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

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. To enhance the scalabili...

Full description

Saved in:

Bibliographic Details
Published in:	Bioinformatics (Oxford, England) Vol. 40; no. 7
Main Authors:	Aihara, Gohta, Clifton, Kalen, Chen, Mayling, Li, Zhuoyan, Atta, Lyla, Miller, Brendan F, Satija, Rahul, Hickey, John W, Fan, Jean
Format:	Journal Article
Language:	English
Published:	England Oxford University Press 01-07-2024
Subjects:	Original Paper
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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. 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 integrate SEraster with existing analysis tools to characterize cell-type spatial co-enrichment across length scales. Finally, we apply SEraster to enable analysis of a mouse pup spatial omics dataset with over a million cells to identify tissue-level and cell-type-specific spatially variable genes as well as spatially co-enriched cell-types that recapitulate expected organ structures. SEraster is implemented as an R package on GitHub (https://github.com/JEFworks-Lab/SEraster) with additional tutorials at https://JEF.works/SEraster. Supplementary data are available at Bioinformatics online.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1367-4803 1367-4811 1367-4811
DOI:	10.1093/bioinformatics/btae412