Description Usage Arguments Details Value Slots Methods Author(s) See Also Examples

Represents the prior distribution used to infer copy number from targeted deep sequencing.

1 2 3 4 5 6 7 8 |

`alpha` |
Parameter for the beta prior distribution on the fraction of normal cells. |

`beta` |
Parameter for the beta prior distribution on the fraction of normal cells. |

`pAbnormal` |
Prior probability of an aberration; split equally between gain and loss. |

`pGain` |
Prior probability of a gain in copy number. Defaults to
NULL, in which case it is set to half of |

`pLoss` |
Prior probability of a loss of copy number. Defaults to
NULL, in which case it is set to half of |

`pMutWrong` |
Prior probability that something called a somatic mutation is actually a known single nucleotide polymorphism (SNP). |

`pSnpWrong` |
Prior probability that something called a SNP is actually a somatic mutation. |

`resn` |
The number of points at which to compute values of the beta distribution in order to compute the likelihood and posterior. |

`object` |
object of class |

`x` |
object of class |

`...` |
extra arguments for generic or plotting routines. |

`lwd` |
Line width parameter. |

The `DeepCNV`

class is used to fit a Bayesian model to targeted
sequencing data from one or a few genes in order to draw inferences
about possible copy number changes. Basically, we assume that the
observed data consists of a list of triples (K, N, V), one for each
variant in a gene. Here K is the number of variant reads, N is the
total number of reads, and V is the type of each variant (either a
known SNP or a somatic mutation). We model (K, N) using a binomial
distribution, where the 'success' parameter depends (in a
deterministic way) on the unknown parameters of interest: the fraction
*ν* of normal cells in the sample and the copy number state (Normal,
Deleted, or Gained).

The prior distribution consists of a continuous (by default,
Beta*(α, β)*) distribution on *ν* and a discrete
distribution on the copy number state S. The discrete distribution
can be defined either by setting `pAbnormal`

, in which case the
probability os divided equally between Deleted and Gained, or by
setting both `pGain`

and `pLoss`

.

Note that the settings of `pGain`

and `pLoss`

, if present,
override any setting of `pAbnormal`

.

One complication arises from the variant type V, which can be observed
with error. In some cases, observations of V are compared with a
matched normal sample of DNA from the same subject, in which case the
variant type is known with great confidence. In the absence of
matched normal samples, however, the variant type may be inferred
indirectly from an external database like dbSNP, in which case the
confidence decreases. Our confidence in these calls is represented by
the prior probabilities assigned in `pMutWrong`

and
`pSnpWrong`

. For purposes of our analysis, V is a nuisance
parameter that we mostly try to hide from the user. However, the full
structure of the discrete prior is revealed when using the
`summary`

method to look at the prior.

The `setCNVPrior`

constructor returns a valid object of the
`CNVPrior`

class.

The `cnPrior`

function returns a vector of length three containing the
prior probabilities of the copy number states.

`discrete`

:Data frame containing the discrete part of the prior.

`pGain`

:Prior probability of a gain in copy number.

`pLoss`

:Prior probability of a loss of copy number.

`pMutWrong`

Prior probability that something called a somatic mutation is actually a known single nucleotide polymorphism (SNP).

`pSnpWrong`

Prior probability that something called a SNP is actually a somatic mutation.

`alpha`

Parameter for the beta prior distribution on the fraction of normal cells.

`beta`

Parameter for the beta prior distribution on the fraction of normal cells.

`grid`

:Realized grid of points at which the continuous prior is computed.

`pdf`

:Values of the prior distribution at the

`grid`

points.

- plot(x, ...)
PLots the continuous part of the prior distribution.

- summary(object, ...)
Writes out a table detailing the discrete part of the prior distribution.

Kevin R. Coombes krc@silicovore.com

1 2 3 4 | ```
prior <- setCNVPrior(alpha=1.2, beta=4.8, pAbnormal=0.6)
cnPrior(prior)
summary(prior)
plot(prior)
``` |

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