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Johnston, K.G.* ; Grieco, S.F.* ; Nie, Q.* ; Theis, F.J. ; Xu, X.*

Small data methods in omics: The power of one.

Nat. Methods 21, 1597-1602 (2024)
DOI PMC
Over the last decade, biology has begun utilizing 'big data' approaches, resulting in large, comprehensive atlases in modalities ranging from transcriptomics to neural connectomics. However, these approaches must be complemented and integrated with 'small data' approaches to efficiently utilize data from individual labs. Integration of smaller datasets with major reference atlases is critical to provide context to individual experiments, and approaches toward integration of large and small data have been a major focus in many fields in recent years. Here we discuss progress in integration of small data with consortium-sized atlases across multiple modalities, and its potential applications. We then examine promising future directions for utilizing the power of small data to maximize the information garnered from small-scale experiments. We envision that, in the near future, international consortia comprising many laboratories will work together to collaboratively build reference atlases and foundation models using small data methods.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Korrespondenzautor
ISSN (print) / ISBN 1548-7091
e-ISSN 1548-7105
Zeitschrift Nature Methods
Quellenangaben Band: 21, Heft: 9, Seiten: 1597-1602 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort New York, NY
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)