Recursive segmentation algorithm for CNV detection and genotyping
Description
Recursive segmentation algorithm for CNV detection and genotyping, using normalized read depth from whole exome sequencing.
Usage
1  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

Yhat 
Normalized read depth matrix returned from

optK 
Optimal value 
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

ref_qc 
IRanges object of genomic positions of each exon after quality control
procedure returned from 
chr 
Chromosome number returned from 
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
1 2 3 4 5 6 7 8 9 10  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

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker. Vote for new features on Trello.