https://github.com/jefworks-lab/seraster
Spatial Experiments raster - a rasterization preprocessing framework for scalable spatial omics data analysis
https://github.com/jefworks-lab/seraster
rstats spatial-analysis spatial-data-analysis spatial-omics spatial-transcriptomics
Last synced: 10 days ago
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Spatial Experiments raster - a rasterization preprocessing framework for scalable spatial omics data analysis
- Host: GitHub
- URL: https://github.com/jefworks-lab/seraster
- Owner: JEFworks-Lab
- Created: 2023-08-23T13:45:37.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-04-23T01:53:26.000Z (10 months ago)
- Last Synced: 2026-01-26T22:22:57.117Z (about 1 month ago)
- Topics: rstats, spatial-analysis, spatial-data-analysis, spatial-omics, spatial-transcriptomics
- Language: R
- Homepage: https://jef.works/SEraster/
- Size: 43.8 MB
- Stars: 19
- Watchers: 1
- Forks: 5
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
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README
# 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](https://github.com/JEFworks-Lab/SEraster/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` using Bioconductor, start R (version "4.5.0") and run:
```r
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("SEraster")
```
[See Bioconductor for more details](https://bioconductor.org/packages/release/bioc/html/SEraster.html).
The latest development version can also be installed from [GitHub](https://github.com/JEFworks-Lab/SEraster) using `remotes`:
```r
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`:
```r
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](https://github.com/satijalab/seurat-wrappers/tree/SEraster).
## Tutorials
Introduction:
- [Formatting a SpatialExperiment Object for SEraster](https://jef.works/SEraster/articles/formatting-SpatialExperiment-for-SEraster.html)
- [Getting Started With SEraster](https://jef.works/SEraster/articles/getting-started-with-SEraster.html)
- [SEraster for Spatial Variable Genes Analysis](https://jef.works/SEraster/articles/SEraster-for-SVG-analysis.html)
- [Characterizing mPOA cell-type heterogeneity with spatial bootstrapping](https://jef.works/SEraster/articles/characterizing-mPOA-cell-type-heterogeneity.html)
## 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](https://academic.oup.com/bioinformatics/article/40/7/btae412/7696710)