痛点，funNLP 提供了“一站式”解决方案。这里不仅涵盖了分词、命名实体识别、情感分析、文本摘要等基础任务的标准工具，还独特地收录了丰富的垂直领域资源，如法律、医疗、金融行业的专用词库与数据集，甚至包含古诗词生成、歌词创作等趣味应用。其核心亮点在于极高的全面性与实用性，从基础的字典词典到前沿的 BERT、GPT-2 模型代码，再到高质量的标注数据和竞赛方案，应有尽有。\n\n无论是刚刚踏入 NLP 领域的学生、需要快速验证想法的算法工程师，还是从事人工智能研究的学者，都能在这里找到急需的“武器弹药”。对于开发者而言，它能大幅减少寻找数据和复现模型的时间；对于研究者，它提供了丰富的基准测试资源和前沿技术参考。funNLP 以开放共享的精神，极大地降低了中文自然语言处理的开发与研究成本，是中文 AI 社区不可或缺的宝藏仓库。",79857,1,"2026-04-08T20:11:31",[19,14,18],{"id":33,"name":34,"github_repo":35,"description_zh":36,"stars":37,"difficulty_score":29,"last_commit_at":38,"category_tags":39,"status":22},5773,"cs-video-courses","Developer-Y\u002Fcs-video-courses","cs-video-courses 是一个精心整理的计算机科学视频课程清单，旨在为自学者提供系统化的学习路径。它汇集了全球知名高校（如加州大学伯克利分校、新南威尔士大学等）的完整课程录像，涵盖从编程基础、数据结构与算法，到操作系统、分布式系统、数据库等核心领域，并深入延伸至人工智能、机器学习、量子计算及区块链等前沿方向。\n\n面对网络上零散且质量参差不齐的教学资源，cs-video-courses 解决了学习者难以找到成体系、高难度大学级别课程的痛点。该项目严格筛选内容，仅收录真正的大学层级课程，排除了碎片化的简短教程或商业广告，确保用户能接触到严谨的学术内容。\n\n这份清单特别适合希望夯实计算机基础的开发者、需要补充特定领域知识的研究人员，以及渴望像在校生一样系统学习计算机科学的自学者。其独特的技术亮点在于分类极其详尽，不仅包含传统的软件工程与网络安全，还细分了生成式 AI、大语言模型、计算生物学等新兴学科，并直接链接至官方视频播放列表，让用户能一站式获取高质量的教育资源，免费享受世界顶尖大学的课堂体验。",79792,"2026-04-08T22:03:59",[18,13,14,20],{"id":41,"name":42,"github_repo":43,"description_zh":44,"stars":45,"difficulty_score":46,"last_commit_at":47,"category_tags":48,"status":22},3128,"ragflow","infiniflow\u002Fragflow","RAGFlow 是一款领先的开源检索增强生成（RAG）引擎，旨在为大语言模型构建更精准、可靠的上下文层。它巧妙地将前沿的 RAG 技术与智能体（Agent）能力相结合，不仅支持从各类文档中高效提取知识，还能让模型基于这些知识进行逻辑推理和任务执行。\n\n在大模型应用中，幻觉问题和知识滞后是常见痛点。RAGFlow 通过深度解析复杂文档结构（如表格、图表及混合排版），显著提升了信息检索的准确度，从而有效减少模型“胡编乱造”的现象，确保回答既有据可依又具备时效性。其内置的智能体机制更进一步，使系统不仅能回答问题，还能自主规划步骤解决复杂问题。\n\n这款工具特别适合开发者、企业技术团队以及 AI 研究人员使用。无论是希望快速搭建私有知识库问答系统，还是致力于探索大模型在垂直领域落地的创新者，都能从中受益。RAGFlow 提供了可视化的工作流编排界面和灵活的 API 接口，既降低了非算法背景用户的上手门槛，也满足了专业开发者对系统深度定制的需求。作为基于 Apache 2.0 协议开源的项目，它正成为连接通用大模型与行业专有知识之间的重要桥梁。",77062,3,"2026-04-04T04:44:48",[17,13,20,19,18],{"id":50,"name":51,"github_repo":52,"description_zh":53,"stars":54,"difficulty_score":46,"last_commit_at":55,"category_tags":56,"status":22},519,"PaddleOCR","PaddlePaddle\u002FPaddleOCR","PaddleOCR 是一款基于百度飞桨框架开发的高性能开源光学字符识别工具包。它的核心能力是将图片、PDF 等文档中的文字提取出来，转换成计算机可读取的结构化数据，让机器真正“看懂”图文内容。\n\n面对海量纸质或电子文档，PaddleOCR 解决了人工录入效率低、数字化成本高的问题。尤其在人工智能领域，它扮演着连接图像与大型语言模型（LLM）的桥梁角色，能将视觉信息直接转化为文本输入，助力智能问答、文档分析等应用场景落地。\n\nPaddleOCR 适合开发者、算法研究人员以及有文档自动化需求的普通用户。其技术优势十分明显：不仅支持全球 100 多种语言的识别，还能在 Windows、Linux、macOS 等多个系统上运行，并灵活适配 CPU、GPU、NPU 等各类硬件。作为一个轻量级且社区活跃的开源项目，PaddleOCR 既能满足快速集成的需求，也能支撑前沿的视觉语言研究，是处理文字识别任务的理想选择。",75489,"2026-04-13T11:13:28",[19,13,20,18],{"id":58,"name":59,"github_repo":60,"description_zh":61,"stars":62,"difficulty_score":29,"last_commit_at":63,"category_tags":64,"status":22},3215,"awesome-machine-learning","josephmisiti\u002Fawesome-machine-learning","awesome-machine-learning 是一份精心整理的机器学习资源清单，汇集了全球优秀的机器学习框架、库和软件工具。面对机器学习领域技术迭代快、资源分散且难以甄选的痛点，这份清单按编程语言（如 Python、C++、Go 等）和应用场景（如计算机视觉、自然语言处理、深度学习等）进行了系统化分类，帮助使用者快速定位高质量项目。\n\n它特别适合开发者、数据科学家及研究人员使用。无论是初学者寻找入门库，还是资深工程师对比不同语言的技术选型，都能从中获得极具价值的参考。此外，清单还延伸提供了免费书籍、在线课程、行业会议、技术博客及线下聚会等丰富资源，构建了从学习到实践的全链路支持体系。\n\n其独特亮点在于严格的维护标准：明确标记已停止维护或长期未更新的项目，确保推荐内容的时效性与可靠性。作为机器学习领域的“导航图”，awesome-machine-learning 以开源协作的方式持续更新，旨在降低技术探索门槛，让每一位从业者都能高效地站在巨人的肩膀上创新。",72149,"2026-04-03T21:50:24",[20,18],{"id":66,"github_repo":67,"name":68,"description_en":69,"description_zh":70,"ai_summary_zh":70,"readme_en":71,"readme_zh":72,"quickstart_zh":73,"use_case_zh":74,"hero_image_url":75,"owner_login":76,"owner_name":76,"owner_avatar_url":77,"owner_bio":78,"owner_company":69,"owner_location":69,"owner_email":69,"owner_twitter":69,"owner_website":79,"owner_url":80,"languages":69,"stars":81,"forks":82,"last_commit_at":83,"license":84,"difficulty_score":29,"env_os":85,"env_gpu":86,"env_ram":86,"env_deps":87,"category_tags":90,"github_topics":69,"view_count":10,"oss_zip_url":69,"oss_zip_packed_at":69,"status":22,"created_at":91,"updated_at":92,"faqs":93,"releases":94},7195,"OmicsML\u002Fawesome-deep-learning-single-cell-papers","awesome-deep-learning-single-cell-papers",null,"awesome-deep-learning-single-cell-papers 是一个专注于单细胞分析与深度学习交叉领域的开源论文合集。它系统地收集并分类了该方向的前沿学术成果，旨在帮助科研人员快速追踪最新的技术动态。\n\n在单细胞测序数据爆炸式增长的背景下，如何利用深度学习高效处理高维、稀疏且复杂的生物数据成为一大挑战。这份资源库通过将海量文献按任务类型精细划分——涵盖从基础的细胞聚类、批次效应校正，到前沿的多模态整合、空间转录组分析、药物反应预测及基础模型构建等二十多个细分领域，有效解决了研究者“找文献难、分类乱”的痛点。此外，它还收录了相关书籍、课程资源以及基准测试工具，形成了完整的知识生态。\n\n该资源特别适合生物信息学研究人员、计算生物学家以及从事医疗 AI 开发的工程师使用。无论是刚入门的学生希望系统了解领域全貌，还是资深专家需要调研特定方向的最新进展，都能从中获得极大便利。其独特的亮点在于不仅关注算法本身，还深入涵盖了可解释性、数据模拟及亚细胞分析等深层议题，并关联了专门的单细胞基础模型论文库，为推动生命科学领域的智能化研究提供了坚实的文献基石。","[![Awesome](https:\u002F\u002Fcdn.rawgit.com\u002Fsindresorhus\u002Fawesome\u002Fd7305f38d29fed78fa85652e3a63e154dd8e8829\u002Fmedia\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fsindresorhus\u002Fawesome)\n[![PRs Welcome](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPRs-welcome-brightgreen.svg?style=flat-square)](https:\u002F\u002Fgithub.com\u002FOmicsML\u002Fawesome-deep-learning-single-cell-papers\u002Fpulls)\n# awesome-deep-learning-single-cell-papers\n\nThis repository keeps track of the latest papers on single-cell analysis with deep learning methods. We categorize them based on individual tasks.\n\nWe will try to make this list updated. If you find any error or any missed paper, please don't hesitate to open an issue or pull request.\n\n## Citation\n\nBe free to refer to our comprehensive survey paper on Deep Learning in Single-cell Analysis:\n\n```bibtex\n@article{molho2024deep,\n  title={Deep learning in single-cell analysis},\n  author={Molho, Dylan and Ding, Jiayuan and Tang, Wenzhuo and Li, Zhaoheng and Wen, Hongzhi and Wang, Yixin and Venegas, Julian and Jin, Wei and Liu, Renming and Su, Runze and others},\n  journal={ACM Transactions on Intelligent Systems and Technology},\n  volume={15},\n  number={3},\n  pages={1--62},\n  year={2024},\n  publisher={ACM New York, NY}\n}\n```\n\n\n## For the foundation model for single-cell, more papers are recorded [[HERE]](https:\u002F\u002Fgithub.com\u002FOmicsML\u002Fawesome-foundation-model-single-cell-papers).\n\n- [Book](#book)\n- [Single Cell Technology](#single-cell-techonoly)\n- [Course](#course)\n- [Survey](#survey)\n- [Pretrained Model or LLM or Foundation Model](#pretrained-model-or-llm-or-foundation-model)\n- [GAN or Diffusion Model](#gan-or-diffusion-model)\n- [Multimodal Learning](#multimodal-learning)\n- [Single Cell Data Simulation](#single-cell-data-simulation)\n- [Interpretability](#interpretability)\n- [Spatialtemporal Transcriptomic](#spatialtemporal-transcriptomic)\n- [RNA Velocity](#rna-velocity)\n- [Molecular Representation Learning](#molecular-representation-learning)\n- [Single Cell Perturbation or Drug Response](#single-cell-perturbation-or-drug-response)\n- [Cellular Dynamics](#cellular-dynamics)\n- [Single Cell Application](#single-cell-application)\n- [Tools For Single Cell or Spatial Data](#tools-for-single-cell-or-spatial-data)\n- [Single Cell Atlas](#single-cell-atlas)\n- [Single Cell Visualization](#single-cell-visualization)\n- [Benchmarking](#benchmarking)\n- [Metric Design](#metric-design)\n- [Subcellular Analysis](#subcellular-analysis)\n- [Dimensionality Reduction and Visualization](#dimensionality-reduction-and-visualization)\n- [Representation Learning](#representation-learning)\n- [Batch Effect Correction](#batch-effect-correction)\n- [Tumor Microenvironment-TME](#tumor-microenvironment-tme)\n- [Cell-Cell Communication Events](#cell-cell-communication-events)\n- [Gene Regulatory Network](#gene-regulatory-network)\n- [Imputation](#imputation)\n- [Spatial Domain](#spatial-domain)\n- [Reference Embedding or Transfer Learning](#reference-embedding-or-transfer-learning)\n- [Cell Segmentation](#cell-segmentation)\n- [Cell Type Deconvolution](#cell-type-deconvolution)\n- [Cell Type Annotation](#cell-type-annotation)\n- [Cell Clustering](#cell-clustering)\n- [Disease Prediction](#disease-prediction)\n- [Multimodal Integration](#multimodal-integration)\n- [Multiomics Translation](#multiomics-translation)\n\n## Book\n1. [[Single Cell Best Practices]](https:\u002F\u002Fwww.sc-best-practices.org\u002Fpreamble.html), Fabian Theis's Lab\n1. [[Basics of Single-Cell Analysis with Bioconductor]](http:\u002F\u002Fbioconductor.org\u002Fbooks\u002F3.15\u002FOSCA.basic\u002Findex.html), Bioconductor software based on R\n\n## Single Cell Technology\n### Single-Modality\n\n### Multimodality\n\n### Spatial Transcriptomic\n1. [2022 Nature Methods] **Museum of spatial transcriptomics** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-022-01409-2)\n\n## Course\n1. [[CSCI 1850 Deep Learning in Genomics]](https:\u002F\u002Fcs.brown.edu\u002Fcourses\u002Fcsci1850\u002Findex.html), Brown University\n1. [[Machine Learning in Genomics: Dissecting Human Disease Circuitry]](http:\u002F\u002Fstellar.mit.edu\u002FS\u002Fcourse\u002F6\u002Ffa21\u002F6.047\u002Fmaterials.html), MIT\n1. [[ANALYSIS OF SINGLE CELL RNA-SEQ DATA]](https:\u002F\u002Fbroadinstitute.github.io\u002F2019_scWorkshop\u002Findex.html), course by Orr Ashenberg, Dana Silverbush, Kirk Gosik\n1. [[Analysis of single cell RNA-seq data, www.singlecellcourse.org]](https:\u002F\u002Fscrnaseq-course.cog.sanger.ac.uk\u002Fwebsite\u002Findex.html) - step-by-step scRNA-seq analysis course. R-based, with code examples, explanations, exercises. From alignment (STAR) and QC (FASTQC) to introduction to R, SingleCellExperiment class, `scater` object, data exploration (reads, UMI), filtering, normalization (`scran`), batch effect removal (`RUV`, `ComBat`, `mnnCorrect`, GLM, `Harmony`), clustering and marker gene identification (`SINCERA`, `SC3`, tSNE, `Seurat`), feature selection (`M3Drop::M3DropConvertData`, `BrenneckeGetVariableGenes`), pseudotime analysis (`TSCAN`, `Monocle`, diffusion maps, `SLICER`, `Ouija`, `destiny`), imputation (`scImpute`, `DrImpute`, `MAGIC`), differential expression (Kolmogorov-Smirnov, Wilcoxon, `edgeR`, `Monocle`, `MAST`), data integration (`scmap`, cell-to-cell mapping, `Metaneighbour`, `mnnCorrect`, `Seurat`'s canonical correllation analysis). Search for scRNA-seq data ([scfind](https:\u002F\u002Fgithub.com\u002Fhemberg-lab\u002Fscfind) R package), as well as [Hemberg group’s public datasets](https:\u002F\u002Fhemberg-lab.github.io\u002FscRNA.seq.datasets\u002F). [Seurat chapter](https:\u002F\u002Fscrnaseq-course.cog.sanger.ac.uk\u002Fwebsite\u002Fseurat-chapter.html). [\"Ideal\" scRNA-seq pipeline](https:\u002F\u002Fscrnaseq-course.cog.sanger.ac.uk\u002Fwebsite\u002Fideal-scrnaseq-pipeline-as-of-oct-2017.html). [Video lectures](https:\u002F\u002Fwww.youtube.com\u002Fwatch?list=PLEyKDyF1qdOYAhwU71qlrOXYsYHtyIu8n&v=56n77bpjiKo). \u003Cdetails>\n    \u003Csummary>Paper\u003C\u002Fsummary>\n    Andrews, Tallulah S., Vladimir Yu Kiselev, Davis McCarthy, and Martin Hemberg. \"Tutorial: Guidelines for the Computational Analysis of Single-Cell RNA Sequencing Data.\" https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41596-020-00409-w Nature Protocols, December 7, 2020. \n\u003C\u002Fdetails>\n\n## Survey\n1. [2023 Biophysics Reviews] **Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing** [[paper]](https:\u002F\u002Fpubs.aip.org\u002Faip\u002Fbpr\u002Farticle\u002F4\u002F1\u002F011306\u002F2879089\u002FDeep-learning-in-spatial-transcriptomics-Learning)\n\n\n## Pretrained Model or LLM or Foundation Model\n**Refer more details to** [[foundation-model-single-cell-papers]](https:\u002F\u002Fgithub.com\u002FOmicsML\u002Ffoundation-model-single-cell-papers)\n1. [2024 BioRxiv] **scPRINT: pre-training on 50 million cells allows robust gene network predictions** [[paper](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2024.07.29.605556v1)]\n1. [2024 ICLR] **BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=jJCeMiwHdH)\n1. [2023 bioRxiv] **CellPLM: Pre-training of Cell Language Model Beyond Single Cells** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.10.03.560734v1)\n1. [2023 bioRxiv] **DNABERT-2: EFFICIENT FOUNDATION MODEL AND BENCHMARK FOR MULTI-SPECIES GENOME** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.15006.pdf)\n1. [2023 bioRxiv] **The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.07361)\n1. [2023 bioRxiv] **Augmenting large language models with chemistry tools** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2304.05376.pdf)\n1. [2023 bioRxiv] **GET: a foundation model of transcription across human cell types** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.09.24.559168v1)\n1. [2023 bioRxiv] **Cell2Sentence: Teaching Large Language Models the Language of Biology** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.09.11.557287v1)\n1. [2023 bioRxiv] **Evaluating the Utilities of Large Language Models in Single-cell Data Analysis** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.09.08.555192v2)\n1. [2023 arxiv] **Towards Generalist Biomedical AI** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.14334)\n1. [2023 bioRxiv] **Contextualizing protein representations using deep learning on protein networks and single-cell data** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.07.18.549602v1)\n1. [2023 Nature] **Large language models encode clinical knowledge** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-023-06291-2)\n1. [2023 Nature Methods] **Towards foundation models of biological image segmentation** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-01885-0)\n1. [2023 bioRxiv] **DrugGPT: A GPT-based Strategy for Designing Potential Ligands Targeting Specific Proteins** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.06.29.543848v1)\n1. [2023 arxiv] **Hyena Hierarchy: Towards Larger Convolutional Language Models** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.10866)\n1. [2023 bioRxiv] **Population-level integration of single-cell datasets enables multi-scale analysis across samples** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.11.28.517803v1)\n1. [2023 bioRxiv] **Large Scale Foundation Model on Single-cell Transcriptomics** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.05.29.542705v2)\n1. [2023 Bioinformatics] **Applications of transformer-based language models in bioinformatics: a survey** [[paper]](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F36845200\u002F)\n1. [2023 Nature] **Transfer learning enables predictions in network biology** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-023-06139-9)\n1. [2023 arxiv] **BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.17100)\n1. [2023 arxiv] **Clinical Camel: An Open-Source Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.12031)\n1. [2023 arxiv] **CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language Models** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.10946)\n1. [2023 iSchience tGPT] **Generative pretraining from large-scale transcriptomes for single-cell deciphering** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2589004223006132)\n1. [2023 bioRxiv] **GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.09667)\n1. [2023 Github] **OpenBioMed** [[Github]](https:\u002F\u002Fgithub.com\u002FBioFM\u002FOpenBioMed)\n1. [2023 blog] **BioMedLM: a Domain-Specific Large Language Model for Biomedical Text** [[blog]](https:\u002F\u002Fwww.mosaicml.com\u002Fblog\u002Fintroducing-pubmed-gpt)\n1. [2023 bioRxiv] **scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.04.30.538439v1)\n1. [2023 bioRxiv] **xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.03.24.534055v1)\n1. [2023 Nature Biotechnology] **Large language models generate functional protein sequences across diverse families** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-022-01618-2)\n1. [2022 arxiv] **A single-cell gene expression language model** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.14330)\n1. [2022 Briefings in Bioinformatics] **BioGPT: generative pre-trained transformer for biomedical text generation and mining** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fbib\u002Farticle\u002F23\u002F6\u002Fbbac409\u002F6713511?guestAccessKey=a66d9b5d-4f83-4017-bb52-405815c907b9&login=true)\n1. [2022 Nature Machine Intelligence] **scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-022-00534-z)\n1. [2022 bioRxiv] **scFormer: a universal representation learning approach for single-cell data using transformers** [[paper]](https:\u002F\u002Fopenreview.net\u002Fpdf?id=7hdmA0qtr5)\n1. [2022 Bioinformatics] **scPretrain: multi-task self-supervised learning for cell-type classification** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F38\u002F6\u002F1607\u002F6499287)\n1. [2021 PNAS] **Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences** [[paper]](https:\u002F\u002Fwww.pnas.org\u002Fdoi\u002F10.1073\u002Fpnas.2016239118)\n1. [2021 Bioinformatics] **DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F37\u002F15\u002F2112\u002F6128680)\n1. [2021 