SEraster

SEraster aggregates cellular information from spatial omics data into spatial pixels to reduce computational resource requirements while maintaining analytical performance.

Key Features:

• Rasterization Preprocessing: Aggregates cellular information into spatial pixels to reduce resource requirements for downstream analysis.
• Integration with Existing Tools: Can be incorporated with other analysis tools for spatial omics datasets.
• Scalability Enhancement: Enables analysis of datasets with over a million cells by reducing computational demands.
• Spatial Variable Gene Expression Analysis: Maintains high performance for identifying spatially variable genes after rasterization preprocessing.
• Cell-Type Spatial Cooccurrence Analysis: Supports characterization of cooccurring cell-types when integrated with existing analysis tools.

Scientific Applications:

• Tissue-Level Spatially Variable Gene Identification: Identifies tissue-level spatially variable genes in large-scale spatial omics datasets.
• Cell-Type-Specific Spatially Variable Gene Identification: Identifies cell-type-specific spatially variable genes in datasets with over a million cells.
• Organ Structure Recapitulation: Identifies cooccurring cell-types that recapitulate expected organ structures in mouse pup spatial omics data.

Methodology:

SEraster implements rasterization preprocessing that aggregates cellular information into spatial pixels. The framework is applied to spatial omics data prior to spatial variable gene expression analysis and can be integrated with existing analysis tools for cell-type spatial cooccurrence characterization.