Best-case exhaustive feature analysis of the mutation types datamatrix.

Exhaustive multivariate feature analysis is used to highlight pairs of MT features giving similarity value equal to 1. Features in column 1 give similarity 1 if and only if coupled with one of the features in column 2. For example, feature A:T>C:G gives a similarity value 1 when coupled with G:C>A:T at CpGs. Relevance of individual features in column 1 is based on the number of other features in column 2 with which it can yield unitary value after pairing. The most relevant feature is G:C>A:T at CpGs, followed by A:T>C:G, G:C>A:T and G:C>C:G.