Unsupervised topological alignment for single-cell multi-omics integration
Abstract Motivation Single-cell multi-omics data provide a comprehensive molecular view of cells. However, single-cell multi-omics datasets consist of unpaired cells measured with distinct unmatched features across modalities, making data integration challenging. Results In this study, we present a...
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| Published in: | Bioinformatics (Oxford, England) Vol. 36; no. Supplement_1; pp. i48 - i56 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
England
Oxford University Press
01.07.2020
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| Subjects: | |
| ISSN: | 1367-4803, 1367-4811, 1367-4811 |
| Online Access: | Get full text |
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| Summary: | Abstract
Motivation
Single-cell multi-omics data provide a comprehensive molecular view of cells. However, single-cell multi-omics datasets consist of unpaired cells measured with distinct unmatched features across modalities, making data integration challenging.
Results
In this study, we present a novel algorithm, termed UnionCom, for the unsupervised topological alignment of single-cell multi-omics integration. UnionCom does not require any correspondence information, either among cells or among features. It first embeds the intrinsic low-dimensional structure of each single-cell dataset into a distance matrix of cells within the same dataset and then aligns the cells across single-cell multi-omics datasets by matching the distance matrices via a matrix optimization method. Finally, it projects the distinct unmatched features across single-cell datasets into a common embedding space for feature comparability of the aligned cells. To match the complex non-linear geometrical distorted low-dimensional structures across datasets, UnionCom proposes and adjusts a global scaling parameter on distance matrices for aligning similar topological structures. It does not require one-to-one correspondence among cells across datasets, and it can accommodate samples with dataset-specific cell types. UnionCom outperforms state-of-the-art methods on both simulated and real single-cell multi-omics datasets. UnionCom is robust to parameter choices, as well as subsampling of features.
Availability and implementation
UnionCom software is available at https://github.com/caokai1073/UnionCom.
Supplementary information
Supplementary data are available at Bioinformatics online. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 1367-4803 1367-4811 1367-4811 |
| DOI: | 10.1093/bioinformatics/btaa443 |