beachmat

beachmat provides agnostic C++ access to multiple matrix representations to enable memory-efficient manipulation and analysis of high-throughput genomic data such as single-cell RNA sequencing (scRNA-seq).

Key Features:

• Agnostic data access: Provides a unified C++ interface to dense, sparse, and file-backed matrix representations.
• Interoperability with R/Bioconductor: Integrates with R and Bioconductor to allow C++ code to operate on R-side matrix objects.
• Performance evaluation: Has been evaluated using both simulated and real scRNA-seq datasets to assess performance across multiple matrix representations.
• Facilitation of large-scale analysis: Enables development of optimized C++ code for handling very large scRNA-seq datasets by providing access to different matrix types.

Scientific Applications:

• Single-cell genomics: Supports analysis of scRNA-seq data, including datasets from mouse cortex tissues.
• High-throughput data management: Enables handling and processing of large gene-by-cell expression matrices through memory-conscious access to different matrix layouts.
• Computational performance optimization: Facilitates selection of matrix representations to balance memory usage and processing speed in genomic analyses.

Methodology:

Provides agnostic access to dense, sparse, and file-backed matrices, integrates with R/Bioconductor, and was evaluated using simulated and real scRNA-seq datasets across different matrix representations.