SpatialROI: An Interactive R Shiny Platform for Customized Region-Aware Spatial Transcriptomics Analysis
Year: 2026
Type: article
Abstract: Motivation Spatial transcriptomics captures high-throughput molecular information across tissue sections, but histological context remains essential for accurate biological insight. Clustering-derived spatial domains are sensitive to technical noise and may not align with morphologically defined structures that pathologists rely on for disease interpretation. Expert-guided region definition directly on H&E images enables precise identification of molecular signals within tissue architecture. However, existing tools require substantial programming expertise or lack seamless integration between image-based region selection and downstream transcriptomic analysis, limiting accessibility for non-computational users. Results We present SpatialROI, an interactive platform that enables users to define regions of interest (ROIs) directly on H&E images and perform targeted analyses without programming. SpatialROI supports standard 10x Visium data as both raw Space Ranger outputs and preprocessed Seurat objects. The platform incorporates eight analytical modules: ROI drawing, gene set and pathway visualization, ligand–receptor colocalization, cell-type deconvolution, spot clustering, differential expression, within-sample group comparison, and multi-sample ROI comparison. ROIs are exportable as Seurat objects or reusable spot-index files. Collectively, SpatialROI facilitates expert-guided, region-aware exploration of spatial transcriptomic data for biologically meaningful insights into tissue architecture. Availability and implementation SpatialROI is freely accessible at https://shiny.crc.pitt.edu/spatial_api/. Source code and example datasets are available at https://github.com/myaol/SpatialROI.
Source: Bioinformatics Advances
Authors: Mengyao Lu, Aodong Qiu, Min Xu, Xinghua Lu, Lujia Chen
Institutions: University of Pittsburgh, Carnegie Mellon University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,726
OpenAlex ID: W7225466296