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

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. It includes routines to simulate
read-counts with known copy number state and known fraction of normal
'contaminating' cells.

1 2 3 |

`nMut` |
integer; the number of somatic mutations in a gene |

`nVar` |
integer; the number of variant SNPs in a gene |

`nu` |
numeric between 0 and 1; the fr4action of normal cells in the sample |

`cnstate` |
the copy number state of the gene; must be one of
"Deleted", "Normal", or "Gained", as enumerated in |

`depth` |
integer; the average read depth at the gene |

`sdepth` |
integer; the stnadard deviation of the read depth across varaints within a gene |

`...` |
extra parameters to pass from |

`S` |
The copy number state, as enumerated in |

`V` |
The variant type. Must be one of "Mutation" or "SNP" as
enumerated in |

`M` |
the total number of replicate copies of a (allelic) gene. The default value of 2 corresponds to a gain of one copy |

`K` |
Number of variant reads |

`N` |
Number of total reads (both variant and refernce) |

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 *φ* 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 functions `cnvLikelihood`

and `CNVariant`

are used to
compute the log-likelihood of the unknown parameters given the
observed data. `CNVariant`

computes the success parameter
*φ* as a function of the observed data (K, N, V), and this
parameter is then used to compute the binomial log-likelihood.

The `simReads`

function generates simulated read-count data based
on the underlying theoretical binomial model. More details can be
found in the vignettes `d01-cnvTheory`

and
`d02-oneGeneSims`

.

The `simReads`

function returns a data frame suitable for use by
the function `makeCNVPosterior`

.

The `CNVariant`

function returns a real number between zero and
one, corresponding to the fraction of reads that are expected to be
variants given the variant type (`V`

), the copy number state
(`S`

), and the fraction of normal cells (`nu`

).

The `cnvLikelihood`

function returns a real number representing
the log-likelihood (yes, I know; it probably should be renamed) of the
parameters (S, *ν*) given the observed data (K, N).

Kevin R. Coombes krc@silicovore.com

1 2 3 4 5 6 7 | ```
simReads(nMut=2, nVar=7, nu=0.17, "Norm", depth=130 )
# check log-likelihhoods of different copy number states
# for the same observed data
obs <- data.frame(K=c(69, 48), N=c(153, 167))
cnvLikelihood(0.22, obs)
cnvLikelihood(0.22, obs)
cnvLikelihood(0.22, obs)
``` |

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