CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• High-throughput transcriptomic methods have generated vast single-cell datasets.
• Integrating these datasets offers potential for novel health and disease insights.
• Challenges exist in managing data volume, models, and accessibility.

Purpose of the Study:

• To present CZ CELLxGENE Discover, a data platform for curated and interoperable single-cell data.
• To provide a centralized, accessible resource for analyzing large-scale single-cell transcriptomic data.
• To facilitate meta-analyses and cross-corpus studies across millions of cells.

Main Methods:

• Development of a free-to-use online data portal (cellxgene.cziscience.com).
• Aggregation and curation of community-contributed single-cell transcriptomic data.
• Implementation of tools for computational and visual data exploration and analysis.

Main Results:

• The platform hosts over 93 million unique cells, representing the largest collection of its kind.
• Data is curated, standardized, and linked with consistent cell-level metadata.
• Features enable exploration of individual datasets and cross-corpus meta-analyses.

Conclusions:

• CZ CELLxGENE Discover addresses data accessibility challenges in single-cell research.
• The platform supports advanced analyses, including meta-analyses of millions of cells.
• It empowers researchers to extract deeper biological insights from large-scale transcriptomic data.