The default segmentation function. This function is called via the
fun.segmentation argument of runAbsoluteCN. The arguments are passed
via args.segmentation.
Copy number log-ratios, one for each exon in coverage file.
plot.cnv
Segmentation plots.
coverage.cutoff
Minimum coverage in both normal and tumor.
sampleid
Sample id, used in output files.
exon.weight.file
Can be used to assign weights to exons.
alpha
Alpha value for CBS, see documentation for the segment function.
vcf
Optional VCF object with germline allelic ratios.
tumor.id.in.vcf
Id of tumor in case multiple samples are stored in VCF.
verbose
Verbose output.
Value
A list with elements
seg
The segmentation.
size
The size of all segments (in base pairs).
Author(s)
Markus Riester
Examples
gatk.normal.file <- system.file("extdata", "example_normal.txt",
package="PureCN")
gatk.tumor.file <- system.file("extdata", "example_tumor.txt",
package="PureCN")
vcf.file <- system.file("extdata", "example_vcf.vcf",
package="PureCN")
gc.gene.file <- system.file("extdata", "example_gc.gene.file.txt",
package="PureCN")
# speed-up the runAbsoluteCN by using the stored grid-search.
# (purecn.example.output$candidates).
data(purecn.example.output)
# The max.candidate.solutions argument is set to a very low value only to
# speed-up this example. This is not a good idea for real samples.
ret <-runAbsoluteCN(gatk.normal.file=gatk.normal.file,
gatk.tumor.file=gatk.tumor.file,
vcf.file=vcf.file, sampleid='Sample1', gc.gene.file=gc.gene.file,
candidates=purecn.example.output$candidates, max.candidate.solutions=2,
fun.segmentation=segmentationCBS, args.segmentation=list(alpha=0.001))
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(PureCN)
Loading required package: DNAcopy
Loading required package: VariantAnnotation
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: GenomeInfoDb
Loading required package: stats4
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: SummarizedExperiment
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: Rsamtools
Loading required package: Biostrings
Loading required package: XVector
Attaching package: 'VariantAnnotation'
The following object is masked from 'package:base':
tabulate
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/PureCN/segmentationCBS.Rd_%03d_medium.png", width=480, height=480)
> ### Name: segmentationCBS
> ### Title: CBS segmentation
> ### Aliases: segmentationCBS
>
> ### ** Examples
>
> gatk.normal.file <- system.file("extdata", "example_normal.txt",
+ package="PureCN")
> gatk.tumor.file <- system.file("extdata", "example_tumor.txt",
+ package="PureCN")
> vcf.file <- system.file("extdata", "example_vcf.vcf",
+ package="PureCN")
> gc.gene.file <- system.file("extdata", "example_gc.gene.file.txt",
+ package="PureCN")
>
> # speed-up the runAbsoluteCN by using the stored grid-search.
> # (purecn.example.output$candidates).
> data(purecn.example.output)
>
> # The max.candidate.solutions argument is set to a very low value only to
> # speed-up this example. This is not a good idea for real samples.
> ret <-runAbsoluteCN(gatk.normal.file=gatk.normal.file,
+ gatk.tumor.file=gatk.tumor.file,
+ vcf.file=vcf.file, sampleid='Sample1', gc.gene.file=gc.gene.file,
+ candidates=purecn.example.output$candidates, max.candidate.solutions=2,
+ fun.segmentation=segmentationCBS, args.segmentation=list(alpha=0.001))
Loading GATK coverage files...
Sex of sample: ?
Removing 7 small exons.
Removing 15 low/high GC exons.
Loading VCF...
Assuming LIB-02240e4 is tumor in VCF file.
Found 2331 variants in VCF file.
Removing 0 non heterozygous (in matched normal) germline SNPs.
Removing 62 SNPs with AF < 0.03 or AF >= 0.97 or less than 3 supporting reads or depth < 15.
Found SOMATIC annotation in VCF. Setting somatic prior probabilities for somatic variants to 0.999 or to 1e-04 otherwise.
Segmenting data...
Removing 267 low coverage exons.
