Moma-class {MOMA} | R Documentation

## MOMA Object

### Description

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

### Fields

`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

### Methods

`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

[Package MOMA version 1.2.0 Index]