Spatial Experiments raster (SEraster)

SEraster is a rasterization preprocessing framework that aggregates cellular information into spatial pixels to reduce resource requirements for spatial omics data analysis. This is the SEraster R documentation website. Questions, suggestions, or problems should be submitted as GitHub issues.

Overview

SEraster reduces the number of spatial points in spatial omics datasets for downstream analysis through a process of rasterization where single cells' gene expression or cell-type labels are aggregated into equally sized pixels based on a user-defined resolution. Here, we refer to a particular resolution of rasterization by the side length of the pixel such that finer resolution indicates smaller pixel size and coarser resolution indicates larger pixel size.

Installation

To install SEraster, we currently recommend using remotes:

require(remotes)
remotes::install_github('JEFworks-Lab/SEraster')

Tutorials

Introduction:

• Formatting a SpatialExperiment Object for SEraster
• Getting Started With SEraster
• SEraster for Spatial Variable Genes Analysis
• Characterizing mPOA cell-type heterogeneity with spatial bootstrapping

Citation

Our manuscript describing SEraster is available on Bioinformatics:

Gohta Aihara, Kalen Clifton, Mayling Chen, Zhuoyan Li, Lyla Atta, Brendan F Miller, Rahul Satija, John W Hickey, Jean Fan, SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis, Bioinformatics, Volume 40, Issue 7, July 2024, btae412, https://doi.org/10.1093/bioinformatics/btae412