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.
install.packages("knitr")
install.packages("rmarkdown")
install.packages("remotes")# Main packages
remotes::install_github("sysbiolab/RGraphSpace", build_vignettes=TRUE)
remotes::install_github("sysbiolab/PathwaySpace", build_vignettes=TRUE)
# Extension
remotes::install_github("sysbiolab/SpotSpace", build_vignettes=TRUE)
# Data package used in the extension
remotes::install_github("satijalab/seurat-data")If you use PathwaySpace, please cite:
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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
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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
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).
The PathwaySpace package is distributed under Artistic-2.0