Arxiv, 576 citations] **Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2004.10964.pdf)\n1. [2021 Arxiv, 1111 citations] **Don't Stop Pretraining: Adapt Language Models to Domains and Tasks** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2004.10964.pdf)\n\n## GAN or Diffusion Model\n1. [2024 Brief Bioinform] **stDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics** [[paper]](https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC11021815\u002F)\n1. [2024 biorxiv] **scDiffEq: drift-diffusion modeling of single-cell dynamics with neural stochastic differential equations** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.12.06.570508v1)\n1. [2024 biorxiv] **scDiffusion: conditional generation of high-quality single-cell data using diffusion model** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.03968)\n1. [2024 biorxiv] **In Silico Generation of Gene Expression profiles using Diffusion Models** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2024.04.10.588825v1)\n1. [2024 Cell] **A programmable reaction-diffusion system for spatiotemporal cell signaling circuit design** [[paper]](https:\u002F\u002Fwww.cell.com\u002Fcell\u002Fpdf\u002FS0092-8674(23)01339-9.pdf)\n1. [2023 ICCV] **Scalable Diffusion Models with Transformers** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.09748)\n1. [2023 biorxiv] **From Noise to Knowledge: Probabilistic Diffusion-Based Neural Inference of Gene Regulatory Networks** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.11.05.565675v1)\n1. [2023 biorxiv Diffusion] **A General Single-Cell Analysis Framework via Conditional Diffusion Generative Models** [[paper]](https:\u002F\u002Fscholar.google.com\u002Fcitations?view_op=view_citation&hl=en&user=7lwkXGEAAAAJ&citation_for_view=7lwkXGEAAAAJ:Se3iqnhoufwC)\n1. [2023 biorxiv GAN] **Predicting cell morphological responses to perturbations using generative modeling** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.07.17.549216v1)\n1. [2023 Nature Diffusion Model] **AI tools are designing entirely new proteins that could transform medicine** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fd41586-023-02227-y)\n1. [2023 biorxiv Diffusion Model] **The Power of Two: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics analysis** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.04.13.536789v1)\n1. [2023 biorxiv GAN] **Scalable Integration of Multiomic Single Cell Data Using Generative Adversarial Networks** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.06.26.546547v2)\n1. [2023 biorxiv Diffusion Model] **Spontanously breaking of symmetry in overlapping cell instance segmentation using diffusion models** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.07.07.548066v1)\n        \n\n## Multimodal Learning\n1. [2024 ICLR workshop NLP+Gene Expression] **Joint Embedding of Transcriptomes and Text Enables Interactive Single-Cell RNA-seq Data Exploration via Natural Language** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=yWiZaE4k3K)\n1. [2024 Nature Biotechnology Image+Gene Expression] **Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-023-02019-9#citeas)\n1. [2023 arxiv Image+Gene Expression] **Transformer with Convolution and Graph-Node co-embedding: An accurate and interpretable vision backbone for predicting gene expressions from local histopathological image** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.05.28.542669v1)\n1. [2023 arxiv multimodal] **MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.02275)\n1. [2023 biorxiv multimodal] **Pathformer: biological pathway informed Transformer model integrating multi-modal data of cancer** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.05.23.541554v1)\n1. [2023 biorxiv Image+Gene Expression] **Spatially Resolved Gene Expression Prediction from H&E Histology Images via Bi-modal Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.01859.pdf)\n1. [2023 biorxiv Image+Gene Expression] **Single-cell gene expression prediction using H&E images based on spatial transcriptomics** [[paper]](https:\u002F\u002Fwww.spiedigitallibrary.org\u002Fconference-proceedings-of-spie\u002F12471\u002F1247105\u002FSingle-cell-gene-expression-prediction-using-HE-images-based-on\u002F10.1117\u002F12.2654294.full?SSO=1)\n\n## Single Cell Data Simulation\n1. [2025 NM] **scMultiSim: simulation of single-cell multi-omics and spatial data guided by gene regulatory networks and cell–cell interactions** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-025-02651-0)\n1. [2023 NBT] **scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-023-01772-1)\n1. [2023 NC] **scReadSim: a single-cell RNA-seq and ATAC-seq read simulator** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-43162-w)\n1. [2023 biorxiv] **GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.07.25.550225v1)\n1. [2022 JCB] **Simulating Single-Cell Gene Expression Count Data with Preserved Gene Correlations by scDesign2** [[paper]](https:\u002F\u002Fwww.liebertpub.com\u002Fdoi\u002Fabs\u002F10.1089\u002Fcmb.2021.0440)\n1. [2021 GB] **scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured** [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1186\u002Fs13059-021-02367-2)\n1. [2019 Bioinformatics] **A statistical simulator scDesign for rational scRNA-seq experimental design** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F35\u002F14\u002Fi41\u002F5529133)\n\n\n\n## Interpretability\n1. [2021 CVPR] **Transformer Interpretability Beyond Attention Visualization** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.09838)[[github]](https:\u002F\u002Fgithub.com\u002Fhila-chefer\u002FTransformer-Explainability)\n1. [2021 ICML] **BERTology Meets Biology: Interpreting Attention in Protein Language Models** [[paper]](https:\u002F\u002Fopenreview.net\u002Fpdf?id=YWtLZvLmud7)\n1. [2019 ACL] **A Multiscale Visualization of Attention in the Transformer Model** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.05714.pdf) [[github]](https:\u002F\u002Fgithub.com\u002Fjessevig\u002Fbertviz\u002Ftree\u002Fmaster)\n\n\n## Spatialtemporal Transcriptomic\n1. [2024 biorxiv] **Gene Trajectory Inference for Single-cell Data by Optimal Transport Metrics** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.07.08.499404v3)\n1. [2023 biorxiv] **Uncovering developmental time and tempo using deep learning** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-02083-8)\n1. [2023 biorxiv] **scNODE: Generative Model for Temporal Single Cell Transcriptomic Data Prediction** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.11.22.568346v1.full.pdf)\n1. [2023 biorxiv] **Gene Trajectory Inference for Single-cell Data by Optimal Transport Metrics** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.07.08.499404v3)\n1. [2023 arxiv survey from CS field] **Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.10196)\n1. [2023 ICML Reference from CS field] **Continuous Spatiotemporal Transformers** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.13338)\n1. [2023 arxiv multimodalities Reference from CS field] **IMAGEBIND: One Embedding Space To Bind Them All** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.05665.pdf)\n1. [2023 arxiv multimodalities Reference from CS field] **UnIVAL: Unified Model for Image, Video, Audio and Language Tasks** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2307.16184.pdf)\n1. [2023 arxiv multimodalities Reference from CS field] **Meta-Transformer: A Unified Framework for Multimodal Learning** [[Meta-Transformer paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2307.10802.pdf)[[viT vision Transformer paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.11929)[[ImageGPT paper Generative Pretraining From Pixels]](https:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fchen20s.html)\n1. [2023 KDD Reference from CS field] **Spatio-temporal Diffusion Point Processes** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.12403.pdf)\n1. [2023 arxiv Reference from CS field] **Long-Range Transformers for Dynamic Spatiotemporal Forecasting** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.12218.pdf)\n1. [2023 Nature Communications] **Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-021-23518-w)\n1. [2023 bioRxiv] **Mapping cells through time and space with moscot** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.05.11.540374v1)        \n1. [2023 Nature Methods] **Spatiotemporally resolved transcriptomics reveals the subcellular RNA kinetic landscape** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-01829-8)\n1. [2022 bioRxiv] **Spateo: multidimensional spatiotemporal modeling of single-cell spatial transcriptomics** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.12.07.519417v1.abstract)\n1. [2022 ICLR Reference from CS field] **UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.04676)\n1. [2022 NeurIPS spatial-temporal single-cell -> spatial-temporal video] **Flamingo: a Visual Language Model for Few-Shot Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.14198)\n1. [2022 arxiv, image-gene expression contrastive learning] **CoCa: Contrastive Captioners are Image-Text Foundation Models** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.01917.pdf)\n1. [2020 ICLR, image-gene expression pretraining] **VL-BERT: Pre-training of Generic Visual-Linguistic Representations** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.08530.pdf)\n1. [2019 AAAI, image-gene expression pretraining] **Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.06066.pdf)\n\n## RNA Velocity\n1. [2023 Nature Methods] **Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-01994-w)\n\n\n\n## Molecular Representation Learning\n1. [2023 ICLR] **Uni-Mol: A Universal 3D Molecular Representation Learning Framework** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=6K2RM6wVqKu)\n\n## Single Cell Perturbation or Drug Response\n1. [2024 NeurIPS AIDrugX (Spotlight)] **Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for Therapeutics** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=kL8dlYp6IM) [[poster]](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1plypydZCaegbgxyCl-xehFxSgwX6e8So\u002Fview)\n2. [2024 biorxiv] **Deep learning-based predictions of gene perturbation effects do not yet outperform simple linear methods** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2024\u002F09\u002F21\u002F2024.09.16.613342.full.pdf)\n1. [2024 ICLR] **Biologically Interpretable VAE with Supervision for Transcriptomics Data Under Ordinal Perturbations** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-02144-y)\n1. [2024 Nature Methods] **scPerturb: harmonized single-cell perturbation data** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-02144-y)\n1. [2023 biorxiv] **Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases** [[paper]](https:\u002F\u002Fassets.researchsquare.com\u002Ffiles\u002Frs-3676579\u002Fv1_covered_2dc4a452-a1f2-47a2-acb1-f816276a9e07.pdf?c=1702865288)\n1. [2023 NeurIPS] **Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=DzaCE00jGV)\n1. [2023 Nature Methods] **Learning single-cell perturbation responses using neural optimal transport** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-01969-x)\n1. [2023 Nature Methods] **Neural optimal transport predicts perturbation responses at the single-cell level** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-01968-y)\n1. [2023 Mol Syst Biol] **Predicting cellular responses to complex perturbations in high-throughput screens** [[paper]](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F37154091\u002F)\n1. [2023 biorxiv] **Learning Perturbation-specific Cell Representations for Prediction of Transcriptional Response across Cellular Contexts** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.03.20.533433v1)\n1. [2023 Nature] **Dissecting cell identity via network inference and in silico gene perturbation** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-022-05688-9)\n1. [2023 biorxiv Diffusion Model] **The Power of Two: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics analysis** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.04.13.536789v1)\n1. [2023 ICLR] **Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=ICYasJBlZNs)\n1. [2022 arxiv] **PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.07.20.500854v2)\n1. [2022 arxiv] **CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.17283)\n1. [2022 NeurIPS] **Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.13545.pdf)\n1. [2022 biorxiv] **GEARS: Predicting transcriptional outcomes of novel multi-gene perturbations** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.07.12.499735v2)\n1. [2021 biorxiv] **Learning interpretable cellular responses to complex perturbations in high-throughput screens** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2021.04.14.439903v2)\n1. [2019 Nature Methods] **scGen predicts single-cell perturbation responses** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-019-0494-8)\n\n\n## Cellular Dynamics\n1. [2023 Genome Biology] **scTour: a deep learning architecture for robust inference and accurate prediction of cellular dynamics** [[paper]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-023-02988-9)\n        \n        \n\n## Single Cell Application\n1. [2023 medrxiv] **Single-cell RNA sequencing of human tissue supports successful drug targets** [[paper]](https:\u002F\u002Fwww.medrxiv.org\u002Fcontent\u002F10.1101\u002F2024.04.04.24305313v1)\n1. [2023 Nature Methods] **Machine learning in rare disease** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-01886-z)\n1. [2023 Molecular System Biology] **Single-cell biology: what does the future hold?** [[paper]](https:\u002F\u002Fwww.embopress.org\u002Fdoi\u002Ffull\u002F10.15252\u002Fmsb.202311799)\n1. [2023 Genes] **Single-Cell Analysis in the Omics Era: Technologies and Applications in Cancer** [[paper]](https:\u002F\u002Fwww.mdpi.com\u002F2073-4425\u002F14\u002F7\u002F1330)\n1. [2023 Nature Communications] **ASGARD is A Single-cell Guided Pipeline to Aid Repurposing of Drugs** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-36637-3)\n1. [2023 Nature Reviews Clinical Oncology] **Advancing CAR T cell therapy through the use of multidimensional omics data** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41571-023-00729-2)\n\n## Tools For Single Cell or Spatial Data\n[[Tool Summary]](https:\u002F\u002Fwww.scrna-tools.org\u002Ftools)\n1. [2024 biorxiv] **Scvi-hub: an actionable repository for model-driven single cell analysis** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2024.03.01.582887v1.full.pdf)\n1. [2024 Nature Methods] **SpatialData: an open and universal data framework for spatial omics** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-024-02212-x)\n1. [2023 Nucleic Acids Research] **DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fnar\u002Fadvance-article\u002Fdoi\u002F10.1093\u002Fnar\u002Fgkad055\u002F7041952)\n1. [2023 Github] **SpatialTis: an ultra-fast spatial analysis toolkit for large-scale spatial single-cell data.