estimateSignatures: Estimate number of signatures based on cophenetic correlation...
In thesushantpatil/maftools: Summarize, Analyze and Visualize MAF Files

Description Usage Arguments Details Value See Also Examples

Estimate number of signatures based on cophenetic correlation metric

estimateSignatures(
  mat,
  nMin = 2,
  nTry = 6,
  nrun = 10,
  parallel = 4,
  pConstant = NULL,
  verbose = TRUE,
  plotBestFitRes = FALSE
)

`mat`	Input matrix of diemnsion nx96 generated by `trinucleotideMatrix`
`nMin`	Minimum number of signatures to try. Default 2.
`nTry`	Maximum number of signatures to try. Default 6.
`nrun`	numeric giving the number of run to perform for each value in range. Default 5
`parallel`	Default 4. Number of cores to use.
`pConstant`	A small positive value to add to the matrix. Use it ONLY if the functions throws an `non-conformable arrays` error
`verbose`	Default TRUE
`plotBestFitRes`	plots consensus heatmap for range of values tried. Default FALSE

This function decomposes a non-negative matrix into n signatures. Extracted signatures are compared against 30 experimentally validated signatures by calculating cosine similarity. See http://cancer.sanger.ac.uk/cosmic/signatures for details.

a list with NMF.rank object and summary stats.

plotCophenetic extractSignatures trinucleotideMatrix

## Not run: 
laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
laml.tnm <- trinucleotideMatrix(maf = laml, ref_genome = 'BSgenome.Hsapiens.UCSC.hg19', prefix = 'chr',
add = TRUE, useSyn = TRUE)
library("NMF")
laml.sign <- estimateSignatures(mat = laml.tnm, plotBestFitRes = FALSE, nMin = 2, nTry = 3, nrun = 2, pConstant = 0.01)

## End(Not run)