MultiMAP: dimensionality reduction and integration of multimodal data

Genome Biol. 2021 Dec 20;22(1):346. doi: 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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Chromatin
  • Chromosome Mapping / methods*
  • Chromosomes, Human
  • Gene Expression Regulation
  • Genetic Markers
  • Genomics
  • Humans
  • Single-Cell Analysis / methods*
  • Software
  • Transcription Factors
  • Transcriptome*


  • Chromatin
  • Genetic Markers
  • Transcription Factors