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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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Publication type Article: Journal article
Document type Review
Corresponding Author
ISSN (print) / ISBN 1548-7091
e-ISSN 1548-7105
Journal Nature Methods
Quellenangaben Volume: 21, Issue: 9, Pages: 1597-1602 Article Number: , Supplement: ,
Publisher Nature Publishing Group
Publishing Place New York, NY
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)