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The future of rapid and automated single-cell data analysis using reference mapping.
Cell 187, 2343-2358 (2024)
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
ISSN (print) / ISBN
0092-8674
e-ISSN
1097-4172
Journal
Cell
Quellenangaben
Volume: 187,
Issue: 10,
Pages: 2343-2358
Publisher
Cell Press
Publishing Place
Cambridge, Mass.
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)
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
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