QPforSig {decompTumor2Sig} | R Documentation

## QPforSig (internal function)

### Description

Perform quadratic programming for signatures to determine exposures.

### Usage

    QPforSig(counts, signatures, constrainToMaxContribution=FALSE,
    tolerance=0.1)

### Arguments

`counts` | The genome's mutation frequencies (either in Alexandrov or Shiraishi format).
`signatures` | The signatures to be used for decomposition. Must be in the same format as the genome's `counts`.
`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

The decomposition in form of an exposure vector (same order as `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]