Cooperative integration of spatially resolved multi-omics data with COSMOS
Abstract Recent advancements in biological technologies have enabled the measurement of spatially resolved multi-omics data, yet computational algorithms for this purpose are scarce. Existing tools target either single omics or lack spatial integration. We generate a graph neural network algorithm n...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55204-y |
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| _version_ | 1841559220111015936 |
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| author | Yuansheng Zhou Xue Xiao Lei Dong Chen Tang Guanghua Xiao Lin Xu |
| author_facet | Yuansheng Zhou Xue Xiao Lei Dong Chen Tang Guanghua Xiao Lin Xu |
| author_sort | Yuansheng Zhou |
| collection | DOAJ |
| description | Abstract Recent advancements in biological technologies have enabled the measurement of spatially resolved multi-omics data, yet computational algorithms for this purpose are scarce. Existing tools target either single omics or lack spatial integration. We generate a graph neural network algorithm named COSMOS to address this gap and demonstrated the superior performance of COSMOS in domain segmentation, visualization, and spatiotemporal map for spatially resolved multi-omics data integration tasks. |
| format | Article |
| id | doaj-art-734bae4b67c04cbf8d9b10fab0d9d71b |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-734bae4b67c04cbf8d9b10fab0d9d71b2025-01-05T12:40:19ZengNature PortfolioNature Communications2041-17232025-01-0116111010.1038/s41467-024-55204-yCooperative integration of spatially resolved multi-omics data with COSMOSYuansheng Zhou0Xue Xiao1Lei Dong2Chen Tang3Guanghua Xiao4Lin Xu5Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical CenterQuantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical CenterQuantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical CenterQuantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical CenterQuantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical CenterQuantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical CenterAbstract Recent advancements in biological technologies have enabled the measurement of spatially resolved multi-omics data, yet computational algorithms for this purpose are scarce. Existing tools target either single omics or lack spatial integration. We generate a graph neural network algorithm named COSMOS to address this gap and demonstrated the superior performance of COSMOS in domain segmentation, visualization, and spatiotemporal map for spatially resolved multi-omics data integration tasks.https://doi.org/10.1038/s41467-024-55204-y |
| spellingShingle | Yuansheng Zhou Xue Xiao Lei Dong Chen Tang Guanghua Xiao Lin Xu Cooperative integration of spatially resolved multi-omics data with COSMOS Nature Communications |
| title | Cooperative integration of spatially resolved multi-omics data with COSMOS |
| title_full | Cooperative integration of spatially resolved multi-omics data with COSMOS |
| title_fullStr | Cooperative integration of spatially resolved multi-omics data with COSMOS |
| title_full_unstemmed | Cooperative integration of spatially resolved multi-omics data with COSMOS |
| title_short | Cooperative integration of spatially resolved multi-omics data with COSMOS |
| title_sort | cooperative integration of spatially resolved multi omics data with cosmos |
| url | https://doi.org/10.1038/s41467-024-55204-y |
| work_keys_str_mv | AT yuanshengzhou cooperativeintegrationofspatiallyresolvedmultiomicsdatawithcosmos AT xuexiao cooperativeintegrationofspatiallyresolvedmultiomicsdatawithcosmos AT leidong cooperativeintegrationofspatiallyresolvedmultiomicsdatawithcosmos AT chentang cooperativeintegrationofspatiallyresolvedmultiomicsdatawithcosmos AT guanghuaxiao cooperativeintegrationofspatiallyresolvedmultiomicsdatawithcosmos AT linxu cooperativeintegrationofspatiallyresolvedmultiomicsdatawithcosmos |