Single-Cell RNA Sequencing

Also known as scRNA-seq

Ask about this topic

Overview

Single-cell RNA sequencing (scRNA-seq) measures RNA in individual cells, helping researchers identify cell types and states and study disease, development, and responses to drugs or gene perturbations. Common approaches include count normalization, clustering and dimensionality reduction, graph-based analysis, and methods for integrating datasets while correcting batch effects. Generative models, transformers, and foundation models are also used to represent cells, predict perturbation responses, and infer trajectories or gene regulation. A central challenge is making results robust across donors, protocols, and tissues while preserving real biological differences; complex models do not consistently outperform carefully tuned simpler methods.

Latest papers

CardsList

38 papers6 papers in the last four weeks, up 100% on the four weeks before. 0.0% of all new papers.

  1. Reducing double-dipping in single-cell topic models with three-way count splitting

    Oct 16, 2026Zheng QuTopic ModelingSingle-Cell RNA Sequencing

    Fei Tian College Northern Campus, Middletown, NY, 10940, USA

  2. scHFBiMamba: a bidirectional Mamba autoencoder for cardiac disease-state classification from single-nucleus RNA-seq data

    Oct 10, 2026Ugo Lomoio, Pietro Hiram Guzzi, Pierangelo VeltriAutoencodersSingle-Cell RNA Sequencing

    Department of Surgical and Medical Sciences University “Magna Græcia” of Catanzaro 88100 Catanzaro, Italy · DIMES University of Calabria 88063 Rende, Italy

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

    Oct 8, 2026Niklas Canova, Jonas Simon FleckFinsler GeometryFlow-Based Generative Modeling

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

  4. TempoBridge: Source-Conditioned Flow Matching with Optimal Transport Couplings for Single-Cell Population Transitions

    Oct 4, 2026Bowen Han, Lingbei Meng, Shihuan Luo +7Single-Cell Perturbation ModelingConditional Flow Matching

    Universiti Sains Malaysia · The Chinese University of Hong Kong Shenzhen · Shenzhen Loop Area Institute +3

  5. CellMSA: Context Modeling for Single-Cell Representation Learning

    Sep 30, 2026Suyuan Zhao, Minghao Liu, Yizhen Luo +1Representation LearningSingle-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

  6. scTrilemma: Balancing Identity, Invariance, and Fidelity in Single-Cell Representation Learning

    Sep 30, 2026Yunhak Oh, Yoonho Lee, Junseok Lee +9Variational AutoencodersRepresentation Learning

    KAIST · Work done during an internship at HITS · HITS

  7. CRNDiff: Count-Native Diffusion Framework via Chemical Reaction Networks

    Sep 25, 2026Yuxuan Qiu, Praful Gagrani, Tetsuya J KobayashiDiffusion ModelsConditional Diffusion Models

    Institute of Industrial Science The University of Tokyo Tokyo 153-8505, Japan

  8. Towards a knowledge-enhanced single-cell foundation model

    Sep 14, 2026Hanqing Zhang, Jie Bao, Mei Ma +7Language Model PretrainingSingle-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

  9. PopPert: Population-level Joint-Distribution Modeling for Single-Cell Perturbation Prediction

    Sep 1, 2026Handong Wang, Jiaxin Qi, Haochen Feng +1Single-Cell Perturbation ModelingSingle-Cell RNA Sequencing

    Computer Network Information Center, Chinese Academy of Sciences · University of Chinese Academy of Sciences

  10. Improving scDiffusion with Sparsity-Biased Classifier-Free Guidance

    Jul 31, 2026Yu Song, Hao Sun, Ikuko Nishikawa +1Classifier-Free GuidanceDiffusion Sampling

    College of Information Science and Engineering, Ritsumeikan University, Osaka, Japan

  11. PerturbMap: Cross-Context Transfer of Single-Cell Perturbation Responses

    Jul 30, 2026Panpan Cui, Yiqi Liu, Wenhao SunSingle-Cell Perturbation ModelingCross-Domain Transfer Learning

    School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences · Institute of Computing Technology, CAS; University of Chinese Academy of Sciences · 3Hong Kong University of Science and Technology

  12. When Does Deep Representation Learning Help Single-Cell Clustering? A Sensitivity-Aware Diagnostic Benchmark for Biomedical AI Pipelines

    Jul 28, 2026Nguyen Thanh Phong, Truong Viet Vu, Nguyen Ha Thu +4Representation LearningClustering

    Faculty of Information Technology, Van Lang School of Technology, Van Lang University, Ho Chi Minh City, Vietnam · Faculty of Law, Van Lang University, Ho Chi Minh City, Vietnam

  13. SCTA: An Agentic Framework for Stable and Interpretable Target Gene Discovery from Single-Cell RNA Sequencing

    Jul 26, 2026Shuyu Chen, Chen Zhu, Ye Zhang +3AI Agents for Scientific DiscoveryScientific Workflow Automation

    Siebel School of Computing and Data Science University of Illinois Urbana-Champaign Champaign, IL, USA · School of Information Sciences University of Illinois Urbana-Champaign Champaign, IL, USA · Novartis Biomedical Research Biomedical Research Cambridge, MA, USA +1

  14. Score Distributions, Not Cells: Evaluating Single-Cell Perturbations Under Class Overlap

    Jul 6, 2026Youssef Marrakchi, Davide D'Ascenzo, Sebastiano Cultrera di MontesanoSingle-Cell Perturbation ModelingClassification

    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA. · Department of Computer Science, University of Milan, Milan, Italy. · Department of Control and Computer Engineering, Politecnico di Torino, Torino, Italy. +1

