Scope+: an open source generalizable architecture for single-cell RNA-seq atlases at sample and cell levels.

Area of Science:

• Single-cell genomics
• Bioinformatics
• Computational biology

Background:

• Single-cell RNA sequencing (scRNA-seq) has generated large cell atlases.
• Existing portals lack open-source adaptability and cell-level meta-analysis capabilities.
• Efficient access to large-scale atlas data remains a challenge.

Purpose of the Study:

• To present Scope+, an open-source, scalable architecture for accessing and analyzing cell atlas data.
• To enable quick access, cell-level selection, and meta-analysis of atlas data.
• To provide a foundation for researchers to build their own adaptable cell atlas portals.

Main Methods:

• Developed Scope+ architecture with a focus on optimization and scalability.
• Implemented cell-as-unit data modeling for efficient data handling.
• Utilized novel database optimization techniques and innovative software architectural design.
• Applied Scope+ to create the Covidscope portal for 5 million COVID-19 blood and immune cells.

Main Results:

• Scope+ provides efficient, fast access to large-scale cell atlas data.
• The Covidscope portal demonstrates the architecture's application to real-world data.
• The architecture supports cell-level selection and meta-analysis.
• Scope+ is open-source and adaptable for diverse atlas datasets.

Conclusions:

• Scope+ offers a robust solution for managing and analyzing large single-cell atlases.
• The architecture facilitates collaborative research through its open-source nature.
• It addresses key limitations of existing cell atlas portals, enhancing data accessibility and utility.