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PSF toolkit

The PSF Toolkit is an R package developed for topology-aware (TA) pathway analysis of various types of omics data. The package includes interactive modules for pathway editing and visualization, facilitating pathway curation and results interpretation.

Installation 

if (!require("remotes", quietly = TRUE))
    install.packages("remotes")

remotes::install_github("hakobyansiras/psf")

Note: Due to potential network issues with GitHub servers, the package download may take longer than expected. If you encounter timeout errors, try increasing the timeout limit using:

options(timeout = 400)

Usage

Running the Shiny App for Pathway Curation and Visualization

The PSF Toolkit includes a Shiny app for interactive pathway curation and visualization.

A detailed tutorial on pathway import, curation, and analysis is available on the website.

Build documentation site locally 

# Install development dependencies
install.packages(c("roxygen2", "pkgdown"), repos = "https://cloud.r-project.org")

# Generate Rd files (optional, already present)
roxygen2::roxygenise()

# Build site into docs/
pkgdown::build_site()

Running PSF on Bulk RNA-seq Data with 40 Curated KEGG Signaling Pathways 

library(psf)

# Load melanoma DESeq2 normalized expression data (GSE112509)
load(system.file("extdata", "melanoma_exp.RData", package="psf"))

# Load curated KEGG pathway collection
load(system.file("extdata", "kegg_curated_40_signalings.RData", package="psf"))

# Calculate fold change (FC) values against the global mean
melanoma_fc <- (melanoma_deseq_normalized_counts + 1) / rowMeans(melanoma_deseq_normalized_counts + 1)

# Run PSF analysis on 40 pathways for all samples
melanoma_psf <- run_psf(entrez.fc = melanoma_fc, kegg.collection = kegg_curated_40_signalings, 
                        calculate.significance = FALSE, ncores = 4)

# Plot KEGG image-based PI3K-Akt signaling pathway with color-coded nodes
plot_pathway(melanoma_psf$PI3K_Akt_signaling_pathway, plot_type = "kegg", 
             color_nodes = "psf_activities", sample_id = "mean", log_norm = TRUE, 
             plot_sink_values = TRUE, use_old_images = TRUE)

# Generate PDF reports for PSF-analyzed pathways
generate_psf_report(psf_list = melanoma_psf, folder_name = "example_psf_report", 
                    plot_type = "kegg", log_norm = TRUE, use_old_images = TRUE)

Running PSF on Spatial Transcriptomics Data

Important: Due to installation issues with the Seurat package, it is included in Suggests rather than Imports. The PSF package will still install even if Seurat fails to install. However, you must manually install Seurat to perform spatial transcriptomic analyses.

library(psf)
library(Seurat)

# Load the spatial dataset. The human melanoma dataset can be downloaded from the following link: https://www.10xgenomics.com/datasets/human-melanoma-if-stained-ffpe-2-standard. To import the data into Seurat, please download the Feature / barcode matrix HDF5 (filtered) and the Spatial imaging data files.
spatial_melanoma <- Load10X_Spatial("/path/to/spatial_dataset", 
                                    filename = "CytAssist_FFPE_Human_Skin_Melanoma_filtered_feature_bc_matrix.h5")

# Load pre-downloaded gene symbol to Entrez ID conversion data
load(system.file("extdata", "gene_symbol_to_entrez.RData", package="psf"))

# Load curated KEGG signaling pathways
load(system.file("extdata", "kegg_curated_40_signalings.RData", package="psf"))
load(system.file("extdata", "kegg_sink_to_process.RData", package="psf"))

# Run pathway analysis
psf_spatial <- spatial_psf_analysis(spatial_obj = spatial_melanoma, 
                                    pathway_collection = kegg_curated_40_signalings, 
                                    gene_symbol_to_entrez = gene_symbol_to_entrez, nthreads = 1)

# Launch the PSF spatial browser application
run_psf_spatial_browser(psf_spatial_results = psf_spatial)

App Usage

A short demo video demonstrating the PSF spatial browser is available: PSF Spatial Browser Demo

You can also explore a demo Shiny app: PSF Spatial Browser

The app includes a dedicated Help tab containing detailed instructions on how to use it.

Running PSF on Telomere Maintenance Mechanism (TMM) Pathways 

library(psf)

# Load melanoma DESeq2 normalized expression data
load(system.file("extdata", "melanoma_exp.RData", package="psf"))

# Load TMM pathway
load(system.file("extdata", "tmm_pathway.RData", package="psf"))

# Calculate fold change (FC) values against the global mean
melanoma_fc <- (melanoma_deseq_normalized_counts + 1) / rowMeans(melanoma_deseq_normalized_counts + 1)

# Run PSF analysis on the TMM pathway for all samples
psf_tmm_result_mel <- psf.from.env.entrez.fc(entrez.fc = melanoma_fc,
                                             kegg.collection = tmm_pathway, 
                                             calculate.significance = FALSE, sum = FALSE, split = FALSE, 
                                             return_only_signals = FALSE, tmm_mode = TRUE, tmm_updated_mode = TRUE)

# Plot the TMM pathway for a sample
plot_tmm_pathway(pathway = psf_tmm_result_mel$X031$tmm, no_color_mode = FALSE,
                 mapping_data_type = "signal", log_norm = FALSE, layout = "layout_nicely")

For more details, please refer to the package documentation and tutorials.