The pathfindR package provides two important main functions: run_pathfindR and
choose_clusters.
This function is the wrapper function for the pathfindR
workflow. It takes in a data frame consisting of Gene Symbol,
log-fold-change (optional) and adjusted-p values. After input testing, any gene
symbols that are not in the PIN are converted to alias symbols if the alias
is in the PIN. Next, active subnetwork search is performed. Pathway
enrichment analysis is performed using the genes in each of the active
subnetworks. Pathways with adjusted-p values lower than
enrichment_threshold are discarded. The lowest adjusted-p value
(over all subnetworks) for each pathway is kept. This process of active
subnetwork search and enrichment is repeated for a selected number of
iterations, which is executed in parallel. Over all iterations, the
lowest and the highest adjusted-p values, as well as number of occurrences
are reported for each enriched pathway.
This function first calculates the pairwise
kappa statistics between the terms in the result_df data frame. By default,
hierarchical clustering is performed and the optimal number of clusters is chosen.
Optionally, a fuzzy partitioning algorithm can also be used. The function returns
a data frame with cluster assignments.
See run_pathfindR and cluster_pathways
for more details.