Nothing
tests/testthat/test.oncoSimulIndiv-miscell.R
In OncoSimulR: Forward Genetic Simulation of Cancer Progression with Epistasis
inittime <- Sys.time()
cat(paste("\n Starting oncoSimulIndiv-miscell tests", date(), "\n"))
test_that("sampleEvery must have a value", {
oi <- allFitnessEffects(orderEffects =
c("F > D" = -0.3, "D > F" = 0.4),
noIntGenes = rexp(5, 10),
geneToModule =
c("Root" = "Root",
"F" = "f1, f2, f3",
"D" = "d1, d2") )
expect_error(oncoSimulIndiv(pi, sampleEvery = NA),
"sampleEvery cannot be NULL or NA",
fixed = TRUE)
})
## RNGkind("Mersenne-Twister")
test_that("can start from 1 individual but error if McFL", {
oi <- allFitnessEffects(orderEffects =
c("F > D" = -0.3, "D > F" = 0.4),
noIntGenes = rexp(5, 10),
geneToModule =
c("Root" = "Root",
"F" = "f1, f2, f3",
"D" = "d1, d2") )
expect_output(print(oncoSimulIndiv(oi, initSize = 1,
sampleEvery = 0.03,
keepEvery = 5,
onlyCancer = FALSE)),
"Individual OncoSimul trajectory", fixed = TRUE)
expect_error(oncoSimulIndiv(oi, initSize = 1,
onlyCancer = FALSE, model = "McFL"),
"Using McFarland's model: K cannot be < 1",
fixed = TRUE)
})
test_that("oncoSimulSample and oncoSimulPop require >= 1 indiv", {
pancr <- allFitnessEffects(data.frame(parent = c("Root", rep("KRAS", 4), "SMAD4", "CDNK2A",
"TP53", "TP53", "MLL3"),
child = c("KRAS","SMAD4", "CDNK2A",
"TP53", "MLL3",
rep("PXDN", 3), rep("TGFBR2", 2)),
s = 0.05,
sh = -0.3,
typeDep = "MN"))
expect_error(pS <- oncoSimulSample(0, pancr),
"Nindiv must be >= 1",
fixed = TRUE)
expect_error(pS <- oncoSimulPop(0, pancr),
"Nindiv must be >= 1",
fixed = TRUE)
expect_error(pS <- oncoSimulSample(-3, pancr),
"Nindiv must be >= 1",
fixed = TRUE)
expect_error(pS <- oncoSimulPop(-4, pancr),
"Nindiv must be >= 1",
fixed = TRUE)
})
test_that("numPassengers no effect with fitnessEffects objects", {
oi <- allFitnessEffects(orderEffects =
c("F > D" = -0.3, "D > F" = 0.4),
noIntGenes = rexp(5, 10),
geneToModule =
c("Root" = "Root",
"F" = "f1, f2, f3",
"D" = "d1, d2") )
expect_warning(oi1 <- oncoSimulIndiv(oi,
sampleEvery = 0.03,
keepEvery = 5,
numPassengers = 10),
"Specifying numPassengers", fixed = TRUE)
})
test_that("initSize cannot be less than 1", {
np <- 20
s <- 0.015
spp <- 0.01
nd <- 5
mcf1 <- allFitnessEffects(noIntGenes = rep(spp, np),
drvNames = integer(0))
expect_error(oncoSimulIndiv(mcf1,
initSize = 0),
"initSize < 1", fixed = TRUE)
expect_error(oncoSimulIndiv(mcf1,
initSize = -3),
"initSize < 1", fixed = TRUE)
})
test_that("samplePop with oncoSimulIndiv object", {
oi <- allFitnessEffects(orderEffects =
c("F > D" = -0.3, "D > F" = 0.4),
noIntGenes = rexp(5, 10),
geneToModule =
c("Root" = "Root",
"F" = "f1, f2, f3",
"D" = "d1, d2") )
oiI1 <- oncoSimulIndiv(oi,
sampleEvery = 0.03,
keepEvery = 5,
model = "Exp")
expect_message(out <- samplePop(oiI1),
"Subjects by Genes matrix of 1 subjects and 10 genes.",
fixed = TRUE)
expect_true(ncol(out) == 10)
expect_true(nrow(out) == 1)
})
test_that("McFl warning with small sampleEvery", {
oi <- allFitnessEffects(orderEffects =
c("F > D" = -0.3, "D > F" = 0.4),
noIntGenes = rexp(5, 10),
geneToModule =
c("Root" = "Root",
