Single-Cell Transcriptomics

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6 papers in the last four weeks, with none the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 37

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Finsler Flow Matching: Dynamics-Aware Geodesic Interpolation for Single-Snapshot Trajectory Inference
Oct 8, 2026Niklas Canova, Jonas Simon FleckFlow-Based Generative Modeling Single-Cell Transcriptomics
Institute of Human Biology (IHB), Roche, Basel, Switzerland
CellMSA: Context Modeling for Single-Cell Representation Learning
Sep 30, 2026Suyuan Zhao, Minghao Liu, Yizhen Luo +1Representation Learning Single-Cell Transcriptomics
Institute for AI Industry Research (AIR), Tsinghua University · Department of Computer Science and Technology, Tsinghua University · Tsinghua Institute of Multidisciplinary Biomedical Research (TIMBR), Tsinghua University +2
DRIFT: Disentangled Responsive-Invariant Flow Transport for Single-Cell Perturbation Prediction
Sep 28, 2026Mustapha Bounoua, Giulio Franzese, Pietro MichiardiDisentangled Representation Learning Conditional Flow Matching
Department of Data Science EURECOM, France
P2P: Cross-View Population Denoising for Unpaired Single-Cell Perturbation Response Prediction
Sep 28, 2026Haojie Yang, Ran SuSingle-Cell Perturbation Modeling Single-Cell Transcriptomics
College of Intelligence and Computing Tianjin University, Tianjin, China
KoopCell: Koopman-Based Generative Model for Learning Single-Cell Dynamics from Distribution Snapshots
Sep 27, 2026Wanfeng Lu, Yutong Zhang, Keyi Zhou +3Koopman Operator Learning Latent Dynamics Modeling
School of Mathematical Sciences, SCMS, SCAM, and CCSB, Fudan University, China · Research Institute of Intelligent Complex Systems, Fudan University, China · State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, China +1
Towards a knowledge-enhanced single-cell foundation model
Sep 14, 2026Hanqing Zhang, Jie Bao, Mei Ma +7Language Model Pretraining Single-Cell Transcriptomics
College of Animal Science and Technology, China Agricultural University, Beijing 100193, China · College of Information and Electrical Engineering, China Agricultural University, Beijing 100193, China
scDEFT: A deep learning framework for drug-effect prediction and counterfactual reasoning
Sep 9, 2026Murthy DevarakondaSingle-Cell Perturbation Modeling Clinical Outcome Prediction
Causal Data AI (causaldata.ai)
VOICE: A Vision-Omics Foundation Model Integrating Direct and Retrieval-Based Prediction of In-situ Single-Cell Gene Expression
Aug 8, 2026Xin Luo, Yicheng Tao, Haoxuan Zeng +6Computational Pathology Spatial Transcriptomics
University of Michigan Ann Arbor, Michigan, USA · Weill Cornell Medicine New York, New York, USA
BioM-JEPA: joint-embedding prediction of graph-connected gene blocks in single cells
Aug 6, 2026Yuhao Wang, Zelin Zang, Yuxuan Liu +2Joint-Embedding Predictive Architecture Predictive Representation Learning
Westlake University, Hangzhou, Zhejiang, China · Centre for Artificial Intelligence and Robotics (CAIR), Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences (HKISI-CAS), Hong Kong SAR, China · State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing, China +1
CohortHijack: Robustness of Single Cell Annotation to Companion Cell Removal
Aug 6, 2026Arash Vashagh, Yasmin VashaghAdversarial Robustness Single-Cell Transcriptomics
Faculty of Computer Science, University of New Brunswick, Fredericton, New Brunswick E3B 5A3, Canada · Farzanegan Amin 2 High School, Isfahan, Iran
Scaling an Autoregressive Transformer for Single-Cell Generation
Aug 3, 2026Aleksandr Sharipov, Yusif Mukhtarov, Igor MolybogTransformer Autoregressive Generation
HawAII, University of Hawai’i at Manoa
Beyond Gene Reconstruction: Learning Cell Representations through Complementary Transcriptomic Views
Aug 2, 2026Jiaqi Xiong, Yuntao hu, Yu Zheng +3Contrastive Learning Representation Learning
University of Oxford, Oxford, United Kingdom · Tongji University, Shanghai, China · Hunan University, Changsha, China +2
Making Single-Cell Data Distillation Auditable: Traceable Real-Cell Coresets via Discrete Min--Max Selection
Jul 20, 2026Yaodi Luo, Peize He, Lingbei Meng +4Dataset Distillation Coreset Selection
The Chinese University of Hong Kong, Shenzhen · Shenzhen Loop Area Institute, Shenzhen · Shenzhen Research Institute of Big Data, Shenzhen, China
CoDiffGRN: Rethinking Gene Regulatory Network Inference via the BEELINE-KGC Benchmark and Co-evolutionary Discrete Diffusion
Jul 14, 2026Jiaze Song, Runhao Zhao, Minghao Xu +2Link Prediction Gene Regulatory Network Inference
Peking University · National University of Defense Technology
SpaCellAgent: A Self-Evolving LLM-Based Multi-Agent Framework for Trajectory Analysis
