AnnSQL: A Python SQL-based package for fast large-scale single-cell genomics analysis using minimal computational...

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Single-cell genomics technologies are rapidly advancing, generating massive datasets.
• Analyzing these large-scale datasets requires efficient and accessible software tools.
• Existing tools can be computationally intensive, limiting accessibility for many researchers.

Purpose of the Study:

• Introduce AnnSQL, a Python package for analyzing single-cell genomics data.
• Demonstrate performance enhancements and reduced computational resource requirements.
• Lower computational barriers for large-scale single-cell analysis on personal computers.

Main Methods:

• Developed AnnSQL, a Python package utilizing the in-process DuckDB engine.
• Constructed an AnnData-inspired database within AnnSQL.
• Compared AnnSQL and AnnData performance on a 4.4 million cell single-nucleus RNA-seq dataset.

Main Results:

• AnnSQL provides orders-of-magnitude performance improvements for parsing single-cell genomics datasets.
• AnnSQL operations on a large dataset completed in minutes on a laptop.
• Equivalent AnnData operations failed or were significantly slower on a high-performance computing cluster.
• AnnSQL demonstrated transformative runtime improvements.

Conclusions:

• AnnSQL offers a computationally efficient and accessible solution for large-scale single-cell genomics analysis.
• The package enables complex analyses on personal computers, democratizing access to single-cell data.
• AnnSQL presents a promising computational infrastructure for future single-cell workflows across various genome-wide measurements.