** [[github]](https:\u002F\u002Fgithub.com\u002FMr-Milk\u002FSpatialTis)\n1. [2023 biorxiv] **CellContrast: Reconstructing Spatial Relationships in Single-Cell RNA Sequencing Data via Deep Contrastive Learning** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.10.12.562026v1)\n\n\n## Single Cell Atlas\n1. [2023 Nature] **A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-023-06812-z)\n1. [2023 Nature] **A spatially resolved single-cell genomic atlas of the adult human breast** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-023-06252-9)\n1. [2023 Nature Medicine] **An integrated cell atlas of the lung in health and disease** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41591-023-02327-2)\n1. [2022 Nucleic Acids Research] **Aquila: a spatial omics database and analysis platform** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fnar\u002Farticle\u002F51\u002FD1\u002FD827\u002F6761736)\n1. [[Cellxgene Datasets: 546 datasets by 2022]](https:\u002F\u002Fcellxgene.cziscience.com\u002Fdatasets)\n1. [2022 Nature Methods] **Benchmarking atlas-level data integration in single-cell genomics** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-021-01336-8.pdf)\n1. [2022 bioRxiv] **A unified analysis of atlas single cell data** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.08.06.503038v1.full.pdf)\n1. [2022 Nature Biotechnology] **Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-021-01006-2)\n1. [2022 bioRxiv] **Supervised spatial inference of dissociated single-cell data with SageNet** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.04.14.488419v1)  \n1. [2022 Nature Communications] **Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-33758-z)\n\n\n## Single Cell Visualization\n1. [[Chanzuckerberg: An interactive explorer for single-cell transcriptomics data]](https:\u002F\u002Fgithub.com\u002Fchanzuckerberg\u002Fcellxgene)\n1. [[UCSC Cell Browser]](http:\u002F\u002Fcells.ucsc.edu\u002F)\n1. [[Cytoscape]](https:\u002F\u002Fcytoscape.org\u002F)\n1. [[UCSC Xena]](https:\u002F\u002Fxena.ucsc.edu\u002F)\n1. [[ASAP: Automated Single-cell Analysis Pipeline]](https:\u002F\u002Fasap.epfl.ch\u002F)\n1. [[GenePattern]](https:\u002F\u002Fnotebook.genepattern.org\u002F)\n1. [[Loopy Browser]](https:\u002F\u002Floopybrowser.com\u002F)\n\n## Benchmarking\n1. [2024 MoML@Mila] **** [[CMT submission]](https:\u002F\u002Fcmt3.research.microsoft.com\u002FMoML2024\u002FSubmission\u002FSummary\u002F13) [[preprint]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2024.06.12.598655v2) [[poster]](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1LYdITeFY5iX07zyXPGVEjMpYjuHMrneS\u002Fview) [[conference]](https:\u002F\u002Fportal.ml4dd.com\u002Fmoml-2024)\n2. [2023 biorxiv] **Benchmarking the translational potential of spatial gene expression prediction from histology** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.12.12.571251v1)\n1. [2023 bioRxiv] **Systematic benchmarking of imaging spatial transcriptomics platforms in FFPE tissues** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.12.07.570603v1)\n1. [2023 bioRxiv] **Benchmarking multi-omics integration algorithms across single-cell RNA and ATAC data** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.11.15.564963v1)\n1. [2023 bioRxiv] **BEND: Benchmarking DNA Language Models on biologically meaningful tasks** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.12570)\n1. [2023 Genome Biology] **Benchmarking algorithms for joint integration of unpaired and paired single-cell RNA-seq and ATAC-seq data** [[paper]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-023-03073-x)\n1. [2023 Nature Communications] **A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-37168-7)\n1. [2023 bioRxiv] **Universal preprocessing of single-cell genomics data** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.09.14.543267v1.full.pdf)\n1. [2023 Genome Biology] **Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability** [[paper]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-023-02962-5)\n1. [2023 Nature Communications] **A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-37168-7)\n1. [2023 bioRxiv] **Benchmarking the Autoencoder Design for Imputing Single-Cell RNA Sequencing Data** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.02.16.528866v1.abstract)\n1. [2023 bioRxiv] **Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.01.14.524081v1)\n1. [2022 Nature Communications] **Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-30755-0)\n1. [2022 Nature Methods] **Benchmarking atlas-level data integration in single-cell genomics** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-021-01336-8.pdf)\n1. [2022 Nature Methods] **Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-022-01480-9)\n1. [2022 BioRxiv] **Benchmarking Automated Cell Type Annotation Tools for Single-cell ATAC-seq Data** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.10.05.511014v1)\n1. [2022 Briefings in Bioinformatics] **Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fbib\u002Farticle\u002F23\u002F5\u002Fbbac286\u002F6649780)\n1. [2022 Nucleic Acids Research] **scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fnar\u002Farticle\u002F50\u002F9\u002F4877\u002F6582166)\n1. [2021 Frontiers in Genetics] **Evaluating the Reproducibility of Single-Cell Gene Regulatory Network Inference Algorithms** [[paper]](https:\u002F\u002Fwww.frontiersin.org\u002Farticles\u002F10.3389\u002Ffgene.2021.617282\u002Ffull)\n1. [2021 Nature Communications] **A benchmark study of simulation methods for single-cell RNA sequencing data** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-021-27130-w)\n1. [2021 Genome Biology] **Benchmarking UMI-based single-cell RNA-seq preprocessing workflows** [[paper]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-021-02552-3)\n1. [2020 Nature Methods] **Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-019-0690-6)\n1. [2020 Genome Biology] **A benchmark of batch-effect correction methods for single-cell RNA sequencing data** [[paper]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-019-1850-9)\n1. [2020 Nature Biotechnology] **A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-020-00748-9)\n1. [2019 Nature Methods] **Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-019-0425-8)\n\n## Metric Design\n1. [2019 Narure Methods] **A test metric for assessing single-cell RNA-seq batch correction** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-018-0254-1)\n\n## Subcellular Analysis\n1. [2024 Nature Communications] **BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-44560-w)\n1. [2023 Nature Methods] **Spatiotemporally resolved transcriptomics reveals the subcellular RNA kinetic landscape** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-01829-8)\n1. [2023 biorxiv] **Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.09.19.558548v1.full.pdf)\n1. [2023 Bioinformatics] **FISHFactor: a probabilistic factor model for spatial transcriptomics data with subcellular resolution** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F39\u002F5\u002Fbtad183\u002F7114027)\n1. [2023 Science] **Spatially resolved single-cell translatomics at molecular resolution** [[paper]](https:\u002F\u002Fwww.science.org\u002Fdoi\u002F10.1126\u002Fscience.add3067)\n1. [2023 Nature Methods] **Subcellular omics: a new frontier pushing the limits of resolution, complexity and throughput** [[paper]](https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC10049458\u002Fpdf\u002Fnihms-1881939.pdf)\n1. [2022 BioRxiv] **Bento: A toolkit for subcellular analysis of spatial transcriptomics data** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.06.10.495510v1)\n1. [2022 BioRxiv] **Subcellular spatially resolved gene neighborhood networks in single cells** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.08.03.502409v1)\n1. [2022 bioRxiv] **Statistical analysis supports pervasive RNA subcellular localization and alternative 3’ UTR regulation** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2022\u002F10\u002F27\u002F2022.10.26.513902.full.pdf)\n1. [2019 Cell] **Atlas of Subcellular RNA Localization Revealed by APEX-Seq** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0092867419305550?via%3Dihub)\n\n\n\n## Dimensionality Reduction and Visualization\n1. [2023 Genome Research] **Complex hierarchical structures in single-cell genomics data unveiled by deep hyperbolic manifold learning** [[paper]](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F36849204\u002F)\n1. [2021 Nature Communications] **Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-021-22851-4)\n1. [2018 Nature Communications] **Interpretable dimensionality reduction of single cell transcriptome data with deep generative models** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-018-04368-5)\n\n\n## Representation Learning\n1. [2025 arxiv] **SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2412.01124)\n1. [2023 Nature Machine Intelligence] **Reusability report: Learning the transcriptional grammar in single-cell RNA-sequencing data using transformers** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-023-00757-8)\n1. [2023 Genome Biology] **Correcting gradient-based interpretations of deep neural networks for genomics** [[paper]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-023-02956-3)\n1. [2023 Nature Methods] **SIMBA: single-cell embedding along with features** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-01899-8)\n1. [2023 bioRxiv] **Towards Universal Cell Embeddings: Integrating Single-cell RNA-seq Datasets across Species with SATURN** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.02.03.526939v1?rss=1)\n1. [2021 Current Opinion in Systems Biology] **Graph representation learning for single-cell biology** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2452310021000329)\n1. [2020 Nature Communications] **Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-019-14018-z)\n1. [2019 Nature Methods] **Data denoising with transfer learning in single-cell transcriptomics** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-019-0537-1)\n1. [2018 Nature Methods] **Deep generative modeling for single-cell transcriptomics** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-018-0229-2)\n\n\n## Batch Effect Correction\n1. [2023 Bioinformatics] **CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Fadvance-article\u002Fdoi\u002F10.1093\u002Fbioinformatics\u002Fbtad099\u002F7055295)\n1. [2020 Genomy Biology] **A benchmark of batch-effect correction methods for single-cell RNA sequencing data** [[paper]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-019-1850-9)\n1. [2020 Nature Biotechnology] **A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-020-00748-9)\n1. [2019 Nature Methods, **Harmony**] **Fast, sensitive and accurate integration of single-cell data with Harmony** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-019-0619-0)\n1. [2018 Nature Biotechnology, **CCA**] **Integrating single-cell transcriptomic data across different conditions, technologies, and species** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fnbt.4096)\n1. [2018 Nature Biotechnology, **Mutual Nearest Neighbors**] **Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fnbt.4091)\n1. [2018 Nature Methods] **A test metric for assessing single-cell RNA-seq batch correction** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-018-0254-1)\n1. [2017 Nature Biotechnology] **Multiplexed droplet single-cell RNA-sequencing using natural genetic variation** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fnbt.4042)\n\n## Tumor Microenvironment-TME\n1. [2023 bioRxiv] **Identifying Spatial Co-occurrence in Healthy and InflAmed tissues (ISCHIA)** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.02.13.526554v1)\n1. [2023 bioRxiv] **Predicting tumor immune microenvironment and checkpoint therapy response of head & neck cancer patients from blood immune single-cell transcriptomics** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.01.17.524455v1)\n1. [2022 Nature Biomedical Engineering] **Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41551-022-00951-w)\n1. [2022 Nature Communications] **SOTIP is a versatile method for microenvironment modeling with spatial omics data** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-34867-5)\n\n\n\n## Cell-Cell Communication Events\n1. [2024 Nature Methods] **Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-02124-2)\n1. [2024 Nature Reviews Genetics] **The diversification of methods for studying cell–cell interactions and communication** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41576-023-00685-8)\n1. [2024 bioRxiv] **Large-scale characterization of cell niches in spatial atlases using bio-inspired graph learning** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2024.02.21.581428v2.full.pdf)\n1. [2024 Pac Symp Biocomput] **PEPSI: Polarity measurements from spatial proteomics imaging suggest immune cell engagement** [[paper]](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F38160302\u002F)\n1. [2023 Cell Systems] **Single-cell A\u002FB testing for cell-cell communication** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2405471223001503)\n1. [2023 Nature Biotechnology] **Inferring cell–cell communication at single-cell resolution** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-023-01834-4)\n1. [2022 bioRxiv] **scTensor detects many-to-many cell–cell interactions from single cell RNA-sequencing data** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.12.07.519225v1)\n1. [2022 Nature Biotechnology] **Modeling intercellular communication in tissues using spatial graphs of cells** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-022-01467-z)\n1. [2022 bioRxiv] **Decoding functional cell–cell communication events by multi-view graph\nlearning on spatial transcriptomics** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.06.22.496105v1)\n1. [2021 Bioinformatics] **Identifying signaling genes in spatial single-cell expression data** [[paper]](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F32886099\u002F)\n1. [2020 Nature Methods] **NicheNet: modeling intercellular communication by linking ligands to target genes** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-019-0667-5)\n1. [2020 Nature Communications] **Predicting cell-to-cell communication networks using NATMI** [[paper]](https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC7538930\u002Fpdf\u002F41467_2020_Article_18873.pdf)\n1. [2018 Nature] **Single-cell reconstruction of the early maternal–fetal interface in humans** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-018-0698-6)\n\n\n## Gene Regulatory Network\n1. [2023 arxiv] **DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.04178.pdf)\n1. [2023 Bioinformatics] **STGRNS: an interpretable transformer-based method for inferring gene regulatory networks from single-cell transcriptomic data** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F39\u002F4\u002Fbtad165\u002F7099621)\n1. [2022 Nature Machine Intelligence] **Inferring transcription factor regulatory networks from single-cell ATAC-seq data based on graph neural networks** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-022-00469-5)\n1. [2022 Nature Biotechnology] **Multi-omics single-cell data integration and regulatory inference with graph-linked embedding** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-022-01284-4.pdf)\n1. [2022 Biorxiv] **scMEGA: Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.08.10.503335v1)\n1. [2022 Bioinformatics] **High-performance single-cell gene regulatory network inference at scale: the Inferelator 3.0** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F38\u002F9\u002F2519\u002F6533443)\n1. [2022 Briefings in Bioinformatic] **SIGNET: single-cell RNA-seq-based gene regulatory network prediction using multiple-layer perceptron bagging** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fbib\u002Farticle\u002F23\u002F1\u002Fbbab547\u002F6484519)\n1. [2020 Nature Methods] **Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-019-0690-6)\n1. [2019 Genome Biology] **Single-cell transcriptomics unveils gene regulatory network plasticity** [[paper]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-019-1713-4)\n1. [2017 Cell Syst] **Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures** [[paper]](https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC5624513\u002F)\n\n\n## Imputation\n1. [2018 Nature Communications] **An accurate and robust imputation method scImpute for single-cell RNA-seq data** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-018-03405-7)\n1. [2019 Genome Biology] **DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data** [[paper]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-019-1837-6)\n1. [2018 Cell] **Recovering Gene Interactions from Single-Cell Data Using Data Diffusion** [[paper]](https:\u002F\u002Fwww.cell.com\u002Fcell\u002Ffulltext\u002FS0092-8674(18)30724-4)\n1. [2018 Genome Biology] **VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies** [[paper]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-018-1575-1)\n1. [2021 PLOS Computational Biology] **G2S3: A gene graph-based imputation method for single-cell RNA sequencing data** [[paper]](https:\u002F\u002Fjournals.plos.org\u002Fploscompbiol\u002Farticle?id=10.1371\u002Fjournal.pcbi.1009029)\n1. [2021 Nature Communications] **scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-021-22197-x#Sec23)\n1. [2021 iScience] **Imputing single-cell RNA-seq data by combining graph convolution and autoencoder neural networks** [[paper]](https:\u002F\u002Fwww.cell.com\u002Fiscience\u002Ffulltext\u002FS2589-0042(21)00361-8)\n1. [2022 PLOS ONE] **Single-cell specific and interpretable machine learning models for sparse scChIP-seq data imputation** [[paper]](https:\u002F\u002Fjournals.plos.org\u002Fplosone\u002Farticle?id=10.1371\u002Fjournal.pone.0270043)\n\n## Spatial Domain\n1. [2023 Nature Genetics] **SPICEMIX enables integrative single-cell spatial modeling of cell identity** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41588-022-01256-z)\n1. [2023 bioRxiv] **CellCharter: a scalable framework to chart and compare cell niches across multiple samples and spatial -omics