"F" = "f1, f2, f3",
"D" = "d1, d2") )
expect_warning(oiI1 <- oncoSimulIndiv(oi, model = "McFL",
sampleEvery = 0.06,
keepEvery = 5,
onlyCancer = FALSE),
"With the McFarland model you often want smaller sampleEvery")
})
test_that("mu < 0 error", {
oi <- allFitnessEffects(orderEffects =
c("F > D" = -0.3, "D > F" = 0.4),
noIntGenes = rexp(5, 10),
geneToModule =
c("Root" = "Root",
"F" = "f1, f2, f3",
"D" = "d1, d2") )
expect_error(oiI1 <- oncoSimulIndiv(oi, mu = -1),
"mutation rate (mu) is negative",
fixed = TRUE)
})
test_that("keepEevery and sampleEvery consistency", {
oi <- allFitnessEffects(orderEffects =
c("F > D" = -0.3, "D > F" = 0.4),
noIntGenes = rexp(5, 10),
geneToModule =
c("Root" = "Root",
"F" = "f1, f2, f3",
"D" = "d1, d2") )
expect_warning(oiI1 <- oncoSimulIndiv(oi,
finalTime = 0.5,
keepEvery = 0.25,
onlyCancer = FALSE,
sampleEvery = 0.3),
"setting keepEvery <- sampleEvery",
fixed = TRUE)
})
test_that("using old poset format: error if need param null", {
data(examplePosets)
p701 <- examplePosets[["p701"]]
expect_error(oncoSimulIndiv(p701, s = NULL),
"You must specify all of")
})
test_that("using old poset format: error if need param null", {
data(examplePosets)
p701 <- examplePosets[["p701"]]
expect_error(oncoSimulIndiv(p701, sh = NULL),
"You must specify all of")
})
test_that("using old poset format: error if need param null", {
data(examplePosets)
p701 <- examplePosets[["p701"]]
expect_error(oncoSimulIndiv(p701, numPassengers = NULL),
"You must specify all of")
})
test_that("verbosity options", {
oi <- allFitnessEffects(orderEffects =
c("F > D" = -0.3, "D > F" = 0.4),
noIntGenes = rexp(5, 10),
geneToModule =
c("Root" = "Root",
"F" = "f1, f2, f3",
"D" = "d1, d2") )
expect_output(oncoSimulIndiv(oi, verbosity = 2,
detectionSize = 1e3,
sampleEvery = 0.03,
keepEvery = 2,
finalTime = 3,
onlyCancer = FALSE),
"Total Pop Size = ", fixed = TRUE)
})
test_that("printing oncosimul object", {
oi <- allFitnessEffects(orderEffects =
c("F > D" = -0.3, "D > F" = 0.4),
noIntGenes = rexp(5, 10),
geneToModule =
c("Root" = "Root",
"F" = "f1, f2, f3",
"D" = "d1, d2") )
out <- oncoSimulIndiv(oi,
sampleEvery = 0.03,
keepEvery = 2,
detectionSize = 1e3,
finalTime = 3,
onlyCancer = FALSE)
expect_output(print(out),
"Individual OncoSimul trajectory with call")
})
test_that("printing oncosimul pop object", {
oi <- allFitnessEffects(orderEffects =
c("F > D" = -0.3, "D > F" = 0.4),
noIntGenes = rexp(5, 10),
geneToModule =
c("Root" = "Root",
"F" = "f1, f2, f3",
"D" = "d1, d2") )
out <- oncoSimulPop(4,
oi,
sampleEvery = 0.03,
keepEvery = 2,
finalTime = 3,
detectionSize = 1e3,
onlyCancer = FALSE, mc.cores = 2)
expect_output(print(out),
"Population of OncoSimul trajectories of 4 individuals")
})
test_that("exercising oncoSimulSample, old format", {
data(examplePosets)
p701 <- examplePosets[["p701"]]
expect_message(ofw <- oncoSimulSample(2, p701,
sampleEvery = 0.03,
detectionSize = 1e3,
finalTime = 3,
onlyCancer = FALSE,
showProgress = TRUE),
"Successfully sampled 2 individuals")
expect_message(ofs <- oncoSimulSample(2, p701,
sampleEvery = 0.03,
detectionSize = 1e3,
finalTime = 3,
