cnv_autoCaptureSignatures: Auto-capture signature and coresponding exposure
In ShixiangWang/VSHunter: Capture Variation Signatures from Genomic Data

Description Usage Arguments Value Author(s) See Also Examples

this is a wrapper of cnv_chooseSigNumber, cnv_extractSignatures and cnv_quantifySigExposure these three functions.

cnv_autoCaptureSignatures(sample_by_component, nTry = 12, nrun = 10,
  cores = 1, seed = 123456, plot = TRUE,
  consensusmap_name = "nmf_consensus", testRandom = TRUE,
  nmfalg = "brunet")

`sample_by_component`	a sample-by-component `matrix`, generate from cnv_generateSbCMatrix function.
`nTry`	the maximal tried number of signatures, default is 12. Of note, this value should far less than number of features or samples.
`nrun`	the number of run to perform for each value in range of 2 to `nTry`, default is 10. According to NMF package documentation, `nrun` set to 30~50 is enough to achieve robust result.
`cores`	number of compute cores to run this task. You can use `parallel::detectCores()` function to check how many cores you can use. If you are using `cnv_pipe()` feature, please do not use maximal number of cores in your computer, it may cause some unexpected problems.
`seed`	seed number.
`plot`	logical. If `TRUE`, plot rank survey.
`consensusmap_name`	a character, basename of consensus map output path.
`testRandom`	Should generate random data from input to test measurements. Default is `TRUE`.
`nmfalg`	specification of the NMF algorithm.

a list contains results of NMF best rank survey, run, signature matrix, exposure list etc..

Geoffrey Macintyre, Shixiang Wang

Other CNV analysis functions: cnv_chooseSigNumber, cnv_derivefeatures, cnv_extractSignatures, cnv_fitMixModels, cnv_generateSbCMatrix, cnv_getLengthFraction, cnv_pipe, cnv_plotDistributionProfile, cnv_plotFeatureDistribution, cnv_plotMixComponents, cnv_plotSignatures, cnv_quantifySigExposure, cnv_readprofile

## Not run: 
## load example copy-number data from tcga
load(system.file("inst/extdata", "example_cn_list.RData", package = "VSHunter"))
## generate copy-number features
tcga_features = cnv_derivefeatures(CN_data = tcga_segTabs, cores = 1, genome_build = "hg19")
## fit mixture model  (this will take some time)
tcga_components = cnv_fitMixModels(CN_features = tcga_features, cores = 1)
## generate a sample-by-component matrix
tcga_sample_component_matrix = cnv_generateSbCMatrix(tcga_features, tcga_components, cores = 1)
## optimal rank survey13
tcga_results = cnv_autoCaptureSignatures(tcga_sample_component_matrix, nrun=10, cores = 1)

## End(Not run)