Single-Cell RNA Sequencing

Also known as scRNA-seq

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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

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

  1. Donor-Aware scRNA-seq Benchmarks for IBD Classification

    May 5, 2026Jonathan MuhireMedical DiagnosisSingle-Cell RNA Sequencing

    Department of Computer Science, Oklahoma Christian University

  2. CellxPert: Inference-Time MCMC Steering of a Multi-Omics Single-Cell Foundation Model for In-Silico Perturbation

    Apr 30, 2026Andac Demir, Erik W. Anderson, Jeremy L. Jenkins +1Single-Cell Perturbation ModelingGenomics

    Novartis Biomedical Research

  3. Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability

    Apr 7, 2026Yuheng Liang, Lucy Chhuo, Ahmadreza Argha +8Clinical Outcome PredictionOncology

    UNSW BioMedical Machine Learning Lab (BML), School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia · Garvan Institute of Meidical Research, Sydney, NSW, 2000, Australia · School of Biomedical Engineering, UNSW, Sydney, NSW, 2052, Australia +7

  4. A Hierarchical Sheaf Spectral Embedding Framework for Single-Cell RNA-seq Analysis

    Mar 27, 2026Xiang Xiang Wang, Guo-Wei WeSingle-Cell RNA SequencingTopological Representation Learning

    Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA. · Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA. · Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA.

  5. A Large-Scale Comparative Analysis of Imputation Methods for Single-Cell RNA Sequencing Data

    Mar 25, 2026Yuichiro Iwashita, Ahtisham Fazeel Abbasi, Koichi Kise +2Incomplete Data ImputationSingle-Cell RNA Sequencing

    RPTU University Kaiserslautern-Landau, Department of Computer Science, 67663 Kaiserslautern, Germany. · German Research Center for Artificial Intelligence (DFKI GmbH), 67663 Kaiserslautern, Germany. · Graduate School of Informatics, Osaka Metropolitan University, Osaka 599-8531, Japan. +1

  6. ELISA: An Interpretable Hybrid Generative AI Agent for Expression-Grounded Discovery in Single-Cell Genomics

    Mar 12, 2026Omar CoserAI Agents for Scientific DiscoverySingle-Cell Transcriptomics

  7. Count Bridges enable Modeling and Deconvolving Transcriptomic Data

    Mar 5, 2026Nic Fishman, Gokul Gowri, Tanush Kumar +4Generative ModelingSingle-Cell RNA Sequencing

    Harvard University · MIT · CNRS +3

  8. Parameter-free representations outperform single-cell foundation models on downstream benchmarks

    Feb 18, 2026Huan Souza, Pankaj MehtaSingle-Cell RNA SequencingLinear Representation Hypothesis

    Department of Physics, Boston University, Boston, MA, 02215, USA · Faculty of Computing and Data Science, Boston University, Boston, MA, 02215, USA