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Lotfollahi, M. ; Hao, Y.* ; Theis, F.J. ; Satija, R.*

The future of rapid and automated single-cell data analysis using reference mapping.

Cell 187, 2343-2358 (2024)
Postprint DOI PMC
Open Access Green
As the number of single-cell datasets continues to grow rapidly, workflows that map new data to well-curated reference atlases offer enormous promise for the biological community. In this perspective, we discuss key computational challenges and opportunities for single-cell reference-mapping algorithms. We discuss how mapping algorithms will enable the integration of diverse datasets across disease states, molecular modalities, genetic perturbations, and diverse species and will eventually replace manual and laborious unsupervised clustering pipelines.
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Publication type Article: Journal article
Document type Review
Keywords Cross-species Comparisons ; Machine Learning ; Multimodal Analysis ; Reference Mapping ; Single-cell Analysis; Chromatin Accessibility; Atlas; Prediction; Rna
Language english
Publication Year 2024
HGF-reported in Year 2024
ISSN (print) / ISBN 0092-8674
e-ISSN 1097-4172
Journal Cell
Quellenangaben Volume: 187, Issue: 10, Pages: 2343-2358 Article Number: , Supplement: ,
Publisher Cell Press
Publishing Place Cambridge, Mass.
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503800-001
Grants National Institutes of Health
BMBF
European Union
Helmholtz Association's Initiative and Networking Fund through Helmholtz AI
Chan Zuckerberg Initiative
Joachim Herz Stiftung via Addon Fellowships for Interdisciplinary Life Science
DOI 10.1016/j.cell.2024.03.009
WOS ID 001240477300001
Scopus ID 85192155563
PubMed ID 38729109
Erfassungsdatum 2024-06-11
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