PathwaySpace: Spatial projection of network signals along geodesic paths

Highlights

• Produces landscape images representing graphs by geodesic paths
• Projects signals using a decay function to model signal attenuation
• Applies a convolution algorithm to combine signals from neighboring vertices

Tutorials

• https://sysbiolab.github.io/PathwaySpace

Overview

PathwaySpace is an R package that creates landscape images from graphs containing vertices (nodes), edges (lines), and a signal associated with the vertices. The package processes the signal using a convolution algorithm that considers the graph's topology to project the signal on a 2D space. Figure1 illustrates the convolution operation problem addressed by the PathwaySpace package. For detailed documentation and usage examples, see the package's vignettes and workflows.

PathwaySpace could have various applications, such as highlighting relationships and signal strengths between network vertices, visualizing sparse feature sets on large graphs, and exploring signal patterns in spatial transcriptomics.

Installation in R (>=4.5)

Dependencies to build the vignettes

install.packages("knitr")
install.packages("rmarkdown")
install.packages("remotes")

Development version

remotes::install_github("sysbiolab/RGraphSpace", build_vignettes=TRUE)
remotes::install_github("sysbiolab/PathwaySpace", build_vignettes=TRUE)

Citation

If you use PathwaySpace, please cite:

• Tercan & Apolonio et al. Protocol for assessing distances in pathway space for classifier feature sets from machine learning methods. STAR Protocols, 6(2):103681, 2025. https://doi.org/10.1016/j.xpro.2025.103681

• Ellrott et al. Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets. Cancer Cell, 43(2):195-212.e11, 2025. https://doi.org/10.1016/j.ccell.2024.12.002

Supporting Material for Tercan et al. (2025)

• Tercan_et_al_20250112.zip

Licenses

The PathwaySpace package is distributed under Artistic-2.0