onlyCancer = FALSE,
typeSample = "single"),
"Successfully sampled 2 individuals")
expect_equal(dim(ofw$popSample), c(2, 7))
expect_equal(dim(ofs$popSample), c(2, 7))
})
test_that("exercising oncoSimulSample, new format", {
pancr <- allFitnessEffects(data.frame(parent = c("Root", rep("KRAS", 4), "SMAD4", "CDNK2A",
"TP53", "TP53", "MLL3"),
child = c("KRAS","SMAD4", "CDNK2A",
"TP53", "MLL3",
rep("PXDN", 3), rep("TGFBR2", 2)),
s = 0.05,
sh = -0.3,
typeDep = "MN"))
expect_message(pS <- oncoSimulSample(2, pancr, sampleEvery = 0.03,
detectionSize = 1e3,
finalTime = 3,
onlyCancer = FALSE),
"Successfully sampled 2 individuals")
expect_message(
pSs <- oncoSimulSample(2,
pancr,
sampleEvery = 0.03,
detectionSize = 1e3,
finalTime = 3,
onlyCancer = FALSE,
typeSample = "single",
showProgress = TRUE),
"Successfully sampled 2 individuals")
expect_equal(dim(pS$popSample), c(2, 7))
expect_equal(dim(pSs$popSample), c(2, 7))
})
test_that("check error unknown timeSample", {
data(examplePosets)
p701 <- examplePosets[["p701"]]
r1 <- oncoSimulIndiv(p701, onlyCancer = TRUE, max.num.tries = 5000)
expect_error(samplePop(r1, timeSample = "uniformo"),
"Unknown timeSample option")
expect_error(samplePop(r1, timeSample = "uni"),
"Unknown timeSample option")
expect_error(samplePop(r1, timeSample = "lasto"),
"Unknown timeSample option")
expect_error(samplePop(r1, timeSample = "whole"),
"Unknown timeSample option")
expect_error(samplePop(r1, timeSample = "single"),
"Unknown timeSample option")
expect_error(samplePop(r1, timeSample = "cucu"),
"Unknown timeSample option")
})
test_that("check error unknown typeSample", {
data(examplePosets)
p701 <- examplePosets[["p701"]]
r1 <- oncoSimulIndiv(p701, onlyCancer = TRUE, max.num.tries = 5000)
expect_error(samplePop(r1, typeSample = "uniformo"),
"Unknown typeSample option")
expect_error(samplePop(r1, typeSample = "uni"),
"Unknown typeSample option")
expect_error(samplePop(r1, typeSample = "lasto"),
"Unknown typeSample option")
expect_error(samplePop(r1, typeSample = "uniform"),
"Unknown typeSample option")
expect_error(samplePop(r1, typeSample = "last"),
"Unknown typeSample option")
expect_error(samplePop(r1, typeSample = "wholo"),
"Unknown typeSample option")
expect_error(samplePop(r1, typeSample = "whola"),
"Unknown typeSample option")
expect_error(samplePop(r1, typeSample = "singlo"),
"Unknown typeSample option")
expect_error(samplePop(r1, typeSample = "cell"),
"Unknown typeSample option")
expect_error(samplePop(r1, typeSample = "cucu"),
"Unknown typeSample option")
})
test_that("oncosimul sample without drivers", {
np <- 20
s <- 0.015
spp <- 0.01
nd <- 5
mcf1 <- allFitnessEffects(noIntGenes = rep(spp, np),
drvNames = integer(0))
mcf2 <- allFitnessEffects(noIntGenes = c(rep(s, nd), rep(spp, np)),
drvNames = character(0))
expect_message(o1 <- oncoSimulSample(2, mcf1, sampleEvery = 0.03,
detectionSize = 1e3,
finalTime = 3,
onlyCancer = FALSE),
"Successfully sampled 2 individuals")
expect_message(o2 <- oncoSimulSample(5, mcf2, sampleEvery = 0.03,
detectionSize = 1e3,
finalTime = 3,
onlyCancer = FALSE),
"Successfully sampled 5 individuals")
})