technologies** [[preprint]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.01.10.523386v1)\n1. [2022 Genome Research] **A model-based constrained deep learning clustering approach for spatially resolved single-cell data** [[paper]](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F36198490\u002F)\n1. [2022 Nature Communications Biology] **Deciphering tissue structure and function using spatial transcriptomics** [[Review paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42003-022-03175-5)\n1. [2022 Genome Biology] **Statistical and machine learning methods for spatially resolved transcriptomics data analysis** [[Review paper]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Ftrack\u002Fpdf\u002F10.1186\u002Fs13059-022-02653-7.pdf)\n1. [2022 Nature Communications] **Deciphering spatial domains from spatially resolved transcriptomics with adaptive graph attention auto-encoder** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-29439-6)\n1. [2022 Nature Computational Science] **Cell clustering for spatial transcriptomics data with graph neural networks** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs43588-022-00266-5)\n1. [2022 Frontiers in Genetics] **Analysis and Visualization of Spatial Transcriptomic Data** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.07787.pdf)\n1. [2021 Nature Methods] **SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-021-01255-8)\n1. [2021 Nature Biotechnology] **Spatial transcriptomics at subspot resolution with BayesSpace** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-021-00935-2)\n1. [2021 Biorxiv] **Unsupervised Spatially Embedded Deep Representation of Spatial Transcriptomics** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2021.06.15.448542v2)\n1. [2021 Genome Biology] **Giotto: a toolbox for integrative analysis\nand visualization of spatial expression data** [[Tool]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Ftrack\u002Fpdf\u002F10.1186\u002Fs13059-021-02286-2.pdf)\n1. [2021 Biorxiv] **Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2021.07.08.451210v2)\n1. [2020 Biorxiv] **stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2020.05.31.125658v1)\n1. [2018 Nature Methods] **SpatialDE: Identification of Spatially Variable Genes** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fnmeth.4636)\n1. [2018 Nature Biotechnology] **Identification of Spatially Associated Subpopulations by Combining scRNAseq and Sequential Fluorescence In Situ Hybridization Data** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fnbt.4260)\n1. [2008 Journal of Statistical Mechanics] **Fast unfolding of community hierarchies in large networks** [[paper]](https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F1742-5468\u002F2008\u002F10\u002FP10008)\n\n\n## Reference Embedding or Transfer Learning\n1. [2019 Nature Methods] **Data denoising with transfer learning in single-cell transcriptomics** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-019-0537-1)\n1. [2018 Nature Methods] **Deep generative modeling for single-cell transcriptomics** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-018-0229-2)\n1. [2020 Bioinformatics] **Conditional out-of-distribution generation for unpaired data using transfer VAE** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F36\u002FSupplement_2\u002Fi610\u002F6055927?guestAccessKey=71253caa-1779-40e8-8597-c217db539fb5&login=false)\n1. [2021 Nature Biotechnology] **Mapping single-cell data to reference atlases by transfer learning** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-021-01001-7)\n1. [2021 Molecular Systems Biology] **Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models** [[paper]](https:\u002F\u002Fwww.embopress.org\u002Fdoi\u002Ffull\u002F10.15252\u002Fmsb.20209620)\n1. [2022 bioRxiv Preprint] **Biologically informed deep learning to infer gene program activity in single cells** [[preprint]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.02.05.479217v2)\n\n## Cell Segmentation\n1. [2023 biorxiv] **Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.09.19.558548v1.full.pdf)\n1. [2022 Cytometry A] **MIRIAM: A machine and deep learning single-cell segmentation and quantification pipeline for multi-dimensional tissue images** [[paper]](https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fepdf\u002F10.1002\u002Fcyto.a.24541)[[code]](https:\u002F\u002Fgithub.com\u002FCoffey-Lab\u002FMIRIAM)(MIRIAM)\n1. [2021 Nature Biotechnology] **Cell segmentation in imaging-based spatial transcriptomics** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-021-01044-w) \n1. [2021 Biorxiv] **Scellseg: a style-aware cell instance segmentation tool with pre-training and\ncontrastive fine-tuning** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2021.12.19.473392v1) [[code]](https:\u002F\u002Fgithub.com\u002Fcellimnet\u002Fscellseg-publish)\n1. [2021 Nature Biotechnology] **Cell segmentation in imaging-based spatial transcriptomics** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-021-01044-w) [[code]](https:\u002F\u002Fgithub.com\u002Fkharchenkolab\u002FBaysor)(Baysor)\n1. [2021 Nature Biotechnology] **Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-021-01094-0) [[code]](https:\u002F\u002Fgithub.com\u002Fvanvalenlab\u002Fintro-to-deepcell)(Memser)\n1. [2021 Nature Methods] **Cellpose: a generalist algorithm for cellular segmentation** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-020-01018-x) [[code]](https:\u002F\u002Fwww.github.com\u002Fmouseland\u002Fcellpose)(Cellpose)\n1. [2021 Molecular Systems Biology]**Joint cell segmentation and cell type annotation for spatial transcriptomics** [[paper]](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F34057817\u002F) [[code]](https:\u002F\u002Fgithub.com\u002Fwollmanlab\u002FJSTA) (JSTA)\n1. [2020 Nature Communications]**A convolutional neural network segments yeast microscopy images with high accuracy** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-020-19557-4) [[code]](http:\u002F\u002Fgithub.com\u002Flpbsscientist\u002FYeaZ-GUI)\n1. [2020 Medical Image Analysis] **DeepDistance: A multi-task deep regression model for cell detection in inverted microscopy images** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841520300840?via%3Dihub) (DeepDistance)\n1. [2016 Computational Biology]**Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments** [[paper]](https:\u002F\u002Fjournals.plos.org\u002Fploscompbiol\u002Farticle?id=10.1371\u002Fjournal.pcbi.1005177) [[code]](https:\u002F\u002Fgithub.com\u002Fvanvalenlab\u002Fdeepcell-tf) (Deepcell)\n\n## Cell Type Deconvolution\n1. [2023 Genome Biology] **Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data** [[paper]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-023-03138-x)\n1. [2023 BioRxiv] **RETROFIT: REFERENCE-FREE DECONVOLUTION OF CELL-TYPE MIXTURES IN SPATIAL TRANSCRIPTOMICS** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.06.07.544126v1)\n1. [2023 BioRxiv] **STdGCN: accurate cell-type deconvolution using graph convolutional networks in spatial transcriptomic data** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.03.10.532112v1)\n1. [2023 BioRxiv] **Spotless: a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.03.22.533802v1)\n1. [2022 Nature Biotechnology] **High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-023-01697-9)\n1. [2022 Nature Communications] **Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-30033-z)\n1. [2022 Nature Biotechnology] **DestVI identifies continuums of cell types in spatial transcriptomics data** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-022-01272-8)\n1. [2022 Biorxiv] **Accurate cell type deconvolution in spatial transcriptomics using a batch effect-free strategy** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.12.15.520612v1)\n1. [2022 Nature Biotechnology] **Spatially informed cell-type deconvolution for spatial transcriptomics** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-022-01273-7#Sec2)\n1. [2022 Nature Cancer] **Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology** [[paper]](https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs43018-022-00356-3)\n1. [2022 Nature Communications] **Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-28020-5#Sec2)\n1. [2022 Nature Biotechnology] **Cell2location maps fine-grained cell types in spatial transcriptomics** [[paper]](https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41587-021-01139-4)\n1. [2021 Briefings in Bioinformatics] **DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence** [[paper]](https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbib\u002Fbbaa414)\n1. [2021 Genome Research] **Likelihood-based deconvolution of bulk gene expression data using single-cell references** [[paper]](https:\u002F\u002Fwww.genome.org\u002Fcgi\u002Fdoi\u002F10.1101\u002Fgr.272344.120.)\n1. [2021 Genome Biology] **SpatialDWLS: accurate deconvolution of spatial transcriptomic data** [[paper]](https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13059-021-02362-7)\n1. [2021 Nucleic Acids Research] **SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes** [[paper]](https:\u002F\u002Fdoi.org\u002F10.1093\u002Fnar\u002Fgkab043)\n1. [2021 Nature Biotechnology] **Robust decomposition of cell type mixtures in spatial transcriptomics** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-021-00830-w)\n1. [2019 Nature Communications] **Accurate estimation of cell-type composition from gene expression data** [[paper]](https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41467-019-10802-z)\n1. [2019 Science] **Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution** [[paper]](https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.aaw1219)\n\n## Cell Type Annotation \n1. [2025 BioRxiv] **Large Language Model Consensus Substantially Improves the Cell Type Annotation Accuracy for scRNA-seq Data** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2025.04.10.647852v1) [[code]](https:\u002F\u002Fgithub.com\u002Fcafferychen777\u002FmLLMCelltype)\n1. [2023 biorxiv] **Scaling cross-tissue single-cell annotation models** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.10.07.561331v1.full.pdf) \n1. [2023 Nature Methods] **Multi-layered maps of neuropil with segmentation-guided contrastive learning** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-02059-8) \n1. [2023 Nature Methods] **Cue: a deep-learning framework for structural variant discovery and genotyping** [[paper]](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F36959322\u002F) \n1. [2023 Nature Communications] **Transformer for one stop interpretable cell type annotation** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-35923-4) \n1. [2023 Nature Biotech] **TACCO unifies annotation transfer and decomposition of cell identities for single-cell and spatial omics** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-023-01657-3) \n1. [2022 Nature Method] **Annotation of spatially resolved single-cell data with STELLAR** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-022-01651-8) \nNOTE: annotated reference cell graph + query cell graph\n1. [2022 Brief Bioinform] **scIAE: an integrative autoencoder-based ensemble classification framework for single-cell RNA-seq data** [[paper]](https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbib\u002Fbbab508)\n1. [2022 Nature Communications] **scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics** [[paper]](https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41467-021-24172-y)\n1. [2022 Science] **Cross-tissue immune cell analysis reveals tissue-specific features in humans** [[paper]](https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC7612735\u002F#S9)\n1. [2022 Bioinformatics] **CellMeSH: probabilistic cell-type identification using indexed literature** [[paper]](https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbioinformatics\u002Fbtab834)\n1. [2022 Cancers] **Transformer for Gene Expression Modeling (T-GEM): An Interpretable Deep Learning Model for Gene Expression-Based Phenotype Predictions** [[paper]](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F36230685\u002F)\n1. [2021 Nucleic Acids Research] **scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network** [[paper]](https:\u002F\u002Fdoi.org\u002F10.1093\u002Fnar\u002Fgkab775)\n1. [2021 BMC Bioinformatics] **Single-cell classification using graph convolutional networks** [[paper]](https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12859-021-04278-2)\n1. [2021 Genome Research] **Semisupervised adversarial neural networks for single-cell classification** [[paper]](https:\u002F\u002Fgenome.cshlp.org\u002Fcontent\u002F31\u002F10\u002F1781)\n1. [2020 BMC Bioinformatics] **EnClaSC: a novel ensemble approach for accurate and robust cell-type classification of single-cell transcriptomes** [[paper]](https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12859-020-03679-z)\n1. [2020 Bioinformatics] **ACTINN: automated identification of cell types in single cell RNA sequencing** [[paper]](https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbioinformatics\u002Fbtz592)\n1. [2020 Nature Communications] **SciBet as a portable and fast single cell type identifier** [[paper]](https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41467-020-15523-2)\n1. [2019 Nucleic Acids Research] **SuperCT: a supervised-learning framework for enhanced characterization of single-cell transcriptomic profiles** [[paper]](https:\u002F\u002Fdoi.org\u002F10.1093\u002Fnar\u002Fgkz116)\n1. [2019 Nucleic Acids Research] **CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing** [[paper]]( https:\u002F\u002Fdoi.org\u002F10.1093\u002Fnar\u002Fgkz543)\n1. [2019 Bioinformatics] **scMatch: a single-cell gene expression profile annotation tool using reference datasets** [[paper]](https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbioinformatics\u002Fbtz292)\n1. [2019 Cell Systems] **SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species** [[paper]](https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cels.2019.06.004)\n1. [2019 Genome Biology] **SingleCellNet: cPred: accurate supervised method for cell-type classification from single-cell RNA-seq data** [[paper]](https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13059-019-1862-5)\n\n\n\n## Cell Clustering\n1. [2023 Bioinformatics] **scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.01.15.524109v1](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F39\u002F2\u002Fbtad075\u002F7025496))\n1. [2023 bioRxiv] **G3DC: a Gene-Graph-Guided selective Deep Clustering method for single cell RNA-seq data** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.01.15.524109v1)\n1. [2022 BMC Bioinformatics] **SC3s: efficient scaling of single cell consensus clustering to millions of cells** [[paper]](https:\u002F\u002Fbmcbioinformatics.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs12859-022-05085-z)\n1. [2022 Bioinformatics] **GNN-based embedding for clustering scRNA-seq data** [[paper]](https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbioinformatics\u002Fbtab787)\n1. [2022 AAAI] **ZINB-based Graph Embedding Autoencoder for Single-cell RNA-seq Interpretations** [[paper]]( https:\u002F\u002Faaai-2022.virtualchair.net\u002Fposter_aaai5060)\n1. [2022 Briefings in Bioinformatics] **Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network** [[paper]]( https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbib\u002Fbbac018)\n1. [2022 Bioinformatics] **scGAC: a graph attentional architecture for clustering single-cell RNA-seq data** [[paper]]( https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbioinformatics\u002Fbtac099)\n1. [2022 Nature Computational Science] **Cell clustering for spatial transcriptomics data with graph neural networks** [[paper]]( https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs43588-022-00266-5)\n1. [2021 Nature Communications] **Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data** [[paper]]( https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-021-22008-3)\n1. [2020 NAR Genomics and Bioinformatics] **Deep soft K-means clustering with self-training for single-cell RNA sequence data** [[paper]]( https:\u002F\u002Fdoi.org\u002F10.1093\u002Fnargab\u002Flqaa039)\n1. [2020 Nature Communications] **Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-020-15851-3)[[website]](https:\u002F\u002Feleozzr.github.io\u002Fdesc\u002F)[[github]](https:\u002F\u002Fgithub.com\u002Feleozzr\u002Fdesc)\n1. [2019 Nature Machine Intelligence] **Clustering single-cell RNA-seq data with a model-based deep learning approach** [[paper]]( https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-019-0037-0)\n\n\u003C!