<?xml version="1.0" encoding="utf-8" ?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:r="https://r-universe.dev"><channel><title>gohtaaihara.r-universe.dev</title><link>https://gohtaaihara.r-universe.dev</link><description>Recent package updates in gohtaaihara</description><generator>R-universe</generator><image><url>https://github.com/gohtaaihara.png</url><title>R packages by gohtaaihara</title><link>https://gohtaaihara.r-universe.dev</link></image><lastBuildDate>Tue, 28 Apr 2026 13:04:54 GMT</lastBuildDate><item><title>[bioc] SEraster 1.5.0</title><author>gohta.aihara@gmail.com (Gohta Aihara)</author><description>SEraster is a rasterization preprocessing framework that
aggregates cellular information into spatial pixels to reduce
resource requirements for spatial omics data analysis. 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. SEraster is built on an R/Bioconductor S4 class
called SpatialExperiment. SEraster can be incorporated with
other packages to conduct downstream analyses for spatial omics
datasets, such as detecting spatially variable genes.</description><link>https://github.com/r-universe/bioc/actions/runs/28709727696</link><pubDate>Tue, 28 Apr 2026 13:04:54 GMT</pubDate><r:package>SEraster</r:package><r:version>1.5.0</r:version><r:status>success</r:status><r:repository>https://bioc.r-universe.dev</r:repository><r:upstream>https://github.com/bioc/SEraster</r:upstream><r:article><r:source>getting-started-with-SEraster.Rmd</r:source><r:filename>getting-started-with-SEraster.html</r:filename><r:title>Getting Started With SEraster</r:title><r:created>2023-12-14 19:24:18</r:created><r:modified>2025-01-15 20:16:06</r:modified></r:article></item></channel></rss>