*.summary.*.RData : *.summary.*.RData : This R object (data frame) is a summary table 
                              listing the raw and normalized activity, and SNVs across samples.

Characterizing underlying mutational processes with correct inferences for signature 
                 activities across samples provide a key understanding on cancer initiation and progression. 
                 However, the number of mutation processes K* is highly variable across patients even in a 
                 single tumor type and its accurate estimation is a non-trivial task due to a different duration and 
                 intensity of exposure to a specific mutational process. Non-negative matrix factorization (NMF) has been 
                 widely used in deciphering mutations signatures in cancer somatic mutations stratified by 96 base substitutions 
                 in tri-nucleotide sequence contexts. In contrast to conventional NMF requiring K* as an input parameter, BayesNMF
                 exploits the automatic relevance determination technique to infer the optimal K* from data itself at a 
                 balance between the data fidelity (likelihood) and the model complexity (regularization)

Bayesian non-negative matrix factorization (BayesNMF)

The formula of V1[i]%*%V2[j]/sqrt(sum(V1[i]^2))/sqrt(sum(V2[j]^2)) was used to calculate to similarity 
                                between two vectors. In our cases, the value will range from 0 to 1.
                                1 meaning exactly the same and 0 indicating orthogonality

COSMIC signature table taken from <a href="https://cancer.sanger.ac.uk/cosmic/signatures">https://cancer.sanger.ac.uk/cosmic/signatures</a> on 2018-05-09 with Keyword annotations added.

Table of COSMIC Signatures with Added Keyword Annotations

Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors