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

Author:

Li Mengwei1,Zhang Xiaomeng1,Ang Kok Siong1,Ling Jingjing1,Sethi Raman1,Lee Nicole Yee Shin1,Ginhoux Florent123,Chen Jinmiao14ORCID

Affiliation:

1. Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, Immunos Building, Level 3, Singapore 138648, Singapore

2. Shanghai Institute of Immunology, Shanghai JiaoTong University School of Medicine, Shanghai 200025, China

3. Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore 169856, Singapore

4. Immunology Translational Research Program, Yong Loo Lin School of Medicine, Department of Microbiology and Immunology, National University of Singapore (NUS), 5 Science Drive 2, Blk MD4, Level 3, Singapore 117545, Singapore

Abstract

Abstract 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.

Funder

Open Fund Individual Research

Publisher

Oxford University Press (OUP)

Subject

Genetics

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