Centrality-Based Pathway Enrichment
Centrality-based pathway enrichment
use CePa package through parallel computing
Apply CePa algorithm on a list of pathways under multiple centralities
Apply centrality-extented ORA on a list of pathways
Apply centrality-extended ORA on a single pathway
Apply CePa algorithm on a single pathway
Apply centrality-extented GSA on a list of pathways
Apply centrality-extended GSA on a single pathway
Generate igraph object from edge list
get single cepa object from cepa.all object
Table of p-values of pathways
names of the pathway nodes
plot the cepa.all object
Plot the cepa object
plot pathway.catalogue object
Plot graph for the pathway network
Plot the null distribution of the pathway score
print the cepa.all object
print the cepa object
print pathway.catalogue object
Calculate radiality centrality
Calculate largest reach centrality
Read CLS file which stores the phenotype data
Read GCT format file which stores the expression values
Generate report for CePa analysis
Generate data structure of sample labels
store pathway data and pre-processing
Calculate radiality centrality
It aims to find significant pathways through network topology information. It has several advantages compared with current pathway enrichment tools. First, pathway node instead of single gene is taken as the basic unit when analysing networks to meet the fact that genes must be constructed into complexes to hold normal functions. Second, multiple network centrality measures are applied simultaneously to measure importance of nodes from different aspects to make a full view on the biological system. CePa extends standard pathway enrichment methods, which include both over-representation analysis procedure and gene-set analysis procedure. <doi:10.1093/bioinformatics/btt008>.