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DISCO: a database of Deeply Integrated human Single-Cell Omics data

The ability to study cellular heterogeneity at single cell resolution is making single-cell sequencing increasingly popular. However, there is no publicly available resource that offers an integrated cell atlas with harmonized metadata that users can integrate new data with. Here, we present DISCO (...

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Autores principales: Li, Mengwei, Zhang, Xiaomeng, Ang, Kok Siong, Ling, Jingjing, Sethi, Raman, Lee, Nicole Yee Shin, Ginhoux, Florent, Chen, Jinmiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8728243/
https://www.ncbi.nlm.nih.gov/pubmed/34791375
http://dx.doi.org/10.1093/nar/gkab1020
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author Li, Mengwei
Zhang, Xiaomeng
Ang, Kok Siong
Ling, Jingjing
Sethi, Raman
Lee, Nicole Yee Shin
Ginhoux, Florent
Chen, Jinmiao
author_facet Li, Mengwei
Zhang, Xiaomeng
Ang, Kok Siong
Ling, Jingjing
Sethi, Raman
Lee, Nicole Yee Shin
Ginhoux, Florent
Chen, Jinmiao
author_sort Li, Mengwei
collection PubMed
description The ability to study cellular heterogeneity at single cell resolution is making single-cell sequencing increasingly popular. However, there is no publicly available resource that offers an integrated cell atlas with harmonized metadata that users can integrate new data with. Here, we present DISCO (https://www.immunesinglecell.org/), a database of Deeply Integrated Single-Cell Omics data. The current release of DISCO integrates more than 18 million cells from 4593 samples, covering 107 tissues/cell lines/organoids, 158 diseases, and 20 platforms. We standardized the associated metadata with a controlled vocabulary and ontology system. To allow large scale integration of single-cell data, we developed FastIntegration, a fast and high-capacity version of Seurat Integration. We also developed CELLiD, an atlas guided automatic cell type identification tool. Employing these two tools on the assembled data, we constructed one global atlas and 27 sub-atlases for different tissues, diseases, and cell types. DISCO provides three online tools, namely Online FastIntegration, Online CELLiD, and CellMapper, for users to integrate, annotate, and project uploaded single-cell RNA-seq data onto a selected atlas. Collectively, DISCO is a versatile platform for users to explore published single-cell data and efficiently perform integrated analysis with their own data.
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spelling pubmed-87282432022-01-05 DISCO: a database of Deeply Integrated human Single-Cell Omics data Li, Mengwei Zhang, Xiaomeng Ang, Kok Siong Ling, Jingjing Sethi, Raman Lee, Nicole Yee Shin Ginhoux, Florent Chen, Jinmiao Nucleic Acids Res Database Issue The ability to study cellular heterogeneity at single cell resolution is making single-cell sequencing increasingly popular. However, there is no publicly available resource that offers an integrated cell atlas with harmonized metadata that users can integrate new data with. Here, we present DISCO (https://www.immunesinglecell.org/), a database of Deeply Integrated Single-Cell Omics data. The current release of DISCO integrates more than 18 million cells from 4593 samples, covering 107 tissues/cell lines/organoids, 158 diseases, and 20 platforms. We standardized the associated metadata with a controlled vocabulary and ontology system. To allow large scale integration of single-cell data, we developed FastIntegration, a fast and high-capacity version of Seurat Integration. We also developed CELLiD, an atlas guided automatic cell type identification tool. Employing these two tools on the assembled data, we constructed one global atlas and 27 sub-atlases for different tissues, diseases, and cell types. DISCO provides three online tools, namely Online FastIntegration, Online CELLiD, and CellMapper, for users to integrate, annotate, and project uploaded single-cell RNA-seq data onto a selected atlas. Collectively, DISCO is a versatile platform for users to explore published single-cell data and efficiently perform integrated analysis with their own data. Oxford University Press 2021-11-17 /pmc/articles/PMC8728243/ /pubmed/34791375 http://dx.doi.org/10.1093/nar/gkab1020 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Database Issue
Li, Mengwei
Zhang, Xiaomeng
Ang, Kok Siong
Ling, Jingjing
Sethi, Raman
Lee, Nicole Yee Shin
Ginhoux, Florent
Chen, Jinmiao
DISCO: a database of Deeply Integrated human Single-Cell Omics data
title DISCO: a database of Deeply Integrated human Single-Cell Omics data
title_full DISCO: a database of Deeply Integrated human Single-Cell Omics data
title_fullStr DISCO: a database of Deeply Integrated human Single-Cell Omics data
title_full_unstemmed DISCO: a database of Deeply Integrated human Single-Cell Omics data
title_short DISCO: a database of Deeply Integrated human Single-Cell Omics data
title_sort disco: a database of deeply integrated human single-cell omics data
topic Database Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8728243/
https://www.ncbi.nlm.nih.gov/pubmed/34791375
http://dx.doi.org/10.1093/nar/gkab1020
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