Moma-class: MOMA Object
In MOMA: Multi Omic Master Regulator Analysis

Main class encapsulating the input data and logic of the MOMA algorithm

viper: matrix of inferred activity score inferred by viper
mut: binary mutation matrix 1 for presence of mutation, 0 for not, NA if not determined
cnv: matrix of cnv values. Can be binary or a range.
fusions: binary matrix of fusion events if appliable
pathways: list of pathways/connections to consider as extra evidence in the analysis
gene.blacklist: character vector of genes to not include because of high mutation frequency
output.folder: character vector of location to save files if desired
gene.loc.mapping: data frame of gene names, entrez ids and cytoband locations
nes: field for saving Normalized Enrichment Matrices from the associate events step
interactions: field for saving the MR-interactions list
clustering.results: results from clustering are saved here
ranks: results field for ranking of MRs based on event association analysis
hypotheses: results field for saving events that have enough occurences to be considered
genomic.saturation: results field for genomic saturation analysis
coverage.summaryStats: results field for genomic saturation analysis
checkpoints: results field with the MRs determined to be the checkpoint for each cluster
sample.clustering: field to save sample clustering vector. Numbers are cluster assignments, names are sample ids

Cluster( clus.eval = c("reliability", "silhouette"), use.parallel = FALSE, cores = 1 ): Cluster the samples after applying the MOMA weights to the VIPER scores
makeInteractions( genomic.event.types = c("amp", "del", "mut", "fus"), cindy.only = FALSE ): Make interaction web for significant MRs based on their associated events
Rank( use.cindy = TRUE, genomic.event.types = c("amp", "del", "mut", "fus"), use.parallel = FALSE, cores = 1 ): Rank MRs based on DIGGIT scores and number of associated events
runDIGGIT(fCNV = NULL, cnvthr = 0.5, min.events = 4, verbose = FALSE): Run DIGGIT association function to get associations for driver genomic events
saturationCalculation( clustering.solution = NULL, cov.fraction = 0.85, topN = 100, verbose = FALSE ): Calculate the number of MRs it takes to represent the desired coverage fraction of events