MultiMAP: dimensionality reduction and integration of multimodal data.
IF: 17.906
Cited by: 15


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.


Spatial Transcriptomics
Gene Expression


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
Efremova, Mirjana
Teichmann, Sarah A