Evaluates genes for SMGs using Bayesian posterior predictive methods

1 | ```
baysic.test(dat.out, fit.out, fdr.level = 0.15, fuzzy.cnt = 10000, r = NULL,subtype = NULL, PB.approx = FALSE)
``` |

`dat.out` |
output from |

`fit.out` |
output from |

`fdr.level` |
numeric ( |

`fuzzy.cnt` |
number of Monte Carlo iterations to use in approximating fuzzy FDR values passed to |

`r` |
Optional number of MCMC draws to thin to for Monte Carlo integration, such that |

`subtype` |
Optional |

`PB.approx` |
logical; if |

When `is.list{ref.dat}`

is `TRUE`

, BaySIC evaluates whether or not a gene is an SMG using the Poisson-Binomial rather than the traditional binomial distribution. This accomodates subject-specific mutation rates given varying sequence content. When *N* is relatively large (e.g., *N≥q50*) it is recommended that optional arguments `r`

and `PB.approx`

be considered to alleviate computational burden.

Returns a `list`

object with the following components:

`test.res` |
a matrix with |

`fdr.level` |
value of |

`fuzzy.cnt` |
value of |

`subtype` |
value of |

Nicholas B. Larson

1 2 3 4 5 6 7 8 9 10 11 12 | ```
## Not run:
data(example.dat)
data(ccds.19)
baysic.dat.ex<-baysic.data(example.dat,ccds.19)
snv.cat.ex<-list()
snv.cat.ex[[1]]<-grep("[^T]C[^G]",colnames(ccds.19)[-c(1:2)])
snv.cat.ex[[2]]<-unique(c(grep("TC.",colnames(ccds.19)[-c(1:2)]),grep(".CG",colnames(ccds.19)[-c(1:2)])))
snv.cat.ex[[3]]<-grep(".T.",colnames(ccds.19)[-c(1:2)])
baysic.fit.ex<-baysic.fit(baysic.dat.ex,snv.cat.ex)
baysic.test.ex<-baysic.test(baysic.dat.ex,baysic.fit.ex)
## End(Not run)
``` |

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