MultiMAP: dimensionality reduction and integration of multimodal data.
Jain, Mika Sarkin
Conde, Cecilia Dominguez
Botting, Rachel A
Teichmann, Sarah A
Springer Science and Business Media LLC
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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
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.
Barts Charity (MGU045)
Wellcome Trust (WT206194, WT211276/Z/18/Z)
Chan Zuckerberg Initiative (CZF2019-002445)
External DOI: https://doi.org/10.1186/s13059-021-02565-y
This record's URL: https://www.repository.cam.ac.uk/handle/1810/333249
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/