Package index • psf

Pathway curation (Shiny)

run_shiny_app(): Run KEGG interactive editor and visualization App
pathway_shiny_vis(): Run KEGG interactive visualization App for provided pathway
generate.kegg.collection(): Download kegg pathways of provided pathway id list from keggrest and generate kegg collection
generate.kegg.collection.from.kgml(): Generate kegg collection from kgml files
download.KGML(): Download a KEGG pathway from the web
parse.KGML(): Parse KGML to a graph object
get.pathway.attrs(): Get pathway general attributes

Analysis (Shiny and R)

run_psf(): Calculates pathway activity with PSF algorithm for provided kegg collection based on expression fold change data.
psf.from.env.entrez.fc(): Calculates pathway activity with PSF algorithm for provided kegg collection based on expression fold change data.
set.edge.impacts(): Sets edge impacts for PSF analysis
determine.sink.nodes(): Detects terminal nodes of the pathway
determine.input.nodes(): Returns vector of node ids which do not have incoming edges but only outgoing.
order.nodes(): Order graph node
map.gene.data(): Map gene data onto a pathway graph
plot_pathway(): Plots the pathway with colored nodes and labels
plot_kegg_image_pathway(): Plots the pathway with colored nodes and labels
plot_tmm_pathway(): Plots the TMM pathway with colored nodes and labels with interactive network.

Reporting

generate_psf_report(): Generates pdf report with colored pathways and plots
calc_psf_and_generate_report_from_collection(): Calculates psf for given kegg pathway based on expression matrix and generates pdf report with colored pathways and plots

Utilities (partial influence and graph)

run_pi(): Performs partial influence analysis which evaluates effect of each pathway node on specific node(s) of the pathway.
calc_node_partial_influences(): Returns ordered list of the nodes by their influence on the signal of specified nodes.
graphnel_to_df(): Converts graphNEL object to 2 data frames(node_table, edge_table)
df_to_graphnel(): Converts 2 data frames(node_table, edge_table) to graphNEL object
edge_data_frame_from_graph(): Export data frame from graphNEL graph for edge data and its attributes
update_edge_weights(): Import edge weights extracted(further edited) via edge_data_frame_from_graph function
correctEdgeDirections(): Guess wrong directed binding interactions and reverse them
isReverseDirection(): This function predicts if the edge diractions are wrong based on graphical position of the KEGG nodes
out.edges(): provide outgoing edges of the specified node
edge.exists(): Check if the edge exists in the graph
get.edge.type(): Returns the general edge type (either activation or inhibition)
process.compounds(): Process protein compound interactions
process.groupNode(): Extend the group node to its component gene nodes
redirectEdge(): Redirect the edge to new source and target nodes
reverseEdge(): Reverse edge direction
remove.disconnected.nodes(): Remove nodes which do not have any interactions with other nodes
add.kegg.edge(): Add edge to GraphNEL graph
add.kegg.edge.mut(): Add edge to GraphNEL graph
addEdgeSafe(): Add an edge between two nodes if no such edge exists, and if no reverse edge exists

Use cases (Spatial and TMM)

spatial_psf_analysis(): Performs pathway activity analysis of Spatial transcriptomics data and subsequent clustering with Seurat clustering. Input data is a Seurat object.
run_psf_spatial_browser(): Run spatial PSF browser App
interactive_spatial_plot(): Renders interactive plot of spatial tissue slice.
plot_tmm_pathway(): Plots the TMM pathway with colored nodes and labels with interactive network.