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.

<p> <img src="https://github.com/JEFworks/SEraster/blob/main/images/seraster_logo_hex.png?raw=true" align="center" height="300" style="float: center; height:300px;"/> </p>

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.

<p align="center"> <img src="https://github.com/JEFworks-Lab/SEraster/blob/main/images/overview.png?raw=true" height="600"/> </p>

Installation

To install SEraster using Bioconductor, start R (version "4.5.0") and run:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("SEraster")

See Bioconductor for more details.

The latest development version can also be installed from GitHub using remotes:

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

In addition, SEraster is also compatible with SeuratObject through SeuratWrappers. SeuratWrappers implementation can be installed using remotes:

require(remotes)
remotes::install_github('satijalab/seurat-wrappers@SEraster')

Documentation and tutorial for the SeuratWrappers implementation can be found in the SEraster branch of the SeuratWrappers GitHub repository.

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