README

Pourya Naderi 2/17/2020

Centrality in Pathways Manual and Walk Through

Overview

This document contains the walk-through for the accompanying code for the publication “Revisiting the Use of Graph Centrality Models in Biological Pathway Analysis”.

Citation

TBA

Dependencies and Installation guide

The provided codes have the following dependencies: graph, stringr, dplyr, magrittr, CHRONOS,pathview,KEGGgraph,Rgraphviz,igraph, org.Mm.eg.db,tm,stringi,stringr,dplyr,sna, RBGL,tidyr,AnnotationDbi,org.Hs.eg.db,annotate,biomaRt,Hmisc,broom,xtable. Make sure the packages are installed before using. To install the packages you can use the following code chunk.

libList <- c("CHRONOS","pathview","KEGGgraph","Rgraphviz","igraph",
             "org.Mm.eg.db","tm","stringi","stringr","dplyr","sna",
             "RBGL","tidyr","AnnotationDbi","org.Hs.eg.db","annotate",
             "biomaRt","Hmisc","broom","xtable")

#source("https://bioconductor.org/biocLite.R")

# for(i in libList) {
#     if(!require(i)){
#         install.packages(i,repos = "http://cran.us.r-project.org")
#     }
#     if(!require(i)){
#         BiocManager::install(i)
#     }
# }

File guides

The folders human_data and mouse_data contain background data, preprocessed pathways, and precomputed centrality values. and its subfolders contains relevant data and code accompanying the package and the original manuscripts. The folders HUMAN_PATHWAYLIST and MOUSE_PATHWAYLIST contain the raw unprocessed XML files of the original pathways.

The folders Human Processing and Mouse Processing contain the scripts. Since the files in the folders are identifical, we will only review the contents of the Human Processing folder.

• 00-basicFunctions.R: Contains basic functions as used throughout the scripts.
• 01-00-PathwayParsing.R: Contains a retrieve script for KEGG Pathways and processing them into graph objects. This script calculates some basic stats regarding pathways as well.
• 01-01-CentralityTable.R: Produces centrality values for all qualified pathways using the centrality models, as described in the manuscript.
• 02-pathwayCleaning.R: Calculates the quantile score as described in the manuscript and filters out the pathways containing less than 5 cancer genes.
• 03-1-regressionAnalysis.R: Computes the regression models for the percentage of cancer genes with in each quantile-normalized score and generates reports.
• 03-2-KStests.R: Kolmogorov-Smirnov test statistics for comparing the distribution of cancer genes and non-cancer genes across all pathways.
• 03-3-pathwayWiseTesting.R: Contains two-sample test between the centrality values of cancer genes and non-cancer genes within each pathway.
• 04-01-pgr_sensitivity.R: This script generates PageRank centrality values across different alpha values.
• 04-02-pgr_sensitivity_fit.R: Repeats the regression analysis for PageRank centrality values from the last script.

References

Naderi Yeganeh P, Richardson C, Saule E, Loraine A, Taghi Mostafavi M. Revisiting the use of graph centrality models in biological pathway analysis. BioData Min. 2020;13:5. Published 2020 Jun 16. doi:10.1186/s13040-020-00214-x