GitHub - sysbiolab/PathwaySpace: PathwaySpace is an R package that creates landscape images from graphs containing vertices (nodes), edges (lines), and a signal associated with the vertices. (original) (raw)
PathwaySpace: Spatial projection of network signals along geodesic paths
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.Figure 1 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 visualizing network data in a graphical format that highlights the relationships and signal strengths between vertices.
Figure 1. Signal processing addressed by the PathwaySpace package.A) Graph overlaid on a 2D coordinate system. Each projection cone represents the signal associated with a graph vertex (referred to as vertex-signal positions), while question marks indicate positions with no signal information (referred to as null-signal positions). Inset: Graph layout of a toy example used in the package's vignette. B) Illustration of signal projection from two neighboring vertices, simplified to one dimension. Right: Signal profiles from aggregation and decay functions.
Installation in R (>=4.4)
Install dependencies to build the package's vignettes
install.packages("knitr") install.packages("rmarkdown")
Install the PathwaySpace package
install.packages("remotes") remotes::install_github("sysbiolab/RGraphSpace", build_vignettes=TRUE) remotes::install_github("sysbiolab/PathwaySpace", build_vignettes=TRUE)
Examples
Follow the PathwaySpace vignette and try to make some brain plots!
library(PathwaySpace) vignette("PathwaySpace")
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, 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, 2025. https://doi.org/10.1016/j.ccell.2024.12.002
Supporting Material for Tercan et al. (2025)
Download and uncompress Tercan_et_al_20250112.zip, then follow the instructions in the pspace_perturbation.R script. This R script has been developed to reproduce the results presented in Figure S1 of Tercan et al. (2025).
Licenses
The PathwaySpace package is distributed under Artistic-2.0