PuSH - Publication Server of Helmholtz Zentrum München

Li, S.* ; Luecken, M. ; Marioni, J.C.* ; Teichmann, S.A.* ; He, P.*

Toward informed batch correction for single-cell transcriptome integration.

Nat. Comput. Sci. 6, 123-133 (2026)
DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
Over the past decade, single-cell datasets have grown in both size and complexity, enabling the construction of large-scale cell atlases. Technical variability in data generation, also known as batch effects, hinders meaningful comparisons. Although numerous batch-correction algorithms have been developed, they often struggle with overcorrection or undercorrection. Here we review commonly used data cleaning and integration methods. We envision that future frameworks will learn interpretable gene and cell representations and achieve informed modeling of technical and biological variation.
Altmetric
Additional Metrics?
[➜Log in]
Edit extra informations Login
Publication type Article: Journal article
Document type Review
Keywords Rna-sequencing Data; Expression; Seq; Atlas; Lung
ISSN (print) / ISBN 2662-8457
e-ISSN 2662-8457
Journal Nature Computational Science
Quellenangaben Volume: 6, Issue: 2, Pages: 123-133 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Campus, 4 Crinan St, London, N1 9xw, England
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Institute of Lung Health and Immunity (LHI)
Grants The Ageing Biology Foundation
CZI data ecosystem grant
UC | UC San Francisco | School of Medicine, University of California, San Francisco (UCSF School of Medicine)