MultiMAP: dimensionality reduction and integration of multimodal data.
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Authors
Jain, Mika Sarkin
Polanski, Krzysztof
Conde, Cecilia Dominguez
Chen, Xi
Park, Jongeun
Mamanova, Lira
Knights, Andrew
Botting, Rachel A
Stephenson, Emily
Haniffa, Muzlifah
Lamacraft, Austen
Teichmann, Sarah A
Publication Date
2021-12-20Journal Title
Genome Biol
ISSN
1474-7596
Publisher
Springer Science and Business Media LLC
Volume
22
Issue
1
Language
eng
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Jain, M. S., Polanski, K., Conde, C. D., Chen, X., Park, J., Mamanova, L., Knights, A., et al. (2021). MultiMAP: dimensionality reduction and integration of multimodal data.. Genome Biol, 22 (1) https://doi.org/10.1186/s13059-021-02565-y
Abstract
Multimodal data is rapidly growing in many fields of science and engineering, including single-cell biology. We introduce MultiMAP, a novel algorithm for dimensionality reduction and integration. MultiMAP can integrate any number of datasets, leverages features not present in all datasets, is not restricted to a linear mapping, allows the user to specify the influence of each dataset, and is extremely scalable to large datasets. We apply MultiMAP to single-cell transcriptomics, chromatin accessibility, methylation, and spatial data and show that it outperforms current approaches. On a new thymus dataset, we use MultiMAP to integrate cells along a temporal trajectory. This enables quantitative comparison of transcription factor expression and binding site accessibility over the course of T cell differentiation, revealing patterns of expression versus binding site opening kinetics.
Sponsorship
Barts Charity (MGU045)
Wellcome Trust (WT206194, WT211276/Z/18/Z)
Chan Zuckerberg Initiative (CZF2019-002445)
Identifiers
34930412, PMC8686224
External DOI: https://doi.org/10.1186/s13059-021-02565-y
This record's URL: https://www.repository.cam.ac.uk/handle/1810/333249
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