Nothing
context("Segmentation of univariate signals by dynamic programming")
test_that("pDPA and classical DP yield identical results for univariate signals", {
## load known real copy number regions
affyDat <- loadCnRegionData(dataSet="GSE29172", tumorFraction=1)
## generate a synthetic CN profile
K <- 4
len <- 1e3
sim <- getCopyNumberDataByResampling(len, K, minLength=100, regData=affyDat)
datS <- sim$profile
## run pruned DPA segmentation
resP <- doDynamicProgramming(datS[["c"]], K=2*K)$dpseg
## run classical DP (slower)
res <- pruneByDP(datS[["c"]], K=2*K)
## breakpoints
bkpP <- resP$bkp
bkp <- res$bkpList
expect_equal(length(bkp), length(bkpP))
if (length(bkp) == length(bkpP)) {
for (kk in 1:length(bkp)) {
expect_equal(bkp[[kk]], bkpP[[kk]])
}
}
## Cost matrix
expect_equal(res$V, resP$V)
## Residual squared error
expect_equal(res$rse, resP$rse)
})
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