test_that("samplePop, a few examples with time last and whole pop", {
## Why am I testing this? No particular reason. But once I got
## confused and thought this function, which is the basis of
## oncoSimulSample, had a bug. The bug was in my understanding.
initSize <- 10 ## as it is passed below, in the structure; the rest
## for the same reason
K <- 1
mu <- 1e-6
sampleEvery <- 0.1
ni <- c(3, 1, rep(0, 5))
names(ni) <- c("a", "b", paste0("n", seq.int(5)))
fe <- allFitnessEffects(noIntGenes = ni)
fp <- fe
numPassengers <- 0
seed <- NULL
verbosity <- 0
initMutant <- NULL
finalTime <- 3.0
max.memory <- 2000
mutationPropGrowth <- TRUE
max.wall.time.total <- 600
possibleAttempts <- max.num.tries.total <- 500 * 10
onlyCancer <- FALSE
keepPhylog <- FALSE
s1 <- structure(list(structure(list(pops.by.time = structure(c(3, 62925,
2, 4, 1, 57), .Dim = c(1L, 6L)), NumClones = 5, TotalPopSize = 62989,
Genotypes = structure(c(1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L), .Dim = c(7L,
5L)), GenotypesWDistinctOrderEff = list(1L, 1:2, c(1L, 5L
), c(1L, 6L), c(1L, 7L)), GenotypesLabels = c("a", "a, b",
"a, n3", "a, n4", "a, n5"), MaxNumDrivers = 0L, MaxDriversLast = 0L,
NumDriversLargestPop = 0L, LargestClone = 62925, PropLargestPopLast = 0.998983949578498,
FinalTime = 3, NumIter = 34L, HittedWallTime = FALSE, HittedMaxTries = FALSE,
TotalPresentDrivers = 0L, CountByDriver = integer(0), OccurringDrivers = "",
PerSampleStats = structure(c(62989, 62925, 0.998983949578498,
0, 0), .Dim = c(1L, 5L)), other = structure(list(attemptsUsed = 1L,
errorMF = -99, errorMF_size = 0, minDMratio = 25000,
minBMratio = 166666.666666667, PhylogDF = structure(list(
parent = structure(integer(0), .Label = character(0), class = "factor"),
child = structure(integer(0), .Label = character(0), class = "factor"),
time = numeric(0)), .Names = c("parent", "child",
"time"), row.names = integer(0), class = "data.frame"),
UnrecoverExcept = FALSE), .Names = c("attemptsUsed",
"errorMF", "errorMF_size", "minDMratio", "minBMratio",
"PhylogDF", "UnrecoverExcept")), Drivers = integer(0),
geneNames = c("a", "b", "n1", "n2", "n3", "n4", "n5")), .Names = c("pops.by.time",
"NumClones", "TotalPopSize", "Genotypes", "GenotypesWDistinctOrderEff",
"GenotypesLabels", "MaxNumDrivers", "MaxDriversLast", "NumDriversLargestPop",
"LargestClone", "PropLargestPopLast", "FinalTime", "NumIter",
"HittedWallTime", "HittedMaxTries", "TotalPresentDrivers", "CountByDriver",
"OccurringDrivers", "PerSampleStats", "other", "Drivers", "geneNames"
), class = c("oncosimul", "oncosimul2"), call = oncoSimulIndiv(fp = fp,
model = "Exp", numPassengers = numPassengers, mu = mu, detectionSize = 1e9