---\n## Cell Trajectory \n1. [2017 Nature Communications] **Reconstructing cell cycle and disease progression using deep learning** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-017-00623-3)\n--->\n\n## Disease Prediction\n1. [2024 Nature Biotechnology] **Can single-cell biology realize the promise of precision medicine?** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-024-02138-x)\n1. [2018 IJCAI] **Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification** [[paper]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F2018\u002F490)\n1. [2021 NPJ Digital Medicine] **DeePaN - A deep patient graph convolutional network integratingclinico-genomic evidence to stratify lung cancers benefiting from immunotherapy** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41746-021-00381-z)\n1. [2022 Biocumputing] **CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq** [[paper]](https:\u002F\u002Fwww.worldscientific.com\u002Fdoi\u002Fabs\u002F10.1142\u002F9789811250477_0031)\n1. [2022 CHIL '20: Proceedings of the ACM Conference on Health, Inference, and Learning] **Disease state prediction from single-cell data using graph attention networks** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3368555.3384449)\n\n## Multimodal Integration\n1. [2024 Nature Methods] **Search and match across spatial omics samples at single-cell resolution** [[Paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-024-02410-7)\n1. [2023 Nature Biotechnology] **Integration of spatial and single-cell data across modalities with weakly linked features** [[Paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-023-01935-0)\n1. [2023 Nature Communications] **scDREAMER for atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier** [[Paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-43590-8)\n1. [2023 biorxiv] **Automated single-cell omics end-to-end framework with data-driven batch inference** [[Paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.11.01.564815v1)\n1. [2023 Nature Biotechnology] **Integration of multi-modal single-cell data** [[Paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-023-01826-4)\n1. [2023 Nature Biotechnology] **Integration of spatial and single-cell data across modalities with weakly linked features** [[Paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-023-01935-0)\n1. [2023 Briefings in Bioinformatics] **A universal framework for single-cell multi-omics data integration with graph convolutional networks** [[Paper]](https:\u002F\u002Facademic.oup.com\u002Fbib\u002Fadvance-article\u002Fdoi\u002F10.1093\u002Fbib\u002Fbbad081\u002F7079707)\n1. [2022 PMLR] **CVQVAE: A representation learning based method for multi-omics single cell data integration** [[Paper]](https:\u002F\u002Fproceedings.mlr.press\u002Fv200\u002Fliu22a.html)\n1. [2022 Nature Biotechnology] **Multi-omics single-cell data integration and regulatory inference with graph-linked embedding** [[Paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-022-01284-4)\n1. [2022 Nature Communications] **Clustering of single-cell multi-omics data with a multimodal deep learning method** [[Survey]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-35031-9)\n1. [2022 Genome Biology] **A benchmark study of deep learning-based multi-omics data fusion methods for cancer** [[Survey]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-022-02739-2)\n1. [2018 ICML] **MAGAN: Aligning biological manifolds** [[paper]](https:\u002F\u002Fproceedings.mlr.press\u002Fv80\u002Famodio18a.html)\n1. [2019 PLoS computational biology] **Building gene regulatory networks from scATAC-seq and scRNA-seq using linked self organizing maps** [[paper]](https:\u002F\u002Fjournals.plos.org\u002Fploscompbiol\u002Farticle?id=10.1371\u002Fjournal.pcbi.1006555)\n1. [2020 Bioinformatics] **SCIM: universal single-cell matching with unpaired feature sets** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F36\u002FSupplement_2\u002Fi919\u002F6055906)\n1. [2021 Nature communications] **Multi-domain translation between single-cell imaging and sequencing data using autoencoders** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-020-20249-2)\n1. [2021 PLoS Computational Biology] **Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion** [[paper]](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F33826608\u002F)\n1. [2021 Genome biology] **Cobolt: integrative analysis of multimodal single-cell sequencing data** [[paper]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-021-02556-z)\n1. [2021 Cell reports methods] **A mixture-of-experts deep generative model for integrated analysis of single-cell multiomics data** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2667237521001235)\n1. [2021 Briefings in Bioinformatics] **Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fbib\u002Farticle\u002F22\u002F4\u002Fbbaa287\u002F5985290)\n1. [2021 Bioinformatics] **Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F37\u002F22\u002F4091\u002F6283577)\n1. [2022 Nature Biotechnology] **scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-021-01161-6)\n1. [2022 Bioinformatics] **SMILE: mutual information learning for integration of single-cell omics data** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F38\u002F2\u002F476\u002F6384571)\n1. [2022 SIGKDD] **Graph Neural Networks for Multimodal Single-Cell Data Integration** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539213)\n1. [2022 Genome biology] **scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously** [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1186\u002Fs13059-022-02706-x)\n1. [2019 Biorxiv] **A Joint Model of RNA Expression and Surface Protein Abundance in Single Cells** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F791947v1.abstract)\n1. [2021 Biorxiv] **DeepMAPS: Single-cell biological network inference using heterogeneous graph transformer** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2021.10.31.466658v2)\n1. [2022 Biorxiv] **Adaptative Machine Translation between paired Single-Cell Multi-Omics Data** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2021.01.27.428400v2)\n1. [2022 Biorxiv] **Multigrate: single-cell multi-omic data integration** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.03.16.484643v1.abstract)\n1. [2019 NeurIPS multi-lingual pretraining for multi-omics] **Cross-lingual Language Model Pretraining** [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1901.07291.pdf)\n\n\n## Multiomics Translation\n1. [2024 Nature Communications] **scButterfly: a versatile single-cell cross-modality translation method via dual-aligned variational autoencoders** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-024-47418-x)\n1. [2023 arxiv scHyena] **scHyena: Foundation Model for Full-Length Single-Cell RNA-Seq Analysis in Brain** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.02713)\n1. [2023 bioRxiv scTranslator] **A pre-trained large language model for translating single-cell transcriptome to proteome** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.07.04.547619v1)\n1. [2023 Advanced Science] **Efficient Generation of Paired Single-Cell Multiomics Profiles by Deep Learning** [[paper]](https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Ffull\u002F10.1002\u002Fadvs.202301169)\n1. [2022 JCB] **Multimodal Single-Cell Translation and Alignment with Semi-Supervised Learning** [[paper]](https:\u002F\u002Fwww.liebertpub.com\u002Fdoi\u002Ffull\u002F10.1089\u002Fcmb.2022.0264)\n1. [2022 Nature Machine Intelligence sciPENN] **A multi-use deep learning method for CITE-seq and single-cell RNA-seq data integration with cell surface protein prediction and imputation** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-022-00545-w)\n1. [2022 RECOMB] **Semi-supervised Single-Cell Cross-modality Translation Using Polarbear** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1007\u002F978-3-031-04749-7_2)\n1. [2020 PNAS] **BABEL enables cross-modality translation between multiomic profiles at single-cell resolution** [[paper]](https:\u002F\u002Fwww.pnas.org\u002Fdoi\u002Fabs\u002F10.1073\u002Fpnas.2023070118)\n\n\n\n","[![Awesome](https:\u002F\u002Fcdn.rawgit.com\u002Fsindresorhus\u002Fawesome\u002Fd7305f38d29fed78fa85652e3a63e154dd8e8829\u002Fmedia\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fsindresorhus\u002Fawesome)\n[![PRs Welcome](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPRs-welcome-brightgreen.svg?style=flat-square)](https:\u002F\u002Fgithub.com\u002FOmicsML\u002Fawesome-deep-learning-single-cell-papers\u002Fpulls)\n# awesome-deep-learning-single-cell-papers\n\n本仓库追踪使用深度学习方法进行单细胞分析的最新论文，并按具体任务对其进行分类。\n\n我们将尽力保持此列表的更新。如果您发现任何错误或遗漏的论文，请随时提交 issue 或 pull request。\n\n## 引用\n\n欢迎引用我们在单细胞分析中关于深度学习的综合综述论文：\n\n```bibtex\n@article{molho2024deep,\n  title={Deep learning in single-cell analysis},\n  author={Molho, Dylan and Ding, Jiayuan and Tang, Wenzhuo and Li, Zhaoheng and Wen, Hongzhi and Wang, Yixin and Venegas, Julian and Jin, Wei and Liu, Renming and Su, Runze and others},\n  journal={ACM Transactions on Intelligent Systems and Technology},\n  volume={15},\n  number={3},\n  pages={1--62},\n  year={2024},\n  publisher={ACM New York, NY}\n}\n```\n\n\n## 关于单细胞基础模型，更多论文记录在[[这里]](https:\u002F\u002Fgithub.com\u002FOmicsML\u002Fawesome-foundation-model-single-cell-papers)。\n\n- [书籍](#book)\n- [单细胞技术](#single-cell-techonoly)\n- [课程](#course)\n- [综述](#survey)\n- [预训练模型、大语言模型或基础模型](#pretrained-model-or-llm-or-foundation-model)\n- [GAN或扩散模型](#gan-or-diffusion-model)\n- [多模态学习](#multimodal-learning)\n- [单细胞数据模拟](#single-cell-data-simulation)\n- [可解释性](#interpretability)\n- [时空转录组学](#spatialtemporal-transcriptomic)\n- [RNA速度](#rna-velocity)\n- [分子表示学习](#molecular-representation-learning)\n- [单细胞扰动或药物反应](#single-cell-perturbation-or-drug-response)\n- [细胞动力学](#cellular-dynamics)\n- [单细胞应用](#single-cell-application)\n- [单细胞或空间数据分析工具](#tools-for-single-cell-or-spatial-data)\n- [单细胞图谱](#single-cell-atlas)\n- [单细胞可视化](#single-cell-visualization)\n- [基准测试](#benchmarking)\n- [指标设计](#metric-design)\n- [亚细胞分析](#subcellular-analysis)\n- [降维与可视化](#dimensionality-reduction-and-visualization)\n- [表示学习](#representation-learning)\n- [批次效应校正](#batch-effect-correction)\n- [肿瘤微环境-TME](#tumor-microenvironment-tme)\n- [细胞间通讯事件](#cell-cell-communication-events)\n- [基因调控网络](#gene-regulatory-network)\n- [插补](#imputation)\n- [空间领域](#spatial-domain)\n- [参考嵌入或迁移学习](#reference-embedding-or-transfer-learning)\n- [细胞分割](#cell-segmentation)\n- [细胞类型去卷积](#cell-type-deconvolution)\n- [细胞类型注释](#cell-type-annotation)\n- [细胞聚类](#cell-clustering)\n- [疾病预测](#disease-prediction)\n- [多模态整合](#multimodal-integration)\n- [多组学翻译](#multiomics-translation)\n\n## 书籍\n1. [[单细胞最佳实践]](https:\u002F\u002Fwww.sc-best-practices.org\u002Fpreamble.html), Fabian Theis 实验室\n1. [[基于 Bioconductor 的单细胞分析基础]](http:\u002F\u002Fbioconductor.org\u002Fbooks\u002F3.15\u002FOSCA.basic\u002Findex.html), 基于 R 语言的 Bioconductor 软件\n\n## 单细胞技术\n### 单模态\n\n### 多模态\n\n### 空间转录组学\n1. [2022 年《自然方法》] **空间转录组学博物馆** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-022-01409-2)\n\n## 课程\n1. [[CSCI 1850 基因组学中的深度学习]](https:\u002F\u002Fcs.brown.edu\u002Fcourses\u002Fcsci1850\u002Findex.html), 布朗大学\n1. [[基因组学中的机器学习：解析人类疾病回路]](http:\u002F\u002Fstellar.mit.edu\u002FS\u002Fcourse\u002F6\u002Ffa21\u002F6.047\u002Fmaterials.html), 麻省理工学院\n1. [[单细胞 RNA 测序数据分析]](https:\u002F\u002Fbroadinstitute.github.io\u002F2019_scWorkshop\u002Findex.html), 由 Orr Ashenberg、Dana Silverbush 和 Kirk Gosik 主讲的课程\n1. [[单细胞 RNA 测序数据分析，www.singlecellcourse.org]](https:\u002F\u002Fscrnaseq-course.cog.sanger.ac.uk\u002Fwebsite\u002Findex.html) - 逐步讲解的 scRNA-seq 分析课程。基于 R 语言，包含代码示例、解释和练习。从比对（STAR）和质量控制（FASTQC）开始，到 R 语言入门、SingleCellExperiment 类、`scater` 对象、数据探索（reads、UMI）、过滤、归一化（`scran`）、去除批次效应（`RUV`、`ComBat`、`mnnCorrect`、GLM、`Harmony`）、聚类和标记基因鉴定（`SINCERA`、`SC3`、tSNE、`Seurat`）、特征选择（`M3Drop::M3DropConvertData`、`BrenneckeGetVariableGenes`）、伪时间分析（`TSCAN`、`Monocle`、扩散图、`SLICER`、`Ouija`、`destiny`）、插补（`scImpute`、`DrImpute`、`MAGIC`）、差异表达（Kolmogorov-Smirnov、Wilcoxon、`edgeR`、`Monocle`、`MAST`）、数据整合（`scmap`、细胞到细胞映射、`Metaneighbour`、`mnnCorrect`、`Seurat` 的典型相关分析）。搜索 scRNA-seq 数据（R 包 `scfind`），以及 Hemberg 团队的公开数据集（https:\u002F\u002Fhemberg-lab.github.io\u002FscRNA.seq.datasets\u002F）。[Seurat 章节](https:\u002F\u002Fscrnaseq-course.cog.sanger.ac.uk\u002Fwebsite\u002Fseurat-chapter.html)。[\"理想\" scRNA-seq 流程](https:\u002F\u002Fscrnaseq-course.cog.sanger.ac.uk\u002Fwebsite\u002Fideal-scrnaseq-pipeline-as-of-oct-2017.html)。[视频讲座](https:\u002F\u002Fwww.youtube.com\u002Fwatch?list=PLEyKDyF1qdOYAhwU71qlrOXYsYHtyIu8n&v=56n77bpjiKo)。\u003Cdetails>\n    \u003Csummary>论文\u003C\u002Fsummary>\n    Andrews, Tallulah S., Vladimir Yu Kiselev, Davis McCarthy 和 Martin Hemberg. \"教程：单细胞 RNA 测序数据计算分析指南.\" https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41596-020-00409-w 《Nature Protocols》，2020 年 12 月 7 日。\n\u003C\u002Fdetails>\n\n## 综述\n1. [2023 年《生物物理评论》] **空间转录组学中的深度学习：从下一代测序中学习** [[论文]](https:\u002F\u002Fpubs.aip.org\u002Faip\u002Fbpr\u002Farticle\u002F4\u002F1\u002F011306\u002F2879089\u002FDeep-learning-in-spatial-transcriptomics-Learning)\n\n## 预训练模型或大语言模型或基础模型\n**更多详情请参阅** [[foundation-model-single-cell-papers]](https:\u002F\u002Fgithub.com\u002FOmicsML\u002Ffoundation-model-single-cell-papers)\n1. [2024 BioRxiv] **scPRINT：在5000万个细胞上进行预训练，实现稳健的基因网络预测** [[论文](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2024.07.29.605556v1)]\n1. [2024 ICLR] **BioBridge：通过知识图谱连接生物医学基础模型** [[论文]](https:\u002F\u002Fopenreview.net\u002Fforum?id=jJCeMiwHdH)\n1. [2023 bioRxiv] **CellPLM：超越单细胞的细胞语言模型预训练** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.10.03.560734v1)\n1. [2023 bioRxiv] **DNABERT-2：高效的基础模型及多物种基因组基准测试** [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.15006.pdf)\n1. [2023 bioRxiv] **大型语言模型对科学发现的影响：基于GPT-4的初步研究** [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.07361)\n1. [2023 bioRxiv] **用化学工具增强大型语言模型** [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2304.05376.pdf)\n1. [2023 bioRxiv] **GET：跨人类细胞类型的转录基础模型** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.09.24.559168v1)\n1. [2023 bioRxiv] **Cell2Sentence：向大型语言模型教授生物学语言** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.09.11.557287v1)\n1. [2023 bioRxiv] **评估大型语言模型在单细胞数据分析中的效用** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.09.08.555192v2)\n1. [2023 arxiv] **迈向通用生物医学人工智能** [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.14334)\n1. [2023 bioRxiv] **利用蛋白质网络和单细胞数据上的深度学习对蛋白质表示进行上下文化处理** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.07.18.549602v1)\n1. [2023 Nature] **大型语言模型编码临床知识** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-023-06291-2)\n1. [2023 Nature Methods] **迈向生物图像分割的基础模型** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-01885-0)\n1. [2023 bioRxiv] **DrugGPT：基于GPT的设计策略，用于开发靶向特定蛋白质的潜在配体** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.06.29.543848v1)\n1. [2023 arxiv] **Hyena Hierarchy：迈向更大规模的卷积语言模型** [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.10866)\n1. [2023 bioRxiv] **基于人群水平的单细胞数据整合，实现跨样本的多尺度分析** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.11.28.517803v1)\n1. [2023 bioRxiv] **单细胞转录组学的大规模基础模型** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.05.29.542705v2)\n1. [2023 Bioinformatics] **基于Transformer的语言模型在生物信息学中的应用：综述** [[论文]](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F36845200\u002F)\n1. [2023 Nature] **迁移学习赋能网络生物学预测** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-023-06139-9)\n1. [2023 arxiv] **BiomedGPT：面向视觉、语言和多模态任务的统一且通用的生物医学生成式预训练Transformer** [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.17100)\n1. [2023 arxiv] **Clinical Camel：一款开源的专家级医学语言模型，采用对话式知识编码** [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.12031)\n1. [2023 arxiv] **CancerGPT：利用大型预训练语言模型进行少样本药物组合协同效应预测** [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.10946)\n1. [2023 iSchience tGPT] **从大规模转录组数据中进行生成式预训练，以解析单细胞数据** [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2589004223006132)\n1. [2023 bioRxiv] **GeneGPT：通过领域工具增强大型语言模型，以提升生物医学信息的可访问性** [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.09667)\n1. [2023 Github] **OpenBioMed** [[Github]](https:\u002F\u002Fgithub.com\u002FBioFM\u002FOpenBioMed)\n1. [2023 blog] **BioMedLM：一款针对生物医学文本的领域专用大型语言模型** [[博客]](https:\u002F\u002Fwww.mosaicml.com\u002Fblog\u002Fintroducing-pubmed-gpt)\n1. [2023 bioRxiv] **scGPT：利用生成式AI构建单细胞多组学基础模型的探索** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.04.30.538439v1)\n1. [2023 bioRxiv] **xTrimoGene：一种高效且可扩展的单细胞RNA测序数据表征学习器** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.03.24.534055v1)\n1. [2023 Nature Biotechnology] **大型语言模型可生成跨不同家族的功能性蛋白质序列** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-022-01618-2)\n1. [2022 