  15. Modeling Cell-Cycle-Aware Single-Cell Drug Perturbation Responses

    Jun 29, 2026Dingping Zhao, Jie Lin, Feng Xu +1Single-Cell Perturbation ModelingSingle-Cell RNA Sequencing

    Division of Pharmacognosy, School of Pharmaceutical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University, Beijing, China · Department of Computer Science at School of Informatics, Xiamen University, Xiamen, China · Peking University Health Science Center, Peking University, Beijing, China

  16. scKDGM: KAN-guided Dynamic Graph Masked Learning for Single-Cell RNA-seq Clustering

    Jun 26, 2026Jun Tang, Pengwei Hu, Sicong Gao +3Graph SSLClustering

    College of Computer and Information Science, Southwest University, Chongqing, China · Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China

  17. scGTN: Deep Siamese Graph Transformer Network for Single-cell RNA Sequencing Clustering

    Jun 17, 2026Jinke Wu, Yifan Wang, Siyu Yi +5Graph SSLClustering

    Sichuan University · University of International Business and Economics · Great Bay University +1

  18. scLLM-DSC: LLM-Knowledge Enhanced Cross-Modal Deep Structural Clustering for Single-Cell RNA Sequencing

    Jun 11, 2026Ping Xu, Pengjiang Li, Tian Du +6ClusteringUnsupervised Clustering

    Computer Network Information Center, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · School of Computing and Information Technology, Great Bay University, Dongguan, China +2

  19. Integrating gene regulatory priors into Transformer attention with scTransformer for interpretable scRNA-seq analysis

    Jun 8, 2026Mikele Milia, Louis Fabrice Tshimanga, Henning Mueller +2Transformer InterpretabilityTransformer Attention

    Department of Information Engineering, University of Padova, Padova, Italy · Department of Neuroscience, University of Padova, Padova, Italy · Padova Neuroscience Center, Padova, Italy +3

  20. scCBGM: Interpretable Single-Cell Counterfactual Editing

    Jun 5, 2026Alma Andersson, Aya Abdelsalam Ismail, Edward De Brouwer +6Concept Bottleneck ModelsCausal Counterfactual Generation

    Genentech · Guide Labs · Department of Computer Science, New York University +1

  21. Single-Cell Cross-Modal Transfer by Adversarial Fine-Tuning of Foundation Models

    Jun 4, 2026Joseph Boyd, Matthew Lyon, Martino Mansoldo +2Spatial TranscriptomicsFine-Tuning

    GSK.ai, GlaxoSmithKline, London, United Kingdom.

  22. BRIDGE: Biological Evidence Refinement and Heterogeneous Dynamic Gating for Gene Regulatory Networks

    Jun 2, 2026Ziyang Dong, Shanwen Tan, Hengchuang Yin +5Heterogeneous GNNsGraph SSL

    College of Computer Science, Sichuan University, Chengdu, 610065, China · Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China · School of Mathematics, Sichuan University, Chengdu, 610065, China +2

  23. CellBRIDGE: Learning Cellular Trajectories via Interaction-Aware Alignment

    May 28, 2026Silas Ruhrberg Estévez, Nicolas Huynh, Tennison Liu +4Single-Cell TranscriptomicsOptimal Transport

    DAMTP, University of Cambridge · Francis Crick Institute · University of Colorado Anschutz Medical Campus

  24. Geometry-First Generative Spatial Single-Cell Reconstruction

    May 27, 2026Ehtesamul Azim, Muhtasim Noor Alif, Tae Hyun Hwang +2Spatial TranscriptomicsGenomics

    University of Central Florida Orlando, Florida, USA · Vanderbilt University Medical Center Nashville, Tennessee, USA · Arizona State University Tempe, Arizona, USA

  25. Learning Latent Dynamical Causal Processes for Single-Cell Perturbation Prediction

    May 25, 2026Wenkang Jiang, Yuhang Liu, Erdun Gao +3Causal Representation LearningLatent Dynamics Modeling

    AIML, Adelaide University Adelaide, Australia · Responsible AI Research Centre AIML, Adelaide University Adelaide, Australia · Monash University Melbourne, Australia +1

  26. scHelix: Asymmetric Dual-Stream Integration via Explicit Gene-Level Disentanglement

    May 18, 2026Xichen Yan, Zelin Zang, Changxi Chi +8Batch Effect CorrectionRepresentation Disentanglement

    Jinan University · Westlake University · HKISI +1

  27. scShapeBench: Discovering geometry from high dimensional scRNAseq data

    May 12, 2026Andrew J Steindl, João Felipe Rocha, Brian Tshilengi Di Bassinga +13Single-Cell RNA SequencingTopological Data Analysis

    Yale University · Mila / Université de Montréal · Garvan Institute of Medical Research +2

  28. Prototype Guided Post-pretraining for Single-Cell Representation Learning

    May 8, 2026Sachini Weerasekara, Natasha Darras, Sagar Kamarthi +2Representation LearningContinued Pretraining

    Sachini Weerasekara∗ · Natasha Darras · Sagar Kamarthi +2

  29. GATHER: Convergence-Centric Hyper-Entity Retrieval for Zero-Shot Cell-Type Annotation

    May 7, 2026Zhonghui Zhang, Feng Jiang, Shaowei Qin +2Zero-Shot LearningGraph Retrieval-Augmented Generation

    Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology Shenzhen, China · Software College, Northeastern University Shenyang, China

  30. When Does Gene Regulatory Network Inference Break? A Controlled Diagnostic Study of Causal and Correlational Methods on Single-Cell Data

    May 6, 2026Miguel Fernandez-de-Retana, Ruben Sanchez-Corcuera, Unai Zulaika +2Causal DiscoveryGene Regulatory Network Inference

    Faculty of Engineering University of Deusto Bilbao, Spain