scBSP

scBSP identifies spatially variable features in high-resolution spatial omics data using sparse matrix operations for computational efficiency.

Key Features:

• Sparse Matrix Operations: scBSP leverages sparse matrix operations to significantly increase computational efficiency in both computational time and memory usage.
• High-Resolution Data Processing: scBSP handles two- and three-dimensional data with up to millions of cells, including processing of 19,950 genes across 181,367 spots within 10 seconds on a typical desktop computer.
• Multi-Omics Compatibility: scBSP identifies spatially variable genes and spatially variable peaks across various sequencing techniques and spatial resolutions.

Scientific Applications:

• Spatial Transcriptomics Analysis: scBSP processes high-definition spatial transcriptomics data for rapid identification of spatially variable genes.
• Spatial Epigenomics Analysis: scBSP identifies spatially variable peaks from spatial omics data.
• Disease Pathology Investigation: scBSP identified spatially variable genes representative of critical pathological mechanisms associated with histology in a case study of kidney disease using 10x Xenium data.

Methodology:

scBSP uses sparse matrix operations for efficient processing of large spatial omics datasets.