arxiv] **一款单细胞基因表达语言模型** [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.14330)\n1. [2022 Briefings in Bioinformatics] **BioGPT：用于生物医学文本生成与挖掘的生成式预训练Transformer** [[论文]](https:\u002F\u002Facademic.oup.com\u002Fbib\u002Farticle\u002F23\u002F6\u002Fbbac409\u002F6713511?guestAccessKey=a66d9b5d-4f83-4017-bb52-405815c907b9&login=true)\n1. [2022 Nature Machine Intelligence] **scBERT作为大规模预训练的深度语言模型，用于单细胞RNA测序数据的细胞类型注释** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-022-00534-z)\n1. [2022 bioRxiv] **scFormer：一种基于Transformer的单细胞数据通用表征学习方法** [[论文]](https:\u002F\u002Fopenreview.net\u002Fpdf?id=7hdmA0qtr5)\n1. [2022 Bioinformatics] **scPretrain：用于细胞类型分类的多任务自监督学习** [[论文]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F38\u002F6\u002F1607\u002F6499287)\n1. [2021 PNAS] **通过将无监督学习扩展到2.5亿条蛋白质序列，揭示生物结构与功能** [[论文]](https:\u002F\u002Fwww.pnas.org\u002Fdoi\u002F10.1073\u002Fpnas.2016239118)\n1. [2021 Bioinformatics] **DNABERT：面向基因组DNA语言的预训练双向编码器表示Transformer模型** [[论文]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F37\u002F15\u002F2112\u002F6128680)\n1. [2021 Arxiv，576次引用] **面向生物医学自然语言处理的领域专用语言模型预训练** [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2004.10964.pdf)\n1. [2021 Arxiv，1111次引用] **不要停止预训练：将语言模型适配到特定领域和任务** [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2004.10964.pdf)\n\n## GAN 或扩散模型\n1. [2024 Brief Bioinform] **stDiff：一种通过单细胞转录组数据推断空间转录组的扩散模型** [[论文]](https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC11021815\u002F)\n1. [2024 biorxiv] **scDiffEq：基于神经随机微分方程的单细胞动力学漂移-扩散建模** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.12.06.570508v1)\n1. [2024 biorxiv] **scDiffusion：利用扩散模型条件生成高质量单细胞数据** [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.03968)\n1. [2024 biorxiv] **使用扩散模型在体外生成基因表达谱** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2024.04.10.588825v1)\n1. [2024 Cell] **用于时空细胞信号通路设计的可编程反应-扩散系统** [[论文]](https:\u002F\u002Fwww.cell.com\u002Fcell\u002Fpdf\u002FS0092-8674(23)01339-9.pdf)\n1. [2023 ICCV] **基于 Transformer 的可扩展扩散模型** [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.09748)\n1. [2023 biorxiv] **从噪声到知识：基于概率扩散的基因调控网络神经推理** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.11.05.565675v1)\n1. [2023 biorxiv Diffusion] **通过条件扩散生成模型构建通用单细胞分析框架** [[论文]](https:\u002F\u002Fscholar.google.com\u002Fcitations?view_op=view_citation&hl=en&user=7lwkXGEAAAAJ&citation_for_view=7lwkXGEAAAAJ:Se3iqnhoufwC)\n1. [2023 biorxiv GAN] **利用生成式建模预测细胞对扰动的形态学响应** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.07.17.549216v1)\n1. [2023 Nature Diffusion Model] **AI 工具正在设计全新的蛋白质，或将变革医学** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fd41586-023-02227-y)\n1. [2023 biorxiv Diffusion Model] **双剑合璧：将深度扩散模型与变分自编码器整合用于单细胞转录组分析** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.04.13.536789v1)\n1. [2023 biorxiv GAN] **利用生成对抗网络实现多组学单细胞数据的可扩展整合** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.06.26.546547v2)\n1. [2023 biorxiv Diffusion Model] **利用扩散模型在重叠细胞实例分割中自发打破对称性** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.07.07.548066v1)\n        \n\n## 多模态学习\n1. [2024 ICLR workshop NLP+Gene Expression] **转录组与文本的联合嵌入实现了通过自然语言交互式探索单细胞 RNA-seq 数据** [[论文]](https:\u002F\u002Fopenreview.net\u002Fforum?id=yWiZaE4k3K)\n1. [2024 Nature Biotechnology Image+Gene Expression] **通过整合空间转录组与组织学推断超分辨率组织结构** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-023-02019-9#citeas)\n1. [2023 arxiv Image+Gene Expression] **融合卷积与图节点共嵌入的 Transformer：一种准确且可解释的视觉骨干网络，用于根据局部病理图像预测基因表达** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.05.28.542669v1)\n1. [2023 arxiv multimodal] **MuSe-GNN：从多模态生物图数据中学习统一的基因表示** [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.02275)\n1. [2023 biorxiv multimodal] **Pathformer：一种受生物通路启发、整合癌症多模态数据的 Transformer 模型** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.05.23.541554v1)\n1. [2023 biorxiv Image+Gene Expression] **基于双模态对比学习，从 H&E 组织学图像预测空间分辨的基因表达** [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.01859.pdf)\n1. [2023 biorxiv Image+Gene Expression] **基于空间转录组学，利用 H&E 图像预测单细胞基因表达** [[论文]](https:\u002F\u002Fwww.spiedigitallibrary.org\u002Fconference-proceedings-of-spie\u002F12471\u002F1247105\u002FSingle-cell-gene-expression-prediction-using-HE-images-based-on\u002F10.1117\u002F12.2654294.full?SSO=1)\n\n## 单细胞数据模拟\n1. [2025 NM] **scMultiSim：由基因调控网络和细胞间相互作用引导的单细胞多组学及空间数据模拟** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-025-02651-0)\n1. [2023 NBT] **scDesign3 生成用于多模态单细胞和空间组学的真实感体外数据** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-023-01772-1)\n1. [2023 NC] **scReadSim：一款单细胞 RNA-seq 和 ATAC-seq 测序数据模拟器** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-43162-w)\n1. [2023 biorxiv] **GRouNdGAN：基于因果生成对抗网络的 GRN 引导型单细胞 RNA-seq 数据模拟** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.07.25.550225v1)\n1. [2022 JCB] **scDesign2 可模拟保留基因相关性的单细胞基因表达计数数据** [[论文]](https:\u002F\u002Fwww.liebertpub.com\u002Fdoi\u002Fabs\u002F10.1089\u002Fcmb.2021.0440)\n1. [2021 GB] **scDesign2：一款透明的模拟器，可生成高保真度的单细胞基因表达计数数据，并捕捉基因相关性** [[论文]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1186\u002Fs13059-021-02367-2)\n1. [2019 Bioinformatics] **用于理性 scRNA-seq 实验设计的统计模拟器 scDesign** [[论文]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F35\u002F14\u002Fi41\u002F5529133)\n\n\n\n## 可解释性\n1. [2021 CVPR] **超越注意力可视化之外的 Transformer 可解释性** [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.09838)[[github]](https:\u002F\u002Fgithub.com\u002Fhila-chefer\u002FTransformer-Explainability)\n1. [2021 ICML] **BERTology 遇上生物学：解读蛋白质语言模型中的注意力机制** [[论文]](https:\u002F\u002Fopenreview.net\u002Fpdf?id=YWtLZvLmud7)\n1. [2019 ACL] **Transformer 模型中注意力机制的多尺度可视化** [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1906.05714.pdf) [[github]](https:\u002F\u002Fgithub.com\u002Fjessevig\u002Fbertviz\u002Ftree\u002Fmaster)\n\n## 空间-时间转录组学\n1. [2024 biorxiv] **基于最优传输度量的单细胞数据基因轨迹推断** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.07.08.499404v3)\n1. [2023 biorxiv] **利用深度学习揭示发育时间和节奏** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-02083-8)\n1. [2023 biorxiv] **scNODE：用于时间序列单细胞转录组数据预测的生成模型** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.11.22.568346v1.full.pdf)\n1. [2023 biorxiv] **基于最优传输度量的单细胞数据基因轨迹推断** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.07.08.499404v3)\n1. [2023 arxiv CS领域综述] **时间序列与时空数据的大规模模型：综述与展望** [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.10196)\n1. [2023 ICML CS领域参考文献] **连续时空Transformer** [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.13338)\n1. [2023 arxiv CS领域多模态参考文献] **IMAGEBIND：一个嵌入空间，连接所有模态** [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.05665.pdf)\n1. [2023 arxiv CS领域多模态参考文献] **UnIVAL：面向图像、视频、音频和语言任务的统一模型** [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2307.16184.pdf)\n1. [2023 arxiv CS领域多模态参考文献] **Meta-Transformer：多模态学习的统一框架** [[Meta-Transformer论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2307.10802.pdf)[[viT视觉Transformer论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.11929)[[ImageGPT论文：基于像素的生成式预训练]](https:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fchen20s.html)\n1. [2023 KDD CS领域参考文献] **时空扩散点过程** [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.12403.pdf)\n1. [2023 arxiv CS领域参考文献] **用于动态时空预测的长距离Transformer** [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.12218.pdf)\n1. [2023 Nature Communications] **使用PRESCIENT对单细胞时间序列进行生成建模，可实现干预下的细胞轨迹预测** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-021-23518-w)\n1. [2023 bioRxiv] **通过moscot在时间和空间中映射细胞** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.05.11.540374v1)        \n1. [2023 Nature Methods] **时空分辨转录组学揭示亚细胞RNA动力学景观** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-01829-8)\n1. [2022 bioRxiv] **Spateo：单细胞空间转录组学的多维时空建模** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.12.07.519417v1.abstract)\n1. [2022 ICLR CS领域参考文献] **UniFormer：用于高效时空表征学习的统一Transformer** [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.04676)\n1. [2022 NeurIPS 从时空单细胞到时空视频] **Flamingo：一种用于少样本学习的视觉语言模型** [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.14198)\n1. [2022 arxiv，图像-基因表达对比学习] **CoCa：对比描述符是图文基础模型** [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.01917.pdf)\n1. [2020 ICLR，图像-基因表达预训练] **VL-BERT：通用视觉-语言表征的预训练** [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.08530.pdf)\n1. [2019 AAAI，图像-基因表达预训练] **Unicoder-VL：通过跨模态预训练构建的视觉与语言通用编码器** [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1908.06066.pdf)\n\n## RNA速度\n1. [2023 Nature Methods] **深度生成建模转录动力学，用于单细胞RNA速度分析** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-01994-w)\n\n\n\n## 分子表征学习\n1. [2023 ICLR] **Uni-Mol：通用三维分子表征学习框架** [[论文]](https:\u002F\u002Fopenreview.net\u002Fforum?id=6K2RM6wVqKu)\n\n## 单细胞扰动或药物反应\n1. [2024 NeurIPS AIDrugX (Spotlight)] **细胞中的信号：用于治疗学的多模态与情境化机器学习基础** [[论文]](https:\u002F\u002Fopenreview.net\u002Fforum?id=kL8dlYp6IM) [[海报]](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1plypydZCaegbgxyCl-xehFxSgwX6e8So\u002Fview)\n2. [2024 biorxiv] **基于深度学习的基因扰动效应预测尚未优于简单的线性方法** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2024\u002F09\u002F21\u002F2024.09.16.613342.full.pdf)\n1. [2024 ICLR] **在序数型扰动下用于转录组数据的带监督生物可解释变分自编码器** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-02144-y)\n1. [2024 Nature Methods] **scPerturb：统一的单细胞扰动数据集** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-02144-y)\n1. [2023 biorxiv] **Unagi：用于解析复杂疾病中细胞动态及计算机辅助药物发现的深度生成模型** [[论文]](https:\u002F\u002Fassets.researchsquare.com\u002Ffiles\u002Frs-3676579\u002Fv1_covered_2dc4a452-a1f2-47a2-acb1-f816276a9e07.pdf?c=1702865288)\n1. [2023 NeurIPS] **使用稀疏加性机制转移变分自编码器建模细胞扰动** [[论文]](https:\u002F\u002Fopenreview.net\u002Fforum?id=DzaCE00jGV)\n1. [2023 Nature Methods] **利用神经最优传输学习单细胞扰动响应** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-01969-x)\n1. [2023 Nature Methods] **神经最优传输可在单细胞水平上预测扰动响应** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-01968-y)\n1. [2023 Mol Syst Biol] **预测高通量筛选中细胞对复杂扰动的响应** [[论文]](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F37154091\u002F)\n1. [2023 biorxiv] **学习扰动特异性细胞表征以预测跨细胞环境的转录响应** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.03.20.533433v1)\n1. [2023 Nature] **通过网络推断和计算机模拟基因扰动解构细胞身份** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-022-05688-9)\n1. [2023 biorxiv扩散模型] **二者的力量：整合深度扩散模型与变分自编码器用于单细胞转录组分析** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.04.13.536789v1)\n1. [2023 ICLR] **利用变分因果推断与精细化关系信息预测细胞响应** [[论文]](https:\u002F\u002Fopenreview.net\u002Fforum?id=ICYasJBlZNs)\n1. [2022 arxiv] **PerturbNet可预测未见化学及遗传扰动下的单细胞响应** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.07.20.500854v2)\n1. [2022 arxiv] **CausalBench：基于单细胞扰动数据的大型网络推断基准测试** [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.17283)\n1. [2022 NeurIPS] **在单细胞分辨率下预测细胞对新型药物扰动的响应** [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.13545.pdf)\n1. [2022 biorxiv] **GEARS：预测新型多基因扰动的转录结果** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.07.12.499735v2)\n1. [2021 biorxiv] **学习高通量筛选中复杂扰动的可解释细胞响应** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2021.04.14.439903v2)\n1. [2019 Nature Methods] **scGen可预测单细胞扰动响应** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-019-0494-8)\n\n\n## 细胞动力学\n1. [2023 Genome Biology] **scTour：一种用于稳健推断和精确预测细胞动力学的深度学习架构** [[论文]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-023-02988-9)\n        \n        \n\n## 单细胞应用\n1. [2023 medrxiv] **人类组织的单细胞RNA测序支持成功药物靶点的确定** [[论文]](https:\u002F\u002Fwww.medrxiv.org\u002Fcontent\u002F10.1101\u002F2024.04.04.24305313v1)\n1. [2023 Nature Methods] **机器学习在罕见病中的应用** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-01886-z)\n1. [2023 Molecular System Biology] **单细胞生物学：未来会怎样？** [[论文]](https:\u002F\u002Fwww.embopress.org\u002Fdoi\u002Ffull\u002F10.15252\u002Fmsb.202311799)\n1. [2023 Genes] **组学时代下的单细胞分析：技术与癌症领域的应用** [[论文]](https:\u002F\u002Fwww.mdpi.com\u002F2073-4425\u002F14\u002F7\u002F1330)\n1. [2023 Nature Communications] **ASGARD是一个单细胞引导的流程，用于辅助药物再定位** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-36637-3)\n1. [2023 Nature Reviews Clinical Oncology] **通过使用多维组学数据推进CAR T细胞疗法** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41571-023-00729-2)\n\n## 单细胞或空间数据分析工具\n[[工具汇总]](https:\u002F\u002Fwww.scrna-tools.org\u002Ftools)\n1. [2024 biorxiv] **Scvi-hub：一个面向模型驱动的单细胞分析的实用资源库** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2024.03.01.582887v1.full.pdf)\n1. [2024 Nature Methods] **SpatialData：一个开放且通用的空间组学数据框架** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-024-02212-x)\n1. [2023 Nucleic Acids Research] **DeepBIO：一个自动化且可解释的深度学习平台，用于高通量生物序列预测、功能注释和可视化分析** [[论文]](https:\u002F\u002Facademic.oup.com\u002Fnar\u002Fadvance-article\u002Fdoi\u002F10.1093\u002Fnar\u002Fgkad055\u002F7041952)\n1. [2023 Github] **SpatialTis：一个超快速的空间分析工具包，适用于大规模空间单细胞数据。** [[github]](https:\u002F\u002Fgithub.com\u002FMr-Milk\u002FSpatialTis)\n1. [2023 biorxiv] **CellContrast：通过深度对比学习重构单细胞RNA测序数据中的空间关系** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.10.12.562026v1)\n\n## 单细胞图谱\n1. [2023年《自然》] **小鼠全脑细胞类型的高分辨率转录组与空间图谱** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-023-06812-z)\n1. [2023年《自然》] **成人人类乳腺的空间分辨单细胞基因组图谱** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-023-06252-9)\n1. [2023年《自然医学》] **健康与疾病状态下肺部的整合细胞图谱** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41591-023-02327-2)\n1. [2022年《核酸研究》] **Aquila：一个空间组学数据库及分析平台** [[论文]](https:\u002F\u002Facademic.oup.com\u002Fnar\u002Farticle\u002F51\u002FD1\u002FD827\u002F6761736)\n1. [[Cellxgene数据集：截至2022年共546个数据集]](https:\u002F\u002Fcellxgene.cziscience.com\u002Fdatasets)\n1. [2022年《自然方法》] **单细胞基因组学中图谱级别数据整合的基准测试** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-021-01336-8.pdf)\n1. [2022年bioRxiv] **对图谱级单细胞数据的统一分析** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.08.06.503038v1.full.pdf)\n1. [2022年《自然生物技术》] **空间与单细胞转录组数据的整合揭示小鼠器官发生过程** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-021-01006-2)\n1. [2022年bioRxiv] **利用SageNet对解离后的单细胞数据进行有监督的空间推断** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.04.14.488419v1)  \n1. [2022年《自然通讯》] **通过将异质性数据集投影到共同的细胞嵌入空间实现在线单细胞数据整合** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-33758-z)\n\n\n## 单细胞可视化工具\n1. [[Chan Zuckerberg：单细胞转录组学数据的交互式探索工具]](https:\u002F\u002Fgithub.com\u002Fchanzuckerberg\u002Fcellxgene)\n1. [[UCSC细胞浏览器]](http:\u002F\u002Fcells.ucsc.edu\u002F)\n1. [[Cytoscape]](https:\u002F\u002Fcytoscape.org\u002F)\n1. [[UCSC Xena]](https:\u002F\u002Fxena.ucsc.edu\u002F)\n1. [[ASAP：自动化单细胞分析流程]](https:\u002F\u002Fasap.epfl.ch\u002F)\n1. [[GenePattern]](https:\u002F\u002Fnotebook.genepattern.org\u002F)\n1. [[Loopy Browser]](https:\u002F\u002Floopybrowser.com\u002F)\n\n## 基准测试\n1. [2024 MoML@Mila] **** [[CMT投稿]](https:\u002F\u002Fcmt3.research.microsoft.com\u002FMoML2024\u002FSubmission\u002FSummary\u002F13) [[预印本]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2024.06.12.598655v2) [[海报]](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1LYdITeFY5iX07zyXPGVEjMpYjuHMrneS\u002Fview) [[会议]](https:\u002F\u002Fportal.ml4dd.com\u002Fmoml-2024)\n2. [2023 biorxiv] **基于组织学的空间基因表达预测的转化潜力基准测试** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.12.12.571251v1)\n1. [2023 bioRxiv] **FFPE组织中成像空间转录组学平台的系统性基准测试** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.12.07.570603v1)\n1. [2023 bioRxiv] **跨单细胞RNA和ATAC数据的多组学整合算法基准测试** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.11.15.564963v1)\n1. [2023 bioRxiv] **BEND：在生物学相关任务上对DNA语言模型的基准测试** [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.12570)\n1. [2023 Genome Biology] **用于未配对和配对单细胞RNA-seq及ATAC-seq数据联合整合的算法基准测试** [[论文]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-023-03073-x)\n1. [2023 Nature Communications] **针对空间转录组学细胞去卷积的全面基准测试及实用指南** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-37168-7)\n1. [2023 bioRxiv] **单细胞基因组数据的通用预处理** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.09.14.543267v1.full.pdf)\n1. [2023 Genome Biology] **（单细胞方法）基准测试的元分析揭示了扩展性和互操作性的需求** [[论文]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-023-02962-5)\n1. [2023 Nature Communications] **针对空间转录组学细胞去卷积的全面基准测试及实用指南** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-37168-7)\n1. [2023 bioRxiv] **用于填补单细胞RNA测序数据的自编码器设计基准测试** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.02.16.528866v1.abstract)\n1. [2023 bioRxiv] **单细胞ATAC-seq数据的基因集评分算法基准测试** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.01.14.524081v1)\n1. [2022 Nature Communications] **从单细胞RNA-Seq数据推断细胞间通讯的方法与资源比较** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-30755-0)\n1. [2022 Nature Methods] **单细胞基因组学中图谱级别数据整合的基准测试** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-021-01336-8.pdf)\n1. [2022 Nature Methods] **用于转录本分布预测和细胞类型去卷积的空间与单细胞转录组学整合方法基准测试** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-022-01480-9)\n1. [2022 