Single-Cell Transcriptomics

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Single-cell transcriptomics measures RNA in individual cells to reveal biological differences that bulk measurements can hide, helping researchers study development, disease, and treatment response. Common approaches include clustering and differential-expression analysis, integration of spatial and multi-omic data, gene regulatory network inference, and perturbation screens. Machine-learning methods range from latent-variable models and optimal-transport or flow-matching methods for trajectories and response distributions to Transformer foundation models for annotation, generation, and transfer. A central challenge is whether models generalize across donors, tissues, and unseen perturbations, and whether strong prediction scores capture causal biology rather than shared expression patterns; batch effects, limited data, and inconsistent evaluation complicate comparisons.

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65 papers8 papers in the last four weeks, up 33% on the four weeks before. 0.0% of all new papers.

BASILISC: Bayesian Alignment of Siamese-Integrated Latents for Inferring Single-cell Correspondence
Oct 28, 2026Sepideh Saran, Mahsa Ghanbari, Uwe OhlerCross-Modal Alignment Multi-Omics Data Integration
The Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Germany · Department of Electrical Engineering and Computer Science, Technical University of Berlin, Germany · Department of Biology, Humboldt University of Berlin, Germany +1
Learning Perturbation-Specific Representations through Conditional Energy Prior for Cellular Response Prediction
Oct 13, 2026Yuchen Yan, Yatao BianSingle-Cell Perturbation Modeling Representation Learning
Department of Computer Science, National University of Singapore
Understanding Gene Expression-to-Image Learning: Spatial Structure and Information Fidelity
Oct 13, 2026Xiaoqing Lian, Pei Liu, Jiahui Tan +3Single-Cell Transcriptomics Visual Representation Learning
College of Computer Science and Electronic Engineering, Hunan University, Changsha, China · University of Electronic Science and Technology of China, Chengdu, China · Xiamen University, Xiamen, China +2
CloneOT: predicting clone-specific perturbation responses with lineage-supervised neural inverse optimal transport
Oct 12, 2026Yanshuo Chen, Wei Chen, Heng HuangSingle-Cell Perturbation Modeling Inverse Optimal Transport
Department of Computer Science, University of Maryland, College Park, MD, 20742, USA. · Center of Bioinformatics and Computational Biology, College Park, MD, 20740, USA. · Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15261, USA. +2
Finsler Flow Matching: Dynamics-Aware Geodesic Interpolation for Single-Snapshot Trajectory Inference
Oct 8, 2026Niklas Canova, Jonas Simon FleckFinsler Geometry Flow-Based Generative Modeling
Institute of Human Biology (IHB), Roche, Basel, Switzerland
Predictive Multiplicity in Cell-Fate Assignment: Label-Free Rashomon Sets and the Limits of Per-Cell Certification
Oct 8, 2026Arjun Bhupatiraju, Abhiram BhupatirajuSingle-Cell Transcriptomics Selective Prediction
McNeil High School, Austin, Texas
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
Generalizable single-cell perturbation response prediction using energy-guided flow matching
Sep 27, 2026Jianan Wei, Jiajun Hong, Guikun Chen +3Single-Cell Perturbation Modeling Single-Cell Transcriptomics
Zhejiang University, Hangzhou, Zhejiang, China · Beijing Institute for General Artificial Intelligence, Beijing, China · Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies, Chengdu, Sichuan, China
AmbiModBench: Benchmarking Gene Perturbation Prediction Beyond Shared Responses
Sep 26, 2026Sikai Huang, Zhiwen Yang, Kai Yu +2Benchmark Design Single-Cell Perturbation Modeling
Fujian Agriculture and Forestry University · The Hong Kong Polytechnic University · Westlake University
Rethinking Class Imbalance for Single-Cell Foundation Models: A Systematic Benchmark Across Architectures and Long-Tail Loss Functions
Sep 20, 2026Zeyu Dong, Jiahui ZhongClass-Imbalanced Learning Long-Tailed Classification
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)
DeMixPert: Decomposed Response Modeling with Gaussian Mixtures for OOD Single-Cell Perturbation Prediction
Aug 24, 2026Jiawen Liu, Xuechenxiao Cao, Yutong Li +5Single-Cell Perturbation Modeling Single-Cell Transcriptomics
School of Computer Science and Engineering, South China University of Technology, Guangzhou, China · School of Future Technology, South China University of Technology, Guangzhou, China
Privacy-Preserving Detection of Rare Disease-Associated Cell Subsets via Secure Multi-Party Computation
Aug 20, 2026Ş. Selcan Magara, Esther Havemann, Debora Jutz +2Privacy-Preserving ML Secure Multi-Party Computation
Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany · Medical Data Privacy and Privacy-Preserving Machine Learning, University of Tübingen, Tübingen, Germany · Lab for Artificial Intelligence in Medicine, Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany +1
D2R2: Discrete Diffusion with Regulation Reinforcement for Single-Cell Perturbation Prediction
Aug 15, 2026Ninghan Fan, Qi Liu, Xunuo Zhu +9Single-Cell Perturbation Modeling Single-Cell Transcriptomics
Zhejiang University · City University of Hong Kong
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
Graph-structured joint-embedding prediction for single-cell transcriptomics
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 Privacy-Preserving Federated Learning
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
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BASILISC: Bayesian Alignment of Siamese-Integrated Latents for Inferring Single-cell Correspondence

Sepideh Saran, Mahsa Ghanbari, Uwe Ohler

The Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Germany · Department of Electrical Engineering and Computer Science, Technical University of Berlin, Germany · Department of Biology, Humboldt University of Berlin, Germany+1

Cross-Modal Alignment Multi-Omics Data Integration Uncertainty Quantification Single-Cell Transcriptomics Bayesian Neural Networks

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Single-cell assays study the genome one cell at a time. Unimodal assays measure one molecular layer, the newer multimodal assays measure several. Most data are single-modality, so integrating them relies on models trained on multimodal assays, where the cell correspondence across modalities is known. These models return a matching, but rarely a confidence measure. We present BASILISC (Bayesian Alignment of Siamese-Integrated Latents for Inferring Single-cell Correspondence), a framework for matching single-cell representations with uncertainty on the correspondence itself. A BASILISC model is a probabilistic siamese network over the two modalities with a predictive distribution over the correspondence for a cross-modal pair of cells. We instantiate it at two levels: at the cell-type level the question is whether two cells share a cell type, at the cell level which cell is the partner in the other modality. To our knowledge, this is the first uncertainty score on a learned cross-modal correspondence evaluated against a known ground-truth pairing. BASILISC learns a similarity across modalities that reflects cell lineage: an unseen cell type is matched to its lineage relative, matching still works on such held-out types (balanced accuracy 0.86 on unseen against 0.93 on seen types), and the predictive uncertainty rises on these out-of-distribution cells. The uncertainty score also selects high-quality matches for downstream analysis: discarding the least certain fifth of the pairs raises accuracy from 0.93 to 0.98. At the cell level, a deep ensemble of the deterministic matcher reaches 0.062 matching accuracy on the benchmark test set, above the competition winner; on the Bayesian matcher's assignment, the posterior flags the wrong matches that cross a cell-type boundary better than a deterministic read of the same scores (AUROC 0.68 against 0.64).