ArticleBioinformatics (Oxford, England)2024
SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- STcompare: comparative spatial transcriptomics data analysis of structurally matched tissues to characterize differentially spatially patterned genes.Bioinformatics (Oxford, England) · 2026Article
- Spatial Dependence and Heterogeneity in Molecular Imaging: Moran Quadrant Maps Enable Advanced Spatial-Statistical Analysis.bioRxiv : the preprint server for biology · 2025Article
- Mapping disease critical spatially variable gene programs by integrating spatial transcriptomics with human genetics.bioRxiv : the preprint server for biology · 2025Article
- Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images.bioRxiv : the preprint server for biology · 2025Article
- Prioritizing perturbation-responsive gene patterns using interpretable deep learning.Nature communications · 2025Article
- SpotSweeper: spatially aware quality control for spatial transcriptomics.Nature methods · 2025Article
- Gene count normalization in single-cell imaging-based spatially resolved transcriptomics.Genome biology · 2024Article
- SpotSweeper: spatially-aware quality control for spatial transcriptomics.bioRxiv : the preprint server for biology · 2024Article
- Gene count normalization in single-cell imaging-based spatially resolved transcriptomics.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
9 authors.
Funding
Abstract
motivationSpatial 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.
resultsTo 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. AVAILABILITY AND IMPLEMENTATION: SEraster is implemented as an R package on GitHub (https://github.com/JEFworks-Lab/SEraster) with additional tutorials at https://JEF.works/SEraster.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.