Rediscovering Publicly Available Single-cell Data with the DISCO Platform

• Main Author: CHEN, Jinmiao
• Format: text
• Published: Institutional Knowledge at Singapore Management University 2024
• Online Access: https://ink.library.smu.edu.sg/sgor2024/programme/schedule/13; https://ink.library.smu.edu.sg/context/sgor2024/article/1013/viewcontent/10_JinmiaoChen_V2.0.pdf
• Institution: Singapore Management University

Since its inception in 2009, single-cell RNA-seq techniques have evolved, increasing throughput and reducing costs. With a growing number of published studies, efficient data retrieval is crucial. DISCO was created as a comprehensive database of single-cell RNA-seq data, enabling exploration of cell types and gene expressions in various tissues. Now, DISCO hosts over 100 million single-cell profiles from 16,734 samples, reflecting a fivefold increase since its first version. We have curated metadata, categorized samples, and refined cell type annotations using a harmonized reference. DISCO platform provides online tools for data integration, cell type annotation, projecting query dataset to atlases, and gene set enrichment analysis. The DISCO R toolkit supports offline analyses. Our data also aids in training foundation AI models like scGPT and scFoundation, enhancing hypothesis generation and data mining. DISCO’s continued updates and extensive dataset underscore its role as a key resource in the field.