Journal Article

MultiMAP: dimensionality reduction and integration of multimodal data

Lamacraft, Austen, Conde, Cecilia Dominguez, Chen, Xi, Jain, Mika Sarkin, Park, Jongeun, Botting, Rachel A., Mamanova, Lira, Teichmann, Sarah A., Efremova, Mirjana, Polanski, Krzysztof, Knights, Andrew, Haniffa, Muzlifah, Stephenson, Emily

2021

Overview

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.

• Publisher: BioMed Central,Springer Nature B.V,BMC
• Subject: Algorithms / Animal Genetics and Genomics / Bioinformatics / Biomedical and Life Sciences / Cell differentiation / Chromatin / Chromosome Mapping - methods / Chromosomes, Human / data collection / Datasets / DNA methylation / Evolutionary Biology / Gene expression / Gene Expression Regulation / Genetic Markers / genome / Genomics / Human Genetics / Humans / Life Sciences / Lymphocytes T / Method / Methylation / Microbial Genetics and Genomics / Neighborhoods / Noise / Plant Genetics and Genomics / Sensory integration / Single-Cell Analysis - methods / Software / spatial data / T-lymphocytes / Transcription Factors / Transcriptome / Transcriptomics