Description Usage Arguments Details Value Author(s)

Bin counts are normalized one bin at a time, using a subset of the bins that look similar across the reference samples.

1 2 3 |

`bc` |
a matrix or data.frame with the bin counts (bin x sample). |

`cont.sample` |
the sample to use as baseline for the pairwise normalization. All the samples will be normalized to it. |

`nb.support.bins` |
the number of bins to use for the normalization. |

`bins` |
a vector the names of the bins to normalize. If NULL (default), all bins are normalized. |

`save.support.bins` |
if TRUE (default) the bins used for the normalization are saved in the output object 'norm.stats'. |

`norm` |
the type of normalization. '1pass' (default) means one pass of normalization. Other options is 'trim' (/!\ experimental /!\). |

`force.diff.chr` |
should the supporting bins be forced to be in a different chromosome. Default is TRUE. |

A specific set of bins, defined by 'bins=', can de normalized using all the bins in 'bc'. The bin names in 'bins=' should be the chr and start position as in '1-501' (for chr 1, start 501).

The default approach ('norm="1pass"') looks for supporting across all samples. A more robust approach, finds supporting bins after trimming a few outlier samples (potential CNV). 'norm="trim"' normalization is better and recommended for small bins.

a list with

`norm.stats` |
a data.frame witht some metrics about the normalization of each bin (row) : correlation with worst supporting bin ('d.max'); coverage average ('m') and standard deviation ('sd'); number of outlier reference samples ('nb.remove'); supporting bins. |

`bc.norm` |
a matrix with the normalized bin counts (bin x sample). |

`nb.support.bins, cont.sample` |
a backup of the input parameters. |

Jean Monlong

jmonlong/PopSV documentation built on April 19, 2018, 2:10 p.m.

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