Jul 8, 2026Songhan Wang, Haoang Chi, He Li +6Spatial Transcriptomics LLM Agent Orchestration
University of Shanghai for Science and Technology · National University of Defense Technology · Hong Kong Baptist University +3
PerturbCellRL: Verifier-Guided Reinforcement Learning for Single-Cell Perturbation Prediction
Jun 26, 2026Dongxia Wu, Mingyu Li, Yuhui Zhang +4Single-Cell Perturbation Modeling Reinforcement Learning with Verifiable Rewards
Peking University Beijing, China · Stanford University Stanford, CA
scBench-Long: Verifiable Benchmarking of Long-Horizon Single-Cell Biology
Jun 25, 2026Ian Diks, Zhen Yang, Arjun Banerjee +2AI Agents for Scientific Discovery Long-Horizon Agent Evaluation
LatchBio, San Francisco, CA, USA
Privacy-preserving federated tensor decomposition of single-cell immune data: recovering multicellular programs across institutions
Jun 22, 2026Axel Faes, Stephanie M. van den Berg, Maryam Amir HaeriFederated Learning Aggregation Tensor Decomposition
Data Science Institute, Hasselt University, Belgium · University of Twente, The Netherlands · Learning, Data Analytics and Technology, University of Twente, The Netherlands
OCOO-T : A Simple and Scalable Virtual Cell Model for Transcriptional Perturbation Response Prediction
Jun 11, 2026Danning Jiang, Zheming An, Yalong Zhao +1Single-Cell Perturbation Modeling Single-Cell Transcriptomics
INFevo
CisTransCell: Single-Cell Perturbation Prediction via Gene Function, Regulatory Control, and Cellular Context
Jun 10, 2026Wei Zhang, Xun Jiang, Yuesi Xi +1Single-Cell Perturbation Modeling Genomics
1L3S Research Center, Leibniz Universität Hannover, Germany · School of Clinical Medicine & Laboratory Medicine, Jiangsu University, China · Institute for Information Processing (tnt), Leibniz Universität Hannover, Germany.
scTranslation: A Comprehensive Benchmark for Single-Cell Multi-Omics Modality Translation
Jun 2, 2026Jiabei Cheng, Jingbo Zhou, Jun Xia +4Multimodal Model Evaluation Single-Cell Transcriptomics
Westlake University Hangzhou, Zhejiang, China · Shanghai Jiao Tong University Shanghai, China · Zhejiang University Hangzhou, Zhejiang, China +3
GC-MoE: Genomics-Guided Cell-Type-Specific Mixture of Experts for Histology-Based Single-Cell Spatial Transcriptomics
Jun 1, 2026Kaito Shiku, Ahtisham Fazeel Abbasi, Ryoma Bise +4Computational Pathology Spatial Transcriptomics
Kyushu University, Japan · German Research Center for Artificial Intelligence (DFKI GmbH), Germany · RPTU University Kaiserslautern-Landau, Germany +3
Prior-Guided Multi-Omic Transformers for Single-Cell Gene Regulatory Network Inference
May 30, 2026Tianyang Xu, Tianci Liu, Niraj Rayamajhi +4Transformer Gene Regulatory Network Inference
Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA · Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, IN, USA · School of Biological Sciences, Illinois State University, Normal, IL, USA
CellBRIDGE: Learning Cellular Trajectories via Interaction-Aware Alignment
May 28, 2026Silas Ruhrberg Estévez, Nicolas Huynh, Tennison Liu +4Single-Cell Transcriptomics Optimal Transport
DAMTP, University of Cambridge · Francis Crick Institute · University of Colorado Anschutz Medical Campus
Applications of temporal graph learning for predicting the dynamics of biological systems
May 27, 2026Manuel Dileo, Andrea SottorivaTemporal GNNs Temporal Link Prediction
Center for Computational Biology, Human Technopole Foundation, Milan, Italy
Chreode: A Cell World Model for One-Step Temporal Dynamics and Perturbation Prediction
May 27, 2026Mufan Qiu, Genhui Zheng, Yinuo Xu +4World Model Learning Latent Dynamics Modeling
University of North Carolina at Chapel Hill · The University of Texas at Austin · University of Pennsylvania
From Snapshots to Trajectories: Learning Single-Cell Gene Expression Dynamics via Conditional Flow Matching
May 21, 2026Siyu Pu, Qingqing Long, Xiaohan Huang +7Latent Dynamics Modeling Conditional Flow Matching
Computer Network Information Center, Chinese Academy of Sciences · University of California, Los Angeles
Modeling Temporal scRNA-seq Data with Latent Gaussian Process and Optimal Transport
May 20, 2026Mehmet Yigit Balik, Harri LähdesmäkiLatent Dynamics Modeling Conditional Generative Modeling
Department of Computer Science, Aalto University, Espoo, Finland.
PACE: Geometry-Aware Bridge Transport for Single-Cell Trajectory Inference
May 18, 2026Chenglei Yu, Chuanrui Wang, Bangyan Liao +1Trajectory Inference Single-Cell Transcriptomics
Zhejiang University · Department of Artificial Intelligence, School of Engineering, Westlake University
Multiscale Supervised Unbalanced Optimal Transport Flow Matching
May 15, 2026Qiangwei Peng, Lezhi Chen, Peijie ZhouFlow Matching Single-Cell Transcriptomics
College of Electrical Engineering Sichuan University, Sichuan, China · AI for Science Institute, Beijing, China

