Last updated: 2018-11-15

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Introduction

library("susieR")
library("DNAcopy")
library("changepoint")
Loading required package: zoo

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

    as.Date, as.Date.numeric
Successfully loaded changepoint package version 2.2.2
 NOTE: Predefined penalty values changed in version 2.2.  Previous penalty values with a postfix 1 i.e. SIC1 are now without i.e. SIC and previous penalties without a postfix i.e. SIC are now with a postfix 0 i.e. SIC0. See NEWS and help files for further details.

Simulated example from DNAcopy

This is the simulated example from ?DNAcopy::segment (note we changed this into a single problem (single chromosome) whereas the example was originally split into two chromosomes).

set.seed(51)
genomdat <- rnorm(500, sd=0.2) +
rep(c(-0.2,0.1,1,-0.5,0.2,-0.5,0.1,-0.2),c(137,87,17,49,29,52,87,42))
chrom <- rep(1,500)
maploc <- c(1:500)
test2 <- DNAcopy::segment(CNA(genomdat, chrom, maploc))
Analyzing: Sample.1 
plot(test2)

genomdat.s = susieR::susie_trendfilter(genomdat,0)
plot_cs = function(s){
  CS = s$sets$cs
  for(i in 1:length(CS)){
    rect(min(CS[[i]]),-5,max(CS[[i]])+1,5,col = rgb(1,0,0,alpha=0.5),border=NA)
  }
}

plot(genomdat,col="gray",ylab="y",xlab="t")
segments(x0=test2$output$loc.start,x1=test2$output$loc.end,y0=test2$output$seg.mean,col="blue",lwd=2)
abline(v=cumsum(c(137,87,17,49,29,52,87))) # changepoint locations
plot_cs(genomdat.s)

Report CSs and compare with true locations

genomdat.s$sets$cs
$L1
[1] 130 131 132 133 134 136 137 138

$L2
[1] 241 242

$L3
[1] 371

$L4
[1] 456 458 459 460

$L5
[1] 223 224 225

$L6
[1] 224

$L7
[1] 319

$L8
[1] 290
cumsum(c(137,87,17,49,29,52,87))
[1] 137 224 241 290 319 371 458

Convergence issue

We set up a simple simulated example with two change points very close together.

set.seed(1)
x = rnorm(100)
x[50:51]=x[50:51]+8
x.s = susie_trendfilter(x,0,L=2,estimate_prior_variance=TRUE)
plot(x,col="gray",ylab="y",xlab="t")
lines(predict(x.s),type="s")

susie_get_objective(x.s)
[1] -181.835

Now try initializing susie at the “truth”

s0 = susie_init_coef(c(49,51),coef_value = c(8,-8),p=100)
x.s2 = susie_trendfilter(x,0,s_init=s0,estimate_prior_variance=TRUE)
plot(x,col="gray",ylab="y",xlab="t")
lines(predict(x.s2),col=2,type="s")

susie_get_objective(x.s2)
[1] -148.2176
plot(x,col="gray",ylab="y",xlab="t")
lines(predict(x.s),type="s")
lines(predict(x.s2),col=2,type="s")

Session information

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: OS X El Capitan 10.11.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] changepoint_2.2.2 zoo_1.8-4         DNAcopy_1.55.0    susieR_0.6.2.0390

loaded via a namespace (and not attached):
 [1] workflowr_1.1.1   Rcpp_1.0.0        lattice_0.20-35  
 [4] digest_0.6.18     rprojroot_1.3-2   R.methodsS3_1.7.1
 [7] grid_3.5.1        backports_1.1.2   magrittr_1.5     
[10] git2r_0.23.0      evaluate_0.12     stringi_1.2.4    
[13] whisker_0.3-2     R.oo_1.22.0       R.utils_2.7.0    
[16] Matrix_1.2-14     rmarkdown_1.10    tools_3.5.1      
[19] stringr_1.3.1     yaml_2.2.0        compiler_3.5.1   
[22] htmltools_0.3.6   knitr_1.20

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