BioRxiv] **用于单细胞ATAC-seq数据的自动化细胞类型注释工具基准测试** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.10.05.511014v1)\n1. [2022 Briefings in Bioinformatics] **从多受试者单细胞RNA-Seq数据中检测不同条件下差异状态的方法基准测试** [[论文]](https:\u002F\u002Facademic.oup.com\u002Fbib\u002Farticle\u002F23\u002F5\u002Fbbac286\u002F6649780)\n1. [2022 Nucleic Acids Research] **scIMC：一个用于基准测试、比较和可视化分析单细胞RNA-Seq数据填补方法的平台** [[论文]](https:\u002F\u002Facademic.oup.com\u002Fnar\u002Farticle\u002F50\u002F9\u002F4877\u002F6582166)\n1. [2021 Frontiers in Genetics] **评估单细胞基因调控网络推断算法的可重复性** [[论文]](https:\u002F\u002Fwww.frontiersin.org\u002Farticles\u002F10.3389\u002Ffgene.2021.617282\u002Ffull)\n1. [2021 Nature Communications] **单细胞RNA测序数据模拟方法的基准研究** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-021-27130-w)\n1. [2021 Genome Biology] **基于UMI的单细胞RNA-Seq预处理流程基准测试** [[论文]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-021-02552-3)\n1. [2020 Nature Methods] **从单细胞转录组数据推断基因调控网络的算法基准测试** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-019-0690-6)\n1. [2020 Genome Biology] **单细胞RNA测序数据批次效应校正方法的基准测试** [[论文]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-019-1850-9)\n1. [2020 Nature Biotechnology] **使用参考样本对单细胞RNA测序技术进行多中心基准测试** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-020-00748-9)\n1. [2019 Nature Methods] **利用混合对照实验对单细胞RNA测序分析流程进行基准测试** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-019-0425-8)\n\n## 指标设计\n1. [2019 Narure Methods] **用于评估单细胞RNA-Seq批次校正效果的测试指标** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-018-0254-1)\n\n## 细胞亚结构分析\n1. [2024年《自然·通讯》] **BIDCell：基于生物学信息的自监督学习方法，用于细胞亚结构空间转录组数据的分割** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-44560-w)\n1. [2023年《自然·方法》] **时空解析转录组学揭示了细胞亚结构RNA动力学图谱** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-01829-8)\n1. [2023年bioRxiv] **Bering：利用迁移图嵌入进行空间转录组学的联合细胞分割与注释** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.09.19.558548v1.full.pdf)\n1. [2023年《生物信息学》] **FISHFactor：一种用于亚细胞分辨率空间转录组数据的概率因子模型** [[论文]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F39\u002F5\u002Fbtad183\u002F7114027)\n1. [2023年《科学》] **分子分辨率下的空间分辨单细胞翻译组学** [[论文]](https:\u002F\u002Fwww.science.org\u002Fdoi\u002F10.1126\u002Fscience.add3067)\n1. [2023年《自然·方法》] **细胞亚结构组学：推动分辨率、复杂性和通量极限的新前沿** [[论文]](https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC10049458\u002Fpdf\u002Fnihms-1881939.pdf)\n1. [2022年bioRxiv] **Bento：用于空间转录组数据分析的细胞亚结构工具包** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.06.10.495510v1)\n1. [2022年bioRxiv] **单细胞中的细胞亚结构空间解析基因邻域网络** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.08.03.502409v1)\n1. [2022年bioRxiv] **统计分析支持普遍存在的RNA亚细胞定位及替代性3’ UTR调控** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2022\u002F10\u002F27\u002F2022.10.26.513902.full.pdf)\n1. [2019年《细胞》] **APEX-Seq揭示的细胞亚结构RNA定位图谱** [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0092867419305550?via%3Dihub)\n\n\n\n## 降维与可视化\n1. [2023年《基因组研究》] **深度双曲流形学习揭示单细胞基因组数据中的复杂层次结构** [[论文]](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F36849204\u002F)\n1. [2021年《自然·通讯》] **单细胞RNA测序谱在超球面和双曲空间上的深度生成模型嵌入** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-021-22851-4)\n1. [2018年《自然·通讯》] **利用深度生成模型对单细胞转录组数据进行可解释的降维** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-018-04368-5)\n\n\n## 表征学习\n1. [2025年arXiv] **SUICA：为空间转录组学学习超高维稀疏隐式神经表征** [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2412.01124)\n1. [2023年《自然·机器智能》] **可复用性报告：利用Transformer模型学习单细胞RNA测序数据中的转录语法** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-023-00757-8)\n1. [2023年《基因组生物学》] **校正基于梯度的深度神经网络在基因组学中的解释** [[论文]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-023-02956-3)\n1. [2023年《自然·方法》] **SIMBA：结合特征的单细胞嵌入** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-01899-8)\n1. [2023年bioRxiv] **迈向通用细胞嵌入：利用SATURN整合跨物种单细胞RNA测序数据集** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.02.03.526939v1?rss=1)\n1. [2021年《系统生物学当前观点》] **单细胞生物学中的图表示学习** [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2452310021000329)\n1. [2020年《自然·通讯》] **利用生成对抗网络实现单细胞RNA测序数据的真实感仿真生成与增强** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-019-14018-z)\n1. [2019年《自然·方法》] **利用迁移学习进行单细胞转录组数据去噪** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-019-0537-1)\n1. [2018年《自然·方法》] **面向单细胞转录组学的深度生成建模** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-018-0229-2)\n\n\n## 批次效应校正\n1. [2023年《生物信息学》] **CLAIRE：基于对比学习的批次校正框架，可在批次混合与细胞异质性保持之间取得更好平衡** [[论文]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Fadvance-article\u002Fdoi\u002F10.1093\u002Fbioinformatics\u002Fbtad099\u002F7055295)\n1. [2020年《基因组生物学》] **单细胞RNA测序数据批次效应校正方法的基准评估** [[论文]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-019-1850-9)\n1. [2020年《自然·生物技术》] **使用参考样本对单细胞RNA测序技术进行多中心基准测试的研究** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-020-00748-9)\n1. [2019年《自然·方法》，**Harmony**] **利用Harmony快速、灵敏且准确地整合单细胞数据** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-019-0619-0)\n1. [2018年《自然·生物技术》，**CCA**] **跨条件、技术和物种整合单细胞转录组数据** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fnbt.4096)\n1. [2018年《自然·生物技术》，**互近邻法**] **通过匹配互近邻校正单细胞RNA测序数据中的批次效应** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fnbt.4091)\n1. [2018年《自然·方法》] **用于评估单细胞RNA测序批次效应校正效果的测试指标** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-018-0254-1)\n1. [2017年《自然·生物技术》] **利用天然遗传变异进行多重液滴单细胞RNA测序** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fnbt.4042)\n\n## 肿瘤微环境-TME\n1. [2023年bioRxiv] **健康与炎症组织中的空间共现识别（ISCHIA）** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.02.13.526554v1)\n1. [2023年bioRxiv] **从血液免疫单细胞转录组数据预测头颈部癌症患者的肿瘤免疫微环境及检查点治疗反应** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.01.17.524455v1)\n1. [2022年《自然·生物医学工程》] **利用图深度学习，基于组织样本中的空间蛋白质谱表征肿瘤微环境** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41551-022-00951-w)\n1. [2022年《自然·通讯》] **SOTIP是一种利用空间组学数据进行微环境建模的多功能方法** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-34867-5)\n\n## 细胞间通讯事件\n1. [2024年《Nature Methods》] **基于细胞表型的组织细胞微环境无监督与有监督发现** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-02124-2)\n1. [2024年《Nature Reviews Genetics》] **研究细胞—细胞相互作用与通讯方法的多样化** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41576-023-00685-8)\n1. [2024年bioRxiv] **利用生物启发图学习对空间图谱中的细胞微环境进行大规模表征** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2024.02.21.581428v2.full.pdf)\n1. [2024年Pac Symp Biocomput] **PEPSI：基于空间蛋白质组学成像的极性测量提示免疫细胞参与** [[论文]](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F38160302\u002F)\n1. [2023年《Cell Systems》] **用于细胞间通讯的单细胞A\u002FB测试** [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2405471223001503)\n1. [2023年《Nature Biotechnology》] **在单细胞分辨率下推断细胞—细胞通讯** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-023-01834-4)\n1. [2022年bioRxiv] **scTensor从单细胞RNA测序数据中检测多对多的细胞—细胞相互作用** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.12.07.519225v1)\n1. [2022年《Nature Biotechnology》] **利用细胞空间图建模组织中的细胞间通讯** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-022-01467-z)\n1. [2022年bioRxiv] **通过空间转录组学的多视图图学习解码功能性细胞—细胞通讯事件** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.06.22.496105v1)\n1. [2021年《Bioinformatics》] **在空间单细胞表达数据中识别信号基因** [[论文]](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F32886099\u002F)\n1. [2020年《Nature Methods》] **NicheNet：通过将配体与靶基因关联来建模细胞间通讯** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-019-0667-5)\n1. [2020年《Nature Communications》] **使用NATMI预测细胞间通讯网络** [[论文]](https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC7538930\u002Fpdf\u002F41467_2020_Article_18873.pdf)\n1. [2018年《Nature》] **人类早期母胎界面的单细胞重建** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-018-0698-6)\n\n\n## 基因调控网络\n1. [2023年arXiv] **DynGFN：利用GFlowNets实现基因调控网络的贝叶斯推断** [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.04178.pdf)\n1. [2023年《Bioinformatics》] **STGRNS：一种基于Transformer的可解释方法，用于从单细胞转录组数据推断基因调控网络** [[论文]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F39\u002F4\u002Fbtad165\u002F7099621)\n1. [2022年《Nature Machine Intelligence》] **基于图神经网络从单细胞ATAC-seq数据推断转录因子调控网络** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-022-00469-5)\n1. [2022年《Nature Biotechnology》] **利用图嵌入整合多组学单细胞数据并进行调控推断** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-022-01284-4.pdf)\n1. [2022年Biorxiv] **scMEGA：基于增强子的单细胞多组学基因调控网络推断** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.08.10.503335v1)\n1. [2022年《Bioinformatics》] **高性能大规模单细胞基因调控网络推断：Inferelator 3.0** [[论文]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F38\u002F9\u002F2519\u002F6533443)\n1. [2022年《Briefings in Bioinformatic》] **SIGNET：基于多层感知机Bagging的单细胞RNA-seq数据基因调控网络预测** [[论文]](https:\u002F\u002Facademic.oup.com\u002Fbib\u002Farticle\u002F23\u002F1\u002Fbbab547\u002F6484519)\n1. [2020年《Nature Methods》] **单细胞转录组数据基因调控网络推断算法的基准测试** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-019-0690-6)\n1. [2019年《Genome Biology》] **单细胞转录组学揭示基因调控网络的可塑性** [[论文]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-019-1713-4)\n1. [2017年《Cell Syst》] **利用多元信息度量从单细胞数据推断基因调控网络** [[论文]](https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC5624513\u002F)\n\n\n## 插补\n1. [2018年《Nature Communications》] **一种准确且鲁棒的单细胞RNA测序数据插补方法scImpute** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-018-03405-7)\n1. [2019年《Genome Biology》] **DeepImpute：一种准确、快速且可扩展的深度神经网络方法，用于插补单细胞RNA测序数据** [[论文]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-019-1837-6)\n1. [2018年《Cell》] **利用数据扩散从单细胞数据中恢复基因互作** [[论文]](https:\u002F\u002Fwww.cell.com\u002Fcell\u002Ffulltext\u002FS0092-8674(18)30724-4)\n1. [2018年《Genome Biology》] **VIPER：保留变异性的插补方法，用于在单细胞RNA测序研究中准确恢复基因表达** [[论文]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-018-1575-1)\n1. [2021年《PLOS Computational Biology》] **G2S3：一种基于基因图的单细胞RNA测序数据插补方法** [[论文]](https:\u002F\u002Fjournals.plos.org\u002Fploscompbiol\u002Farticle?id=10.1371\u002Fjournal.pcbi.1009029)\n1. [2021年《Nature Communications》] **scGNN是一种用于单细胞RNA测序分析的新颖图神经网络框架** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-021-22197-x#Sec23)\n1. [2021年《iScience》] **结合图卷积和自编码器神经网络对单细胞RNA测序数据进行插补** [[论文]](https:\u002F\u002Fwww.cell.com\u002Fiscience\u002Ffulltext\u002FS2589-0042(21)00361-8)\n1. [2022年《PLOS ONE》] **针对稀疏scChIP-seq数据插补的单细胞特异性和可解释性机器学习模型** [[论文]](https:\u002F\u002Fjournals.plos.org\u002Fplosone\u002Farticle?id=10.1371\u002Fjournal.pone.0270043)\n\n## 空间域\n1. [2023年《自然·遗传学》] **SPICEMIX实现细胞身份的整合单细胞空间建模** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41588-022-01256-z)\n1. [2023年bioRxiv] **CellCharter：一种可扩展的框架，用于绘制和比较跨多个样本及空间组学技术的细胞微环境** [[预印本]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.01.10.523386v1)\n1. [2022年《基因组研究》] **基于模型的约束深度学习聚类方法，用于解析空间分辨的单细胞数据** [[论文]](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F36198490\u002F)\n1. [2022年《自然·通讯·生物学》] **利用空间转录组学解析组织结构与功能** [[综述论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42003-022-03175-5)\n1. [2022年《基因组生物学》] **用于空间分辨转录组数据分析的统计与机器学习方法** [[综述论文]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Ftrack\u002Fpdf\u002F10.1186\u002Fs13059-022-02653-7.pdf)\n1. [2022年《自然·通讯》] **利用自适应图注意力自动编码器从空间分辨转录组数据中解析空间区域** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-29439-6)\n1. [2022年《自然·计算科学》] **基于图神经网络的空间转录组数据细胞聚类** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs43588-022-00266-5)\n1. [2022年《遗传学前沿》] **空间转录组数据的分析与可视化** [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.07787.pdf)\n1. [2021年《自然·方法》] **SpaGCN：通过图卷积网络整合基因表达、空间位置和组织学信息，以识别空间区域及空间变异基因** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-021-01255-8)\n1. [2021年《自然·生物技术》] **使用BayesSpace实现亚斑点分辨率的空间转录组学** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-021-00935-2)\n1. [2021年bioRxiv] **无监督的空间嵌入式深度表征用于空间转录组学** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2021.06.15.448542v2)\n1. [2021年《基因组生物学》] **Giotto：用于整合分析和可视化空间表达数据的工具箱** [[工具]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Ftrack\u002Fpdf\u002F10.1186\u002Fs13059-021-02286-2.pdf)\n1. [2021年bioRxiv] **利用深度学习从空间分辨转录组数据中定义并可视化人类组织的病理结构** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2021.07.08.451210v2)\n1. [2020年bioRxiv] **stLearn：整合空间位置、组织形态和基因表达，以在未解离组织中发现细胞类型、细胞间相互作用及空间轨迹** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2020.05.31.125658v1)\n1. [2018年《自然·方法》] **SpatialDE：空间变异基因的鉴定** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fnmeth.4636)\n1. [2018年《自然·生物技术》] **结合单细胞RNA测序和连续荧光原位杂交数据识别空间关联亚群** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fnbt.4260)\n1. [2008年《统计力学杂志》] **大型网络中社区层次结构的快速展开** [[论文]](https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F1742-5468\u002F2008\u002F10\u002FP10008)\n\n\n## 参考嵌入或迁移学习\n1. [2019年《自然·方法》] **单细胞转录组学中的迁移学习去噪** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-019-0537-1)\n1. [2018年《自然·方法》] **单细胞转录组学的深度生成建模** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-018-0229-2)\n1. [2020年《生物信息学》] **利用迁移VAE对未配对数据进行条件性分布外生成** [[论文]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F36\u002FSupplement_2\u002Fi610\u002F6055927?guestAccessKey=71253caa-1779-40e8-8597-c217db539fb5&login=false)\n1. [2021年《自然·生物技术》] **通过迁移学习将单细胞数据映射到参考图谱** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-021-01001-7)\n1. [2021年《分子系统生物学》] **利用深度生成模型对单细胞转录组数据进行概率性协调与注释** [[论文]](https:\u002F\u002Fwww.embopress.org\u002Fdoi\u002Ffull\u002F10.15252\u002Fmsb.20209620)\n1. [2022年bioRxiv预印本] **基于生物学知识的深度学习，用于推断单细胞中的基因程序活性** [[预印本]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.02.05.479217v2)\n\n## 细胞分割\n1. [2023 biorxiv] **Bering：基于迁移图嵌入的空间转录组学联合细胞分割与注释** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.09.19.558548v1.full.pdf)\n1. [2022 Cytometry A] **MIRIAM：用于多维组织图像的机器学习和深度学习单细胞分割与定量分析流程** [[论文]](https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fepdf\u002F10.1002\u002Fcyto.a.24541)[[代码]](https:\u002F\u002Fgithub.com\u002FCoffey-Lab\u002FMIRIAM)(MIRIAM)\n1. [2021 Nature Biotechnology] **基于成像的空间转录组学中的细胞分割** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-021-01044-w) \n1. [2021 Biorxiv] **Scellseg：一种具有预训练和对比度微调功能的风格感知细胞实例分割工具** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2021.12.19.473392v1) [[代码]](https:\u002F\u002Fgithub.com\u002Fcellimnet\u002Fscellseg-publish)\n1. [2021 Nature Biotechnology] **基于成像的空间转录组学中的细胞分割** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-021-01044-w) [[代码]](https:\u002F\u002Fgithub.com\u002Fkharchenkolab\u002FBaysor)(Baysor)\n1. [2021 Nature Biotechnology] **利用大规模数据标注和深度学习实现人类水平性能的组织图像全细胞分割** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-021-01094-0) [[代码]](https:\u002F\u002Fgithub.com\u002Fvanvalenlab\u002Fintro-to-deepcell)(Memser)\n1. [2021 Nature Methods] **Cellpose：一种通用的细胞分割算法** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-020-01018-x) [[代码]](https:\u002F\u002Fwww.github.com\u002Fmouseland\u002Fcellpose)(Cellpose)\n1. [2021 Molecular Systems Biology]**空间转录组学的联合细胞分割与细胞类型注释** [[论文]](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F34057817\u002F) [[代码]](https:\u002F\u002Fgithub.com\u002Fwollmanlab\u002FJSTA) (JSTA)\n1. [2020 Nature Communications]**卷积神经网络以高精度分割酵母显微镜图像** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-020-19557-4) [[代码]](http:\u002F\u002Fgithub.com\u002Flpbsscientist\u002FYeaZ-GUI)\n1. [2020 Medical Image Analysis] **DeepDistance：一种用于倒置显微镜图像中细胞检测的多任务深度回归模型** [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS1361841520300840?via%3Dihub) (DeepDistance)\n1. [2016 Computational Biology]**深度学习自动化了活细胞成像实验中单个细胞的定量分析** [[论文]](https:\u002F\u002Fjournals.plos.org\u002Fploscompbiol\u002Farticle?id=10.1371\u002Fjournal.pcbi.1005177) [[代码]](https:\u002F\u002Fgithub.com\u002Fvanvalenlab\u002Fdeepcell-tf) (Deepcell)\n\n## 细胞类型去卷积\n1. [2023 Genome Biology] **Smoother：一种统一且模块化的框架，用于在空间组学数据中整合结构依赖性** [[论文]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-023-03138-x)\n1. [2023 BioRxiv] **RETROFIT：空间转录组学中无参考的细胞类型混合物去卷积** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.06.07.544126v1)\n1. [2023 BioRxiv] **STdGCN：利用图卷积网络在空间转录组数据中进行精确的细胞类型去卷积** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.03.10.532112v1)\n1. [2023 BioRxiv] **Spotless：一个可重复的空间转录组学细胞类型去卷积基准测试流程** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.03.22.533802v1)\n1. [2022 Nature Biotechnology] **使用CytoSPACE实现单细胞与空间转录组图谱的高分辨率对齐** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-023-01697-9)\n1. [2022 Nature Communications] **无参考的多细胞像素分辨率空间解析转录组数据的细胞类型去卷积** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-30033-z)\n1. [2022 Nature Biotechnology] **DestVI识别空间转录组数据中的细胞类型连续体** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-022-01272-8)\n1. [2022 Biorxiv] **采用无批次效应策略在空间转录组学中进行精确的细胞类型去卷积** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.12.15.520612v1)\n1. [2022 Nature Biotechnology] **面向空间转录组学的空间信息引导的细胞类型去卷积** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-022-01273-7#Sec2)\n1. [2022 Nature Cancer] **通过BayesPrism进行细胞类型和基因表达去卷积，可在肿瘤学领域实现批量与单细胞RNA测序的贝叶斯整合分析** [[论文]](https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs43018-022-00356-3)\n1. [2022 Nature Communications] **混合细胞去卷积技术的进步使得能够量化空间转录组数据中的细胞类型** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-28020-5#Sec2)\n1. [2022 Nature Biotechnology] **Cell2location绘制空间转录组中的细粒度细胞类型图谱** [[论文]](https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41587-021-01139-4)\n1. [2021 Briefings in Bioinformatics] **DSTG：通过基于图的人工智能对空间转录组数据进行去卷积** [[论文]](https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbib\u002Fbbaa414)\n1. [2021 Genome Research] **利用单细胞参考对批量基因表达数据进行基于似然的去卷积** [[论文]](https:\u002F\u002Fwww.genome.org\u002Fcgi\u002Fdoi\u002F10.1101\u002Fgr.272344.120.)