Clustering of single-cell multi-omics data with a multimodal deep learning method
Single-cell multimodal sequencing technologies are developed to simultaneously profile different modalities of data in the same cell. Here the authors develops a multimodal deep clustering method for the analysis of single-cell multi-omics data that supports clustering different types of multi-omics...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-12-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-022-35031-9 |
| _version_ | 1797980492530712576 |
|---|---|
| author | Xiang Lin Tian Tian Zhi Wei Hakon Hakonarson |
| author_facet | Xiang Lin Tian Tian Zhi Wei Hakon Hakonarson |
| author_sort | Xiang Lin |
| collection | DOAJ |
| description | Single-cell multimodal sequencing technologies are developed to simultaneously profile different modalities of data in the same cell. Here the authors develops a multimodal deep clustering method for the analysis of single-cell multi-omics data that supports clustering different types of multi-omics data and multi-batch data, as well as downstream differential expression analysis. |
| first_indexed | 2024-04-11T05:55:13Z |
| format | Article |
| id | doaj.art-3f319554a2dd45bfa3ccebf65ad3f260 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-11T05:55:13Z |
| publishDate | 2022-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-3f319554a2dd45bfa3ccebf65ad3f2602022-12-22T04:41:55ZengNature PortfolioNature Communications2041-17232022-12-0113111810.1038/s41467-022-35031-9Clustering of single-cell multi-omics data with a multimodal deep learning methodXiang Lin0Tian Tian1Zhi Wei2Hakon Hakonarson3Department of Computer Science, New Jersey Institute of TechnologyCenter of Applied Genomics, Children’s Hospital of PhiladelphiaDepartment of Computer Science, New Jersey Institute of TechnologyCenter of Applied Genomics, Children’s Hospital of PhiladelphiaSingle-cell multimodal sequencing technologies are developed to simultaneously profile different modalities of data in the same cell. Here the authors develops a multimodal deep clustering method for the analysis of single-cell multi-omics data that supports clustering different types of multi-omics data and multi-batch data, as well as downstream differential expression analysis.https://doi.org/10.1038/s41467-022-35031-9 |
| spellingShingle | Xiang Lin Tian Tian Zhi Wei Hakon Hakonarson Clustering of single-cell multi-omics data with a multimodal deep learning method Nature Communications |
| title | Clustering of single-cell multi-omics data with a multimodal deep learning method |
| title_full | Clustering of single-cell multi-omics data with a multimodal deep learning method |
| title_fullStr | Clustering of single-cell multi-omics data with a multimodal deep learning method |
| title_full_unstemmed | Clustering of single-cell multi-omics data with a multimodal deep learning method |
| title_short | Clustering of single-cell multi-omics data with a multimodal deep learning method |
| title_sort | clustering of single cell multi omics data with a multimodal deep learning method |
| url | https://doi.org/10.1038/s41467-022-35031-9 |
| work_keys_str_mv | AT xianglin clusteringofsinglecellmultiomicsdatawithamultimodaldeeplearningmethod AT tiantian clusteringofsinglecellmultiomicsdatawithamultimodaldeeplearningmethod AT zhiwei clusteringofsinglecellmultiomicsdatawithamultimodaldeeplearningmethod AT hakonhakonarson clusteringofsinglecellmultiomicsdatawithamultimodaldeeplearningmethod |