PuSH - Publikationsserver des Helmholtz Zentrums 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 möglich sobald Postprint bei der ZB eingereicht worden ist.
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
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Rna-sequencing Data; Expression; Seq; Atlas; Lung
ISSN (print) / ISBN 2662-8457
e-ISSN 2662-8457
Zeitschrift Nature Computational Science
Quellenangaben Band: 6, Heft: 2, Seiten: 123-133 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Campus, 4 Crinan St, London, N1 9xw, England
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Institute of Lung Health and Immunity (LHI)
Förderungen The Ageing Biology Foundation
CZI data ecosystem grant
UC | UC San Francisco | School of Medicine, University of California, San Francisco (UCSF School of Medicine)