PathwayFinder enables linked exploration of cell types and gene modules in spatially resolved transcriptomics data
Abstract
The rapid development of spatially resolved transcriptomics technologies has provided opportunities and challenges for understanding biological systems. Interpreting these data requires integrated analysis of gene expression and cell types within their spatial context, however, existing tools fall short in scaling this linked interactive exploration to large-scale datasets. We present PathwayFinder, a visual analytics application designed to bridge the gap between gene-centric and cell-centric analysis. Unlike standard workflows that view cells and genes in isolation, PathwayFinder links them through a multi-scale interactive interface. This allows researchers to explore the relationships between transcriptomic profiles and tissue architecture, for example, by selecting a cell type to instantly discover its potential marker genes, or brushing gene modules to identify cell populations expressing them and their spatial distributions. Through this tight integration of gene, cell, and spatial views, PathwayFinder facilitates rapid discovery of co-expressed gene modules, specialized cell subpopulations, and functional hypotheses in spatial contexts.
Related
- Spatially resolved host-fungal transcriptomics profiling by fungal-DBiT
- Spatially resolved transcriptomics of poplar reveals tissue organization across shoot-associated organs
- SpCAST enables scalable and interpretable integration of single-cell RNA sequencing and single-cell-resolved spatial transcriptomics
- SpaCoEx: Sparse Gene Selection for Spatially Varying Co-expression in Spatial Transcriptomics
- Covariance Nonstationarity is Evident in Spatial Transcriptomics and Provides a New Categorization of Spatially Varying Genes
BibTeX
@article{li2026pathwayfinder,
title = {{PathwayFinder enables linked exploration of cell types and gene modules in spatially resolved transcriptomics data}},
author = {Chang Li and Julian Thijssen and Thomas Kroes and Lisa Koorneef and Thomas Höllt and Louise van der Weerd and Boudewijn Lelieveldt},
journal = {iScience},
year = {2026},
month = oct,
doi = {10.1016/j.isci.2026.118043},
url = {https://www.cell.com/iscience/fulltext/S2589-0042(26)03426-7},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - PathwayFinder enables linked exploration of cell types and gene modules in spatially resolved transcriptomics data
AU - Chang Li
AU - Julian Thijssen
AU - Thomas Kroes
AU - Lisa Koorneef
AU - Thomas Höllt
AU - Louise van der Weerd
AU - Boudewijn Lelieveldt
PY - 2026
DA - 2026/10/29
JO - iScience
DO - 10.1016/j.isci.2026.118043
UR - https://www.cell.com/iscience/fulltext/S2589-0042(26)03426-7
AB - The rapid development of spatially resolved transcriptomics technologies has provided opportunities and challenges for understanding biological systems. Interpreting these data requires integrated analysis of gene expression and cell types within their spatial context, however, existing tools fall short in scaling this linked interactive exploration to large-scale datasets. We present PathwayFinder, a visual analytics application designed to bridge the gap between gene-centric and cell-centric analysis. Unlike standard workflows that view cells and genes in isolation, PathwayFinder links them through a multi-scale interactive interface. This allows researchers to explore the relationships between transcriptomic profiles and tissue architecture, for example, by selecting a cell type to instantly discover its potential marker genes, or brushing gene modules to identify cell populations expressing them and their spatial distributions. Through this tight integration of gene, cell, and spatial views, PathwayFinder facilitates rapid discovery of co-expressed gene modules, specialized cell subpopulations, and functional hypotheses in spatial contexts.
SN - 2589-0042
ER -