segment: Recursive segmentation algorithm for CNV detection and...

Description Usage Arguments Value Author(s) See Also Examples

Description

Recursive segmentation algorithm for CNV detection and genotyping, using normalized read depth from whole exome sequencing.

Usage

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segment(Y_qc, Yhat, optK, K, sampname_qc, ref_qc, chr, lmax, mode)

Arguments

Y_qc

Raw read depth matrix after quality control procedure returned from qc

Yhat

Normalized read depth matrix returned from normalize

optK

Optimal value K returned from choiceofK

K

Number of latent Poisson factors. Can be an integer if optimal solution has been chosen or a vector of integers so that AIC, BIC, and RSS are computed for choice of optimal k.

sampname_qc

Vector of sample names after quality control procedure returned from qc

ref_qc

IRanges object of genomic positions of each exon after quality control procedure returned from qc

chr

Chromosome number returned from getbambed

lmax

Maximum CNV length in number of exons returned.

mode

Can be either "integer" or "fraction", which respectively correspond to format of the returned copy numbers.

Value

Final callset of CNVs with genotyping results.

Author(s)

Yuchao Jiang yuchaoj@wharton.upenn.edu

See Also

normalize, choiceofK

Examples

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Y_qc <- qcObjDemo$Y_qc
Yhat <- normObjDemo$Yhat
BIC <- normObjDemo$BIC
K <- normObjDemo$K
sampname_qc <- qcObjDemo$sampname_qc
ref_qc <- qcObjDemo$ref_qc
chr <- bambedObjDemo$chr
finalcall <- segment(Y_qc, Yhat, optK = K[which.max(BIC)], K = K, sampname_qc,
    ref_qc, chr, lmax = 200, mode = "integer")
finalcall


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