Related Experiment Video
Updated: Jan 10, 2026
06:02
A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells
Published on: October 28, 2025
414
stPipe: a flexible and streamlined R/Bioconductor pipeline for preprocessing sequencing-based spatial transcriptomics
Yang Xu1,2, Callum J Sargeant1, Yue You3,4
1The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia.
NAR Genomics and Bioinformatics
|November 24, 2025
Summary
stPipe is a new R/Bioconductor package that offers a unified solution for preprocessing spatial transcriptomics (sST) data from various sequencing platforms. This tool simplifies data analysis and enables robust methods benchmarking for improved spatial biology research.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Sequencing-based spatial transcriptomics (sST) technologies like 10× Visium, Slide-seq, and Stereo-seq are rapidly advancing.
- Diverse platforms present challenges in data preprocessing and standardization for downstream analysis.
Purpose of the Study:
- To introduce stPipe, a comprehensive, modular, and open-source preprocessing pipeline for sST data.
- To provide a unified solution for handling data from various mainstream sST platforms.
Main Methods:
- stPipe is implemented as an R/Bioconductor package.
- It processes raw FASTQ files into spatially resolved gene count matrices.
- It includes quality control metrics and standardized data storage for downstream compatibility.
Main Results:
- stPipe facilitates uniform preprocessing of sST data from different platforms.
- The pipeline simplifies methods benchmarking using reference datasets like cadasSTre and SpatialBenchVisium.
- Enables easier comparison of different sST technologies and analysis tools.
Conclusions:
- stPipe addresses the need for simplified, standardized preprocessing of sST data.
- It enhances the comparability of results across diverse spatial transcriptomics platforms.
- Facilitates robust benchmarking and downstream analysis in spatial biology research.
