The KINC.R package provides supplementary R functions to assist with analysis of files used and generated by [KINC] (https://github.com/SystemsGenetics/KINC). An important function is the RMT() function which perform Random Matrix Theory (RMT) analysis of a network.
Clone this repository and start R in the cloned directory:
Now you can use KINC.R in R by importing the library:
While KINC has been written for gene co-expression networks, the RMT function can be used with any similarity matrix. The matrix must be in a data frame with at least three columns named: Source, Target and Similarity. The Source and Target columns indicate the edge in the network and the Similarity contains the similarity score. The RMT() function will perform RMT analysis on a similarity matrix that it constructs from the network file.
Example 1 -- RMT of Traditional KINC network
Below is an example from traditional KINC networks:
library('KINC.R') # Import the network from a file. colNames = c('Source', 'Target', 'Similarity', 'interaction') colClasses = c('character', 'character', 'numeric', 'character') net = read.table("KINC_traditional_net.txt", header=TRUE, sep="\t", colClasses=colClasses, col.names=colNames) # Now perform RMT analysis on the loaded network RMT(net)
In this example, the network file being read from a file was generated using the traditional KINC method and is named
KINC_traditional_net.txt. It is tab-delimited and has four columns: source, target, similarity score and interaction type.
Example 2 -- RMT of Clustered KINC Network
library('KINC.R') # Import the network from a file. net = loadNetwork('KINC_clustered_net.txt') # Now perform RMT analysis on the loaded network RMT(net)