Description Usage Arguments Details Value Author(s)

Z-score computation from bin count and/or mean/sd metrics on the reference samples.

1 2 |

`bc.f` |
the path to the normalized bin count file. |

`norm.stats.f` |
the name of the file with the statistic of the targeted normalization run. |

`files.df` |
a data.frame with the file paths. |

`z.poisson` |
Should the Z-score be computed as an normal-poisson hybrid (see Details). Default is FALSE. |

`nb.cores` |
the number of cores to use. |

`chunk.size` |
the chunk size. Default is 1e4. If NULL, no chunks are used. |

`append` |
should the Z-scores be appended to existing files. Default is FALSE. |

`recomp.msd` |
Should the mean/SD be recomputed and written to 'norm.stats.f' ? Default is FALSE. Most of the times you don't want that but it's useful when combining discordant mapping. |

The Z-score is computed by substracting the bin count by the average bin count across the reference samples and dividing by their standard deviation. If 'z.poisson' is TRUE, a score using Poisson distribution is also computed, using the average bin count as an estimator of the lambda. Then the score with the lowest absolute value is kept. This hybrid Z-score is to be used when some regions have low coverage where it is more robust to use Poisson assumptions.

a list with

`z` |
a data.frame with the Z-scores if 'bc.f' was a data.frame. NULL otherwise. |

`fc` |
a data.frame with the fold-changes if 'bc.f' was a data.frame. NULL otherwise. |

`msd` |
a data.frame with the mean-sd information if 'bc.f' was a data.frame. NULL otherwise. |

`z.poisson` |
was Normal-Poisson hybrid Z-score score computed. |

Jean Monlong

jmonlong/PopSV documentation built on Dec. 9, 2018, 10:10 a.m.

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