Journal
GENOME BIOLOGY
Volume 22, Issue 1, Pages -Publisher
BMC
DOI: 10.1186/s13059-021-02565-y
Keywords
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Funding
- Gates Cambridge Scholarship
- EMBO Long-Term and Advanced Fellowships
- Wellcome [WT206194]
- Wellcome Human Cell Atlas Strategic Science Support [WT211276/Z/18/Z]
- Chan Zuckerberg Initiative [CZF2019-002445]
- Barts Charity Lectureship [MGU045]
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MultiMAP is a novel algorithm for dimensionality reduction and integration of multimodal data, which is particularly suitable for single-cell biology. It outperforms current approaches in analyzing single-cell transcriptomics, chromatin accessibility, methylation, spatial data, etc. The application of MultiMAP enables quantitative comparison of transcription factor expression and binding site accessibility over the course of T cell differentiation.
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.
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