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Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS.

Authors :
He, Zhen
Hu, Shuofeng
Chen, Yaowen
An, Sijing
Zhou, Jiahao
Liu, Runyan
Shi, Junfeng
Wang, Jing
Dong, Guohua
Shi, Jinhui
Zhao, Jiaxin
Ou-Yang, Le
Zhu, Yuan
Bo, Xiaochen
Ying, Xiaomin
Source :
Nature Biotechnology; Oct2024, Vol. 42 Issue 10, p1594-1605, 12p
Publication Year :
2024

Abstract

Integrating single-cell datasets produced by multiple omics technologies is essential for defining cellular heterogeneity. Mosaic integration, in which different datasets share only some of the measured modalities, poses major challenges, particularly regarding modality alignment and batch effect removal. Here, we present a deep probabilistic framework for the mosaic integration and knowledge transfer (MIDAS) of single-cell multimodal data. MIDAS simultaneously achieves dimensionality reduction, imputation and batch correction of mosaic data by using self-supervised modality alignment and information-theoretic latent disentanglement. We demonstrate its superiority to 19 other methods and reliability by evaluating its performance in trimodal and mosaic integration tasks. We also constructed a single-cell trimodal atlas of human peripheral blood mononuclear cells and tailored transfer learning and reciprocal reference mapping schemes to enable flexible and accurate knowledge transfer from the atlas to new data. Applications in mosaic integration, pseudotime analysis and cross-tissue knowledge transfer on bone marrow mosaic datasets demonstrate the versatility and superiority of MIDAS. MIDAS is available at https://github.com/labomics/midas. Single-cell, multiomic datasets are integrated using dimensionality reduction, imputation and batch correction. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10870156
Volume :
42
Issue :
10
Database :
Complementary Index
Journal :
Nature Biotechnology
Publication Type :
Academic Journal
Accession number :
180234719
Full Text :
https://doi.org/10.1038/s41587-023-02040-y