processMultipleSigSets {decompTumor2Sig} | R Documentation

## processMultipleSigSets (internal function)

### Description

Performs the quadratic programming/exposure prediction for multiple subsets (of size k) of mutational signatures and returns information on the best subset (highest explained variance). This function is used by `getBestDecomp4Ksignatures` and `addBestSignatureToSubset`.

### Usage

    processMultipleSigSets(genome, signatures, sigCombn, k,
    constrainToMaxContribution=FALSE, tolerance=0.1)

### Arguments

`genome` | Genome for which to approximate the decomposition.
`signatures` | The whole set of signatures (from which to choose a subset signatures.
`sigCombn` | The combinations of subsets of `k` signatures to use. Has to be the same format as generated by `combn`.
`k` | Number of signatures to use (subset size).
`constrainToMaxContribution` | (Optional) [Note: this is experimental and is usually not needed!] If `TRUE`, the maximum contribution that can be attributed to a signature will be constraint by the variant feature counts (e.g., specific flanking bases) observed in the individual tumor genome. If, for example, 30% of all observed variants have a specific feature and 60% of the variants produced by a mutational process/signature will manifest the feature, then the signature can have contributed up to 0.3/0.6 (=0.5 or 50%) of the observed variants. The lowest possible contribution over all signature features will be taken as the allowed maximum contribution of the signature. This allowed maximum will additionally be increased by the value specified as `tolerance` (see below). For the illustrated example and `tolerance`=0.1 a contribution of up to 0.5+0.1 = 0.6 (or 60%) of the signature would be allowed.
`tolerance` | (Optional) If `constrainToMaxContribution` is `TRUE`, the maximum contribution computed for a signature is increased by this value (see above). If the parameter `constrainToMaxContribution` is `FALSE`, the tolerance value is ignored. Default: 0.1.

### Value

A list object containing: k=number of signatures; explVar=variance explained by these signatures; sigList=list of the signatures; decomposition=decomposition (exposures) obtained with these signatures.

### Author(s)

Rosario M. Piro
Freie Universitaet Berlin
Maintainer: Rosario M. Piro
E-Mail: <rmpiro@gmail.com> or <r.piro@fu-berlin.de>

### References

http://rmpiro.net/decompTumor2Sig/
Krueger, Piro (2018) decompTumor2Sig: Identification of mutational signatures active in individual tumors. BMC Bioinformatics (accepted for publication).
Krueger, Piro (2017) Identification of Mutational Signatures Active in Individual Tumors. NETTAB 2017 - Methods, Tools & Platforms for Personalized Medicine in the Big Data Era, October 16-18, 2017, Palermo, Italy. PeerJ Preprints 5:e3257v1, 2017.

[Package decompTumor2Sig version 2.0.0 Index]