Bi-order multimodal integration of single-cell data

Integration of single-cell multiomics profiles generated by different single-cell technologies from the same biological sample is still challenging. Previous approaches based on shared features have only provided approximate solutions. Here, we present a novel mathematical solution named bi-order ca...

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Detalles Bibliográficos
Autores principales: Dou, Jinzhuang, Liang, Shaoheng, Mohanty, Vakul, Miao, Qi, Huang, Yuefan, Liang, Qingnan, Cheng, Xuesen, Kim, Sangbae, Choi, Jongsu, Li, Yumei, Li, Li, Daher, May, Basar, Rafet, Rezvani, Katayoun, Chen, Rui, Chen, Ken
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9082907/
https://www.ncbi.nlm.nih.gov/pubmed/35534898
http://dx.doi.org/10.1186/s13059-022-02679-x
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author Dou, Jinzhuang
Liang, Shaoheng
Mohanty, Vakul
Miao, Qi
Huang, Yuefan
Liang, Qingnan
Cheng, Xuesen
Kim, Sangbae
Choi, Jongsu
Li, Yumei
Li, Li
Daher, May
Basar, Rafet
Rezvani, Katayoun
Chen, Rui
Chen, Ken
author_facet Dou, Jinzhuang
Liang, Shaoheng
Mohanty, Vakul
Miao, Qi
Huang, Yuefan
Liang, Qingnan
Cheng, Xuesen
Kim, Sangbae
Choi, Jongsu
Li, Yumei
Li, Li
Daher, May
Basar, Rafet
Rezvani, Katayoun
Chen, Rui
Chen, Ken
author_sort Dou, Jinzhuang
collection PubMed
description Integration of single-cell multiomics profiles generated by different single-cell technologies from the same biological sample is still challenging. Previous approaches based on shared features have only provided approximate solutions. Here, we present a novel mathematical solution named bi-order canonical correlation analysis (bi-CCA), which extends the widely used CCA approach to iteratively align the rows and the columns between data matrices. Bi-CCA is generally applicable to combinations of any two single-cell modalities. Validations using co-assayed ground truth data and application to a CAR-NK study and a fetal muscle atlas demonstrate its capability in generating accurate multimodal co-embeddings and discovering cellular identity. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02679-x.
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spelling pubmed-90829072022-05-10 Bi-order multimodal integration of single-cell data Dou, Jinzhuang Liang, Shaoheng Mohanty, Vakul Miao, Qi Huang, Yuefan Liang, Qingnan Cheng, Xuesen Kim, Sangbae Choi, Jongsu Li, Yumei Li, Li Daher, May Basar, Rafet Rezvani, Katayoun Chen, Rui Chen, Ken Genome Biol Method Integration of single-cell multiomics profiles generated by different single-cell technologies from the same biological sample is still challenging. Previous approaches based on shared features have only provided approximate solutions. Here, we present a novel mathematical solution named bi-order canonical correlation analysis (bi-CCA), which extends the widely used CCA approach to iteratively align the rows and the columns between data matrices. Bi-CCA is generally applicable to combinations of any two single-cell modalities. Validations using co-assayed ground truth data and application to a CAR-NK study and a fetal muscle atlas demonstrate its capability in generating accurate multimodal co-embeddings and discovering cellular identity. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02679-x. BioMed Central 2022-05-09 /pmc/articles/PMC9082907/ /pubmed/35534898 http://dx.doi.org/10.1186/s13059-022-02679-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Dou, Jinzhuang
Liang, Shaoheng
Mohanty, Vakul
Miao, Qi
Huang, Yuefan
Liang, Qingnan
Cheng, Xuesen
Kim, Sangbae
Choi, Jongsu
Li, Yumei
Li, Li
Daher, May
Basar, Rafet
Rezvani, Katayoun
Chen, Rui
Chen, Ken
Bi-order multimodal integration of single-cell data
title Bi-order multimodal integration of single-cell data
title_full Bi-order multimodal integration of single-cell data
title_fullStr Bi-order multimodal integration of single-cell data
title_full_unstemmed Bi-order multimodal integration of single-cell data
title_short Bi-order multimodal integration of single-cell data
title_sort bi-order multimodal integration of single-cell data
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9082907/
https://www.ncbi.nlm.nih.gov/pubmed/35534898
http://dx.doi.org/10.1186/s13059-022-02679-x
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