SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis.

ABSTRACT:

Motivation

Spatial omics data demand computational analysis but many analysis tools have computational resource requirements that increase with the number of cells analyzed. This presents scalability challenges as researchers use spatial omics technologies to profile millions of cells.

Results

To enhance the scalability of spatial omics data analysis, we developed a rasterization preprocessing framework called SEraster that aggregates cellular information into spatial pixels. We apply SEraster to both real and simulated spatial omics data prior to spatial variable gene expression analysis to demonstrate that such preprocessing can reduce computational resource requirements while maintaining high performance, including as compared to other down-sampling approaches. We further

SUBMITTER: Aihara G 

PROVIDER: S-EPMC11226864 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature