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Last data update: 2014.03.03

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

segment

R Documentation

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

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

Examples

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

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(CODEX)
Loading required package: Rsamtools
Loading required package: GenomeInfoDb
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: S4Vectors

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomicRanges
Loading required package: Biostrings
Loading required package: XVector
Loading required package: BSgenome.Hsapiens.UCSC.hg19
Loading required package: BSgenome
Loading required package: rtracklayer

Attaching package: 'CODEX'

The following object is masked from 'package:BiocGenerics':

    normalize

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/CODEX/segment.Rd_%03d_medium.png", width=480, height=480)
> ### Name: segment
> ### Title: Recursive segmentation algorithm for CNV detection and
> ###   genotyping
> ### Aliases: segment
> ### Keywords: package
> 
> ### ** Examples
> 
> 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")
Segmenting sample 1: NA06994.
Segmenting sample 2: NA10847.
Segmenting sample 3: NA11840.
Segmenting sample 4: NA12249.
Segmenting sample 5: NA12716.
Segmenting sample 6: NA12750.
Segmenting sample 7: NA12751.
Segmenting sample 8: NA12760.
Segmenting sample 9: NA12761.
Segmenting sample 10: NA12763.
Segmenting sample 11: NA18966.
Segmenting sample 12: NA18967.
Segmenting sample 13: NA18968.
Segmenting sample 14: NA18969.
Segmenting sample 15: NA18970.
Segmenting sample 16: NA18971.
Segmenting sample 17: NA18972.
Segmenting sample 18: NA18973.
Segmenting sample 19: NA18974.
Segmenting sample 20: NA18975.
Segmenting sample 21: NA18976.
Segmenting sample 22: NA18981.
Segmenting sample 23: NA18987.
Segmenting sample 24: NA18990.
Segmenting sample 25: NA18991.
Segmenting sample 26: NA19098.
Segmenting sample 27: NA19119.
Segmenting sample 28: NA19131.
Segmenting sample 29: NA19137.
Segmenting sample 30: NA19138.
Segmenting sample 31: NA19141.
Segmenting sample 32: NA19143.
Segmenting sample 33: NA19144.
Segmenting sample 34: NA19152.
Segmenting sample 35: NA19153.
Segmenting sample 36: NA19159.
Segmenting sample 37: NA19160.
Segmenting sample 38: NA19171.
Segmenting sample 39: NA19200.
Segmenting sample 40: NA19201.
Segmenting sample 41: NA19204.
Segmenting sample 42: NA19206.
Segmenting sample 43: NA19207.
Segmenting sample 44: NA19209.
Segmenting sample 45: NA19210.
Segmenting sample 46: NA19223.
> finalcall
 sample_name chr  cnv   st_bp      ed_bp      length_kb st_exon ed_exon raw_cov
 "NA18990"   "22" "dup" "22312814" "22326373" "13.56"   "60"    "72"    "1382" 
 norm_cov   copy_no lratio   mBIC   
 "1005.096" "3"     "57.782" "47.18"
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>