Oct 8, 2026, cs.AIJ/K move · Enter open · S save

Finsler Flow Matching: Dynamics-Aware Geodesic Interpolation for Single-Snapshot Trajectory Inference

Niklas Canova, Jonas Simon Fleck

Institute of Human Biology (IHB), Roche, Basel, Switzerland

Flow-Based Generative Modeling Single-Cell Transcriptomics Single-Cell RNA Sequencing Conditional Flow Matching

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Single-cell snapshot data can resolve a continuum of cellular states but do not uniquely determine the dynamics governing transitions between them. However, additional dynamical information can often be encoded in a cell-cell Markov transition kernel. Existing generative approaches for single cell trajectory inference either infer transport only from population marginals, impose a symmetric geometry on the state space, or incorporate directionality through a single velocity vector at each observed state. We introduce Finsler Flow Matching (FFM), a framework for learning continuous stochastic dynamics from discrete Markov transition graphs. We use the first and second local moments to construct a Finsler structure motivated by the Freidlin--Wentzell action, where the second moment determines anisotropic accessibility and the first moment introduces a preferred direction of motion. We learn neural approximations of the resulting directed geodesics, use their Finsler cost to construct source-target couplings, and define geometry-aware stochastic conditional paths that can be distilled into a continuous generative process through simulation-free score and flow matching. Across synthetic and single-cell trajectory inference benchmarks, FFM improves recovery of withheld intermediate populations, particularly when the transition dynamics are strongly directional or anisotropic. Our results provide a principled route from discrete transition probabilities to continuous generative dynamics while retaining both directional and diffusive structure.

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