Analyzing: Sample1
Setting multi-figure configuration
Call:
segment(x = smoothed.CNA.obj, alpha = alpha, undo.splits = undo.splits,
undo.SD = sdundo, verbose = ifelse(verbose, 1, 0))
ID chrom loc.start loc.end num.mark seg.mean
1 Sample1 1 1216044.5 248722319 934 0.3272
2 Sample1 2 1638036.0 231775198 708 -0.2446
3 Sample1 2 236403412.5 241737117 93 0.2648
4 Sample1 3 11832017.5 149470198 436 0.2969
5 Sample1 3 150264604.0 151542537 18 1.6288
6 Sample1 3 151545662.5 195938114 80 0.3072
7 Sample1 4 843512.0 70146580 133 0.3118
8 Sample1 4 75673305.5 77700146 39 -0.1997
9 Sample1 4 81188156.5 108831608 44 -1.2019
10 Sample1 4 110635592.5 186611721 139 0.2606
11 Sample1 5 442758.0 10761154 38 0.3255
12 Sample1 5 38869183.5 180687408 360 -0.2858
13 Sample1 6 2623865.0 144219759 293 -0.2464
14 Sample1 6 144224235.5 170862274 117 0.3914
15 Sample1 7 938572.5 14028656 56 0.3075
16 Sample1 7 23286512.0 23313764 11 1.7239
17 Sample1 7 26232167.0 156469232 310 -0.2223
18 Sample1 8 6264200.0 145537891 337 -0.2747
19 Sample1 9 214953.0 139440208 371 -0.2226
20 Sample1 10 323391.5 72576624 233 0.2804
21 Sample1 10 72604313.0 72645621 16 -0.2807
22 Sample1 10 72648289.5 75000741 30 0.2522
23 Sample1 10 82300671.5 82403794 7 -1.2827
24 Sample1 10 85982056.5 88768888 12 1.0633
25 Sample1 10 91066426.0 99790218 36 -1.1192
26 Sample1 10 102283640.5 102289566 5 1.2394
27 Sample1 10 103541552.5 121214530 73 -1.2211
28 Sample1 10 124591880.5 134121207 24 0.1328
29 Sample1 11 2291272.0 34378690 106 -0.2981
30 Sample1 11 36614927.0 44081430 15 0.4322
31 Sample1 11 46880700.5 57317514 71 -0.2062
32 Sample1 11 57947383.5 65172438 77 0.2423
33 Sample1 11 65340286.5 66335024 33 -0.3169
34 Sample1 11 66335504.5 71209486 26 -1.2807
35 Sample1 11 71847083.0 82549523 78 0.3824
36 Sample1 11 82550385.5 134134828 151 -0.2000
37 Sample1 12 1740561.0 99126272 373 -1.1729
38 Sample1 12 113537804.0 124428836 212 0.2830
39 Sample1 13 20398996.5 114438189 322 -1.2399
40 Sample1 14 20757846.0 101349088 319 0.2821
41 Sample1 15 27216709.5 99926272 394 0.3090
42 Sample1 16 230533.5 31123514 224 0.2804
43 Sample1 16 56899289.0 56947247 25 0.9000
44 Sample1 16 57507348.5 57722319 20 -0.2426
45 Sample1 16 66918983.0 90038049 183 0.3186
46 Sample1 17 1399145.5 76832320 621 0.3029
47 Sample1 17 77768896.0 80559278 24 -0.2538
48 Sample1 18 5394737.5 71825664 133 -0.2692
49 Sample1 19 1481982.5 57301280 498 0.3127
50 Sample1 20 207959.0 62610776 330 0.2918
51 Sample1 21 11098731.0 47865219 176 0.3147
52 Sample1 22 17443695.0 45996257 148 0.3021
53 Sample1 22 50703417.0 51066096 20 -0.4576
Mean standard deviation of log-ratios: 0.4
Optimizing purity and ploidy. Will take a minute or two...
Local optima: 0.65/1.6, 0.5/2.4, 0.9/2.4, 0.5/2
Testing local optimum at purity 0.65 and total ploidy 1.6.
Fitting SNVs for purity 0.65 and tumor ploidy 1.38.
Analyzing: Sample1
Optimized purity: 0.65
Testing local optimum at purity 0.5 and total ploidy 2.4.
Fitting SNVs for purity 0.48 and tumor ploidy 2.77.
Analyzing: Sample1
Optimized purity: 0.48
Testing local optimum at purity 0.9 and total ploidy 2.4.
Fitting SNVs for purity 0.95 and tumor ploidy 2.38.
Analyzing: Sample1
Optimized purity: 0.95
Testing local optimum at purity 0.5 and total ploidy 2.
Fitting SNVs for purity 0.48 and tumor ploidy 2.77.
Analyzing: Sample1
Optimized purity: 0.48
Remember, posterior probabilities assume a correct SCNA fit.
Warning message:
In runAbsoluteCN(gatk.normal.file = gatk.normal.file, gatk.tumor.file = gatk.tumor.file, :
Too many candidate solutions! Trying optimizing the top candidates.
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> dev.off()
null device
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providing 
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