Description Usage Arguments Value Author(s) References Examples

View source: R/seqCNA_functions.r

The seqnorm method performs several regressions on homogeneous regions of the tumoural genomic profile.

1 | ```
runSeqnorm(rco, norm.win = NULL, method = "quadratic", lambdabreak=8, minSeg=7, maxSeg=35, nproc = 2, plots = TRUE, folder = NULL)
``` |

`rco` |
A |

`norm.win` |
The genomic window size, given in kilobases, in which to normalize the read count profile.
Leave it to the default NULL value for automatic selection.
The window size should be the same as or a multiple of the summarization window in |

`method` |
A string being |

`lambdabreak` |
The lambda prime parameter in GLAD, indicating the penalty term used during the optimization of the number of breakpoints. |

`minSeg` |
The minimum number of segments in which the second pass regression is based. Too few would result in noisy estimates of the regression curve. We recommend to leave it to the default. |

`maxSeg` |
The minimum number of segments in which the second pass regression is based. Too many would include segments whose regression curve does not approximate the overall curve. Additionally, they would add additional execution time. We recommend to leave it to the default. |

`nproc` |
A value indicating how many processing cores to use in the segmentation prior to regression and in the segment regressions. Greater values speed up normalization using more CPU cores and RAM memory, but you should not use values greater than the number of cores in your machine. If unsure, the safest value is 1, but most computers nowadays are multi-core, so you could probably go up to 2, 4 or 8. |

`plots` |
A boolean value indicating whether to output normalization plots to the |

`folder` |
If the |

A `SeqCNAInfo-class`

object, with additional information on the normalized profile.

David Mosen-Ansorena

Analysis of array CGH data: from signal ratio to gain and loss of DNA regions. Hupe P, Stransky N, Thiery JP, Radvanyi F, Barillot E. Bioinformatics. 2004 Dec 12; 20(18):3413-22.

1 2 3 4 5 6 7 | ```
data(seqsumm_HCC1143)
rco = readSeqsumm(tumour.data=seqsumm_HCC1143)
rco = applyFilters(rco, 0, 1, 0, 2, FALSE, plots=FALSE)
### NORMALIZATION ###
rco = runSeqnorm(rco, plots=FALSE)
``` |

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