\n1. [2021 Genome Biology] **SpatialDWLS：精确的空间转录组数据去卷积** [[论文]](https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13059-021-02362-7)\n1. [2021 Nucleic Acids Research] **SPOTlight：基于种子的非负矩阵分解回归，用于将空间转录组斑点与单细胞转录组去卷积** [[论文]](https:\u002F\u002Fdoi.org\u002F10.1093\u002Fnar\u002Fgkab043)\n1. [2021 Nature Biotechnology] **空间转录组中细胞类型混合物的稳健分解** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-021-00830-w)\n1. [2019 Nature Communications] **从基因表达数据中准确估算细胞类型组成** [[论文]](https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41467-019-10802-z)\n1. [2019 Science] **Slide-seq：一种可扩展的高空间分辨率全基因组表达测量技术** [[论文]](https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.aaw1219)\n\n## 细胞类型注释\n1. [2025 BioRxiv] **大型语言模型共识显著提升单细胞RNA测序数据的细胞类型注释准确度** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2025.04.10.647852v1) [[代码]](https:\u002F\u002Fgithub.com\u002Fcafferychen777\u002FmLLMCelltype)\n1. [2023 biorxiv] **跨组织单细胞注释模型的扩展** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.10.07.561331v1.full.pdf) \n1. [2023 Nature Methods] **基于分割引导对比学习的神经元网络多层图谱** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-023-02059-8) \n1. [2023 Nature Methods] **Cue：一种用于结构变异发现和基因分型的深度学习框架** [[论文]](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F36959322\u002F) \n1. [2023 Nature Communications] **用于一站式可解释细胞类型注释的Transformer模型** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-35923-4) \n1. [2023 Nature Biotech] **TACCO统一了单细胞和空间组学中的细胞身份注释转移与分解** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-023-01657-3) \n1. [2022 Nature Method] **利用STELLAR对空间分辨单细胞数据进行注释** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-022-01651-8) \n注意：已注释参考细胞图 + 查询细胞图\n1. [2022 Brief Bioinform] **scIAE：一种基于集成自编码器的单细胞RNA测序数据分类框架** [[论文]](https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbib\u002Fbbab508)\n1. [2022 Nature Communications] **scGCN是一种用于单细胞组学知识迁移的图卷积网络算法** [[论文]](https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41467-021-24172-y)\n1. [2022 Science] **跨组织免疫细胞分析揭示人类组织特异性特征** [[论文]](https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC7612735\u002F#S9)\n1. [2022 Bioinformatics] **CellMeSH：利用索引文献进行概率性细胞类型鉴定** [[论文]](https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbioinformatics\u002Fbtab834)\n1. [2022 Cancers] **用于基因表达建模的Transformer（T-GEM）：一种基于基因表达的表型预测的可解释深度学习模型** [[论文]](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F36230685\u002F)\n1. [2021 Nucleic Acids Research] **scDeepSort：一种基于加权图神经网络的深度学习预训练单细胞转录组细胞类型注释方法** [[论文]](https:\u002F\u002Fdoi.org\u002F10.1093\u002Fnar\u002Fgkab775)\n1. [2021 BMC Bioinformatics] **使用图卷积网络进行单细胞分类** [[论文]](https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12859-021-04278-2)\n1. [2021 Genome Research] **半监督对抗神经网络用于单细胞分类** [[论文]](https:\u002F\u002Fgenome.cshlp.org\u002Fcontent\u002F31\u002F10\u002F1781)\n1. [2020 BMC Bioinformatics] **EnClaSC：一种新颖的集成方法，用于准确且稳健的单细胞转录组细胞类型分类** [[论文]](https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12859-020-03679-z)\n1. [2020 Bioinformatics] **ACTINN：单细胞RNA测序中细胞类型自动识别工具** [[论文]](https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbioinformatics\u002Fbtz592)\n1. [2020 Nature Communications] **SciBet作为便携式快速单细胞类型鉴定工具** [[论文]](https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41467-020-15523-2)\n1. [2019 Nucleic Acids Research] **SuperCT：一种用于增强单细胞转录组图谱特征化的监督学习框架** [[论文]](https:\u002F\u002Fdoi.org\u002F10.1093\u002Fnar\u002Fgkz116)\n1. [2019 Nucleic Acids Research] **CHETAH：一种选择性、层次化的单细胞RNA测序细胞类型鉴定方法** [[论文]]( https:\u002F\u002Fdoi.org\u002F10.1093\u002Fnar\u002Fgkz543)\n1. [2019 Bioinformatics] **scMatch：一款利用参考数据集进行单细胞基因表达谱注释的工具** [[论文]](https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbioinformatics\u002Fbtz292)\n1. [2019 Cell Systems] **SingleCellNet：一个跨平台、跨物种的单细胞RNA测序数据分类计算工具** [[论文]](https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cels.2019.06.004)\n1. [2019 Genome Biology] **SingleCellNet：cPred：一种基于监督学习的单细胞RNA测序数据细胞类型分类精确方法** [[论文]](https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13059-019-1862-5)\n\n\n\n## 细胞聚类\n1. [2023 Bioinformatics] **scBGEDA：通过双去噪自编码器结合二部图集成聚类的深度单细胞聚类分析** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.01.15.524109v1](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F39\u002F2\u002Fbtad075\u002F7025496))\n1. [2023 bioRxiv] **G3DC：一种基于基因—图引导的选择性深度聚类方法，用于单细胞RNA测序数据** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.01.15.524109v1)\n1. [2022 BMC Bioinformatics] **SC3s：将单细胞共识聚类高效扩展至数百万细胞** [[论文]](https:\u002F\u002Fbmcbioinformatics.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs12859-022-05085-z)\n1. [2022 Bioinformatics] **基于图神经网络的嵌入用于scRNA-seq数据聚类** [[论文]](https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbioinformatics\u002Fbtab787)\n1. [2022 AAAI] **基于ZINB的图嵌入自编码器用于单细胞RNA测序数据解读** [[论文]]( https:\u002F\u002Faaai-2022.virtualchair.net\u002Fposter_aaai5060)\n1. [2022 Briefings in Bioinformatics] **通过自编码器和图神经网络联合实现单细胞RNA测序数据的深度结构聚类** [[论文]]( https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbib\u002Fbbac018)\n1. [2022 Bioinformatics] **scGAC：一种用于聚类单细胞RNA测序数据的图注意力架构** [[论文]]( https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbioinformatics\u002Fbtac099)\n1. [2022 Nature Computational Science] **利用图神经网络对空间转录组数据进行细胞聚类** [[论文]]( https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs43588-022-00266-5)\n1. [2021 Nature Communications] **基于模型的深度嵌入用于单细胞RNA测序数据的约束聚类分析** [[论文]]( https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-021-22008-3)\n1. [2020 NAR Genomics and Bioinformatics] **针对单细胞RNA序列数据的自训练软K-means深度聚类** [[论文]]( https:\u002F\u002Fdoi.org\u002F10.1093\u002Fnargab\u002Flqaa039)\n1. [2020 Nature Communications] **深度学习使单细胞RNA测序分析在去除批次效应的同时实现精准聚类** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-020-15851-3)[[网站]](https:\u002F\u002Feleozzr.github.io\u002Fdesc\u002F)[[github]](https:\u002F\u002Fgithub.com\u002Feleozzr\u002Fdesc)\n1. [2019 Nature Machine Intelligence] **采用基于模型的深度学习方法对单细胞RNA测序数据进行聚类** [[论文]]( https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-019-0037-0)\n\n\u003C!--\n## 细胞轨迹\n1. [2017 Nature Communications] **利用深度学习重建细胞周期和疾病进展** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-017-00623-3)\n-->\n\n## 疾病预测\n1. [2024年《自然·生物技术》] **单细胞生物学能否实现精准医学的承诺？** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-024-02138-x)\n1. [2018年IJCAI] **基于关系网络与局部图卷积滤波的混合方法用于乳腺癌亚型分类** [[论文]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F2018\u002F490)\n1. [2021年《NPJ数字医学》] **DeePaN——一种整合临床基因组证据的深度患者图卷积网络，用于对受益于免疫治疗的肺癌进行分层** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41746-021-00381-z)\n1. [2022年《Biocumputing》] **CloudPred：从单细胞RNA测序数据中预测患者表型** [[论文]](https:\u002F\u002Fwww.worldscientific.com\u002Fdoi\u002Fabs\u002F10.1142\u002F9789811250477_0031)\n1. [2022年CHIL '20：ACM健康、推理与学习会议论文集] **利用图注意力网络从单细胞数据中预测疾病状态** [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3368555.3384449)\n\n## 多模态整合\n1. [2024年《自然·方法学》] **在单细胞分辨率下跨空间组学样本进行搜索与匹配** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41592-024-02410-7)\n1. [2023年《自然·生物技术》] **利用弱关联特征实现跨模态的空间与单细胞数据整合** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-023-01935-0)\n1. [2023年《自然·通讯》] **scDREAMER：基于深度生成模型与对抗性分类器的配对方法，用于单细胞数据集的图谱级整合** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-023-43590-8)\n1. [2023年bioRxiv] **自动化的单细胞组学端到端框架，具备数据驱动的批次推断功能** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.11.01.564815v1)\n1. [2023年《自然·生物技术》] **多模态单细胞数据的整合** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-023-01826-4)\n1. [2023年《自然·生物技术》] **利用弱关联特征实现跨模态的空间与单细胞数据整合** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-023-01935-0)\n1. [2023年《Briefings in Bioinformatics》] **基于图卷积网络的单细胞多组学数据通用整合框架** [[论文]](https:\u002F\u002Facademic.oup.com\u002Fbib\u002Fadvance-article\u002Fdoi\u002F10.1093\u002Fbib\u002Fbbad081\u002F7079707)\n1. [2022年PMLR] **CVQVAE：一种基于表示学习的多组学单细胞数据整合方法** [[论文]](https:\u002F\u002Fproceedings.mlr.press\u002Fv200\u002Fliu22a.html)\n1. [2022年《自然·生物技术》] **利用图链接嵌入进行多组学单细胞数据整合及调控推断** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-022-01284-4)\n1. [2022年《自然·通讯》] **使用多模态深度学习方法对单细胞多组学数据进行聚类** [[综述]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-022-35031-9)\n1. [2022年《Genome Biology》] **基于深度学习的多组学数据融合方法在癌症领域的基准研究** [[综述]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-022-02739-2)\n1. [2018年ICML] **MAGAN：对生物流形进行对齐** [[论文]](https:\u002F\u002Fproceedings.mlr.press\u002Fv80\u002Famodio18a.html)\n1. [2019年《PLoS计算生物学》] **利用关联自组织映射从scATAC-seq和scRNA-seq数据构建基因调控网络** [[论文]](https:\u002F\u002Fjournals.plos.org\u002Fploscompbiol\u002Farticle?id=10.1371\u002Fjournal.pcbi.1006555)\n1. [2020年《Bioinformatics》] **SCIM：针对未配对特征集的通用单细胞匹配方法** [[论文]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F36\u002FSupplement_2\u002Fi919\u002F6055906)\n1. [2021年《Nature Communications》] **利用自编码器实现单细胞成像与测序数据之间的多领域转换** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-020-20249-2)\n1. [2021年《PLoS计算生物学》] **通过图正则化张量补全法推算空间分辨转录组数据** [[论文]](https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F33826608\u002F)\n1. [2021年《Genome Biology》] **Cobolt：多模态单细胞测序数据的整合分析工具** [[论文]](https:\u002F\u002Fgenomebiology.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13059-021-02556-z)\n1. [2021年《Cell Reports Methods》] **用于单细胞多组学数据综合分析的专家混合深度生成模型** [[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2667237521001235)\n1. [2021年《Briefings in Bioinformatics》] **单细胞转录组与开放染色质可及性数据的深度联合学习分析模型** [[论文]](https:\u002F\u002Facademic.oup.com\u002Fbib\u002Farticle\u002F22\u002F4\u002Fbbaa287\u002F5985290)\n1. [2021年《Bioinformatics》] **用于单细胞多组学数据联合分析的深度跨组学循环注意力模型** [[论文]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F37\u002F22\u002F4091\u002F6283577)\n1. [2022年《自然·生物技术》] **scJoint利用迁移学习整合图谱尺度的单细胞RNA-seq和ATAC-seq数据** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41587-021-01161-6)\n1. [2022年《Bioinformatics》] **SMILE：基于互信息学习的单细胞组学数据整合方法** [[论文]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Farticle\u002F38\u002F2\u002F476\u002F6384571)\n1. [2022年SIGKDD] **用于多模态单细胞数据整合的图神经网络** [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3534678.3539213)\n1. [2022年《Genome Biology》] **scDART：同时整合未配对的scRNA-seq和scATAC-seq数据并学习跨模态关系** [[论文]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1186\u002Fs13059-022-02706-x)\n1. [2019年bioRxiv] **单细胞中RNA表达与表面蛋白丰度的联合模型** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F791947v1.abstract)\n1. [2021年bioRxiv] **DeepMAPS：利用异构图Transformer进行单细胞生物网络推断** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2021.10.31.466658v2)\n1. [2022年bioRxiv] **配对单细胞多组学数据之间的自适应机器翻译** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2021.01.27.428400v2)\n1. [2022年bioRxiv] **Multigrate：单细胞多组学数据整合工具** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2022.03.16.484643v1.abstract)\n1. [2019年NeurIPS多组学多语言预训练] **跨语言语言模型预训练** [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1901.07291.pdf)\n\n## 多组学跨模态转换\n1. [2024年《自然·通讯》] **scButterfly：一种基于双对齐变分自编码器的通用单细胞跨模态转换方法** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-024-47418-x)\n1. [2023年arXiv scHyena] **scHyena：用于脑部全长单细胞RNA测序分析的基础模型** [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.02713)\n1. [2023年bioRxiv scTranslator] **一种用于将单细胞转录组数据翻译为蛋白质组数据的预训练大型语言模型** [[论文]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2023.07.04.547619v1)\n1. [2023年《先进科学》] **利用深度学习高效生成配对的单细胞多组学图谱** [[论文]](https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Ffull\u002F10.1002\u002Fadvs.202301169)\n1. [2022年《细胞生物学杂志》] **基于半监督学习的多模态单细胞转换与对齐** [[论文]](https:\u002F\u002Fwww.liebertpub.com\u002Fdoi\u002Ffull\u002F10.1089\u002Fcmb.2022.0264)\n1. [2022年《自然·机器智能》sciPENN] **一种多用途深度学习方法，用于CITE-seq和单细胞RNA测序数据的整合，并实现细胞表面蛋白的预测与填补** [[论文]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-022-00545-w)\n1. [2022年RECOMB] **使用Polarbear进行半监督单细胞跨模态转换** [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1007\u002F978-3-031-04749-7_2)\n1. [2020年《美国国家科学院院刊》] **BABEL实现单细胞分辨率下多组学图谱之间的跨模态转换** [[论文]](https:\u002F\u002Fwww.pnas.org\u002Fdoi\u002Fabs\u002F10.1073\u002Fpnas.2023070118)","# awesome-deep-learning-single-cell-papers 快速上手指南\n\n`awesome-deep-learning-single-cell-papers` 并非一个可安装的软件工具或代码库，而是一个**精选论文列表资源库**。它汇集了利用深度学习方法进行单细胞分析的最新学术论文，并按任务类型进行了分类整理。\n\n因此，本指南旨在指导开发者如何高效地获取、浏览和利用该资源库中的学术内容，以辅助科研开发工作。\n\n## 环境准备\n\n由于本项目本质上是文档和链接集合，无需复杂的系统依赖，仅需具备以下基础环境即可：\n\n*   **操作系统**：Windows, macOS 或 Linux 均可。\n*   **必备工具**：\n    *   **Web 浏览器**：用于直接在 GitHub 网页版浏览分类目录和论文链接。\n    *   **Git**（可选）：如果你希望将列表克隆到本地进行离线查阅或通过 PR 贡献内容。\n*   **前置知识**：\n    *   熟悉单细胞测序技术（scRNA-seq, Spatial Transcriptomics 等）基本概念。\n    *   具备深度学习基础，了解 Transformer, GAN, Diffusion Model 等在生物信息学中的应用场景。\n\n## 安装步骤（获取资源）\n\n你可以通过以下两种方式获取该论文列表：\n\n### 方式一：在线浏览（推荐）\n直接访问 GitHub 仓库页面，利用右侧目录导航快速查找特定领域的论文：\n> 地址：https:\u002F\u002Fgithub.com\u002FOmicsML\u002Fawesome-deep-learning-single-cell-papers\n\n### 方式二：克隆到本地\n如果你需要离线查看或计划提交 Pull Request 补充新论文，请使用 Git 克隆：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FOmicsML\u002Fawesome-deep-learning-single-cell-papers.git\ncd awesome-deep-learning-single-cell-papers\n```\n\n*注：国内用户若遇到克隆速度慢的问题，可使用国内镜像源（如 Gitee 镜像，若有）或配置 Git 代理加速。*\n\n## 基本使用\n\n本资源库的核心价值在于其**分类索引**和**文献追踪**。以下是最高效的使用流程：\n\n### 1. 按任务领域查找论文\n打开 `README.md` 文件，根据你当前的研究任务，点击对应的目录跳转。主要涵盖领域包括：\n\n*   **基础模型与大语言模型**：查看 `Pretrained Model or LLM or Foundation Model` 章节，获取如 `scGPT`, `CellPLM`, `GeneGPT` 等前沿模型论文。\n*   **数据生成与增强**：参考 `GAN or Diffusion Model` 及 `Single Cell Data Simulation` 章节。\n*   **具体分析方法**：\n    *   细胞类型注释：`Cell Type Annotation`\n    *   批次效应校正：`Batch Effect Correction`\n    *   多组学整合：`Multimodal Integration` \u002F `Multiomics Translation`\n    *   空间转录组：`Spatialtemporal Transcriptomic` \u002F `Spatial Domain`\n*   **教程与书籍**：新手可优先阅读 `Book` 和 `Course` 章节提供的链接（如 Broad Institute 的 scRNA-seq 分析课程）。\n\n### 2. 获取论文原文与代码\n在列表中找到感兴趣的论文标题后：\n1.  点击 **[[paper]]** 链接直达期刊官网或 arXiv\u002FbioRxiv 预印本页面下载 PDF。\n2.  通常论文标题旁或摘要中会包含指向官方代码仓库的链接（若未直接列出，建议在论文中搜索 \"Code\" 或 \"GitHub\" 关键字）。\n\n### 3. 引用综述文章\n若在研究中使用了该列表整理的思路，建议引用其配套的综述论文以支持开源社区：\n\n```bibtex\n@article{molho2024deep,\n  title={Deep learning in single-cell analysis},\n  author={Molho, Dylan and Ding, Jiayuan and Tang, Wenzhuo and Li, Zhaoheng and Wen, Hongzhi and Wang, Yixin and Venegas, Julian and Jin, Wei and Liu, Renming and Su, Runze and others},\n  journal={ACM Transactions on Intelligent Systems and Technology},\n  volume={15},\n  number={3},\n  pages={1--62},\n  year={2024},\n  publisher={ACM New York, NY}\n}\n```\n\n### 4. 贡献最新论文\n发现遗漏的重要论文？欢迎通过以下方式贡献：\n*   **提交 Issue**：在仓库 Issues 区留言推荐论文。\n*   **提交 Pull Request**：克隆仓库后，按照现有格式在 `README.md` 对应分类下添加论文链接，并提交 PR。","某生物信息学博士正在开展一项关于肿瘤微环境中细胞通讯机制的研究，急需寻找最新的深度学习模型来整合单细胞转录组与空间转录组数据。\n\n### 没有 awesome-deep-learning-single-cell-papers 时\n- **文献检索如大海捞针**：需要在 PubMed、arXiv 和 Google Scholar 上手动组合\"single-cell\"、\"deep learning\"、\"spatial\"等关键词，耗时数天仍难以覆盖最新预印本。\n- **技术分类混乱难辨**：找到的论文混杂了聚类、插补、批效应校正等不同任务，难以快速筛选出专门针对“细胞间通讯”或“多模态整合”的特定算法。\n- **复现资源缺失**：许多论文摘要未直接提供代码链接或基准测试对比，导致评估模型可用性和复现难度极高，容易选错工具浪费实验时间。\n- **领域前沿脱节**：难以系统性追踪从传统 CNN\u002FRNN 到最新 Foundation Model（基础模型）的技术演进，可能还在使用已过时的方法处理数据。\n\n### 使用 awesome-deep-learning-single-cell-papers 后\n- **一站式精准获取**：直接查阅\"Cell-Cell Communication Events\"和\"Spatialtemporal Transcriptomic\"分类章节，几分钟内即可锁定 2024 年最新的几篇核心论文及对应代码库。\n- **任务导向清晰导航**：利用仓库细致的任务分类（如 Multimodal Integration、Imputation），迅速排除无关干扰，专注于解决肿瘤微环境特有的数据稀疏与空间定位问题。\n- **资源链路完整**：每个条目均关联原始论文、代码实现甚至相关综述，快速确认模型是否经过 Benchmarking 验证，大幅降低试错成本。\n- **紧跟技术浪潮**：通过\"Pretrained Model or LLM\"等板块，及时了解到将大语言模型迁移至单细胞分析的最新范式，为研究引入更先进的特征提取策略。\n\nawesome-deep-learning-single-cell-papers 将原本数周的文献调研工作压缩至数小时，让研究人员能从繁琐的信息搜集中解放出来，全身心投入到算法优化与生物学发现中。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FOmicsML_awesome-deep-learning-single-cell-papers_0b85d1dc.png","OmicsML","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002FOmicsML_aa1571cb.jpg","LLMs & Generative AI for omics data analysis (non-profit organization))","https:\u002F\u002Fjiayuanding100.github.io\u002F","https:\u002F\u002Fgithub.com\u002FOmicsML",847,113,"2026-04-13T12:22:58","Apache-2.0","","未说明",{"notes":88,"python":86,"dependencies":89},"该仓库是一个论文和资源列表（Awesome List），并非可执行的软件工具或代码库，因此没有具体的运行环境、依赖库或硬件需求。它主要提供指向各类单细胞深度学习论文、书籍、课程和外部工具链接的索引。",[],[18],"2026-03-27T02:49:30.150509","2026-04-14T03:09:51.340431",[],[]]