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.
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.
To install SEraster using Bioconductor, start R (version "4.4.0") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("SEraster")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.
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
Our manuscript describing SEraster is available on Bioinformatics: