ESSNet v1.3
===========

ABOUT US
 
ESSNet is developed in the School of Computing
National University of Singapore
Supervisor: Prof. Wong Limsoon
Developer: Kevin Lim


REFERENCE

Kevin Lim, Zhenhua Li, Kwok Pui Choi, Limsoon Wong.
"A quantum leap in the reproducibility, precision, and 
sensitivity of gene expression profile analysis even 
when sample size is extremely small".
Journal of Bioinformatics and Computational Biology,
13(4):???, August 2015.


DOWNLOAD
 
ESSNet v1.3 is available for the R platform and requires the 
following dependencies:
 
Java 7 SDK [http://www.oracle.com/technetwork/java/javase/downloads/index.html]
Aparche Ant [http://ant.apache.org]
R Statistical Software [http://www.r-project.org]
    ::requires(igraph, rJava)
Recommended at least 1GB of RAM


INSTRUCTIONS

Please read the instructions below carefully, on how to load and use ESSNet.

1/ Install pre-requisites

Instructions for installing java, ant and R can be found in their
respective websites.

2/ Load the ESSNet package into R

Unzip the file essnet_1.2.tar.gz to a specific folder.
In a terminal change directory to the specified folder 
and paste the following command:
>> R CMD install essnet

3/ Usage
 
Start R or RStudio, depending on your preference of a command line
tool or graphical interface.
Load the library using the following command:
>>install.packages(igraph)
>>install.packages(rJava)
>>library(essnet)
>>library(igraph)
>> library(rJava)

Examples:

#run essnet to find the subnetworks upregulated in class1
>>results <- essnet("./class1.txt","./class2.txt","./pathway.txt")

#run essnet to find the subnetworks upregulated in class2
>>results <- essnet("./class2.txt","./class1.txt","./pathway.txt")

#plot the first subnetwork
>>tkplot(results[[1]])

#show the p-value
>>results[[1]]$p.value
 
#other options

#weighted version
>>results <- essnet("./class1.txt","./class2.txt","./pathway.txt",weighted=TRUE)

#generate p-values by rotation test
>>results <- essnet("./class1.txt","./class2.txt","./pathway.txt",rot=TRUE)
 
 
#############################################################################

where:

class1.txt and class2.txt are tab delimited files representing a 
matrix of values:
rows represent the genes and columns represent the samples,
the first column denote the geneids.
pathway.txt is a tab delimited file, each row having the following format:
[pathwayid] [TAB] [geneid1] [TAB] [geneid2]
note that the geneids should match in the gene expression files 
and pathway file.