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MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Argelaguet, Ricard 
Arnol, Damien 
Bredikhin, Danila 
Deloro, Yonatan 
Velten, Britta 

Abstract

Technological advances have enabled the profiling of multiple molecular layers at single-cell resolution, assaying cells from multiple samples or conditions. Consequently, there is a growing need for computational strategies to analyze data from complex experimental designs that include multiple data modalities and multiple groups of samples. We present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of single-cell multi-modal data. MOFA+ reconstructs a low-dimensional representation of the data using computationally efficient variational inference and supports flexible sparsity constraints, allowing to jointly model variation across multiple sample groups and data modalities.

Description

Keywords

Data integration, Factor analysis, Multi-omics, Single cell, Animals, DNA Methylation, Embryonic Development, Factor Analysis, Statistical, Frontal Lobe, Mice, Sequence Analysis, RNA, Single-Cell Analysis

Journal Title

Genome Biol

Conference Name

Journal ISSN

1474-7596
1474-760X

Volume Title

21

Publisher

Springer Science and Business Media LLC
Sponsorship
Cancer Research UK (22231)