README.Rmd

---
title: "README"
author: "Pourya Naderi"
date: "2/17/2020"
output: github_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```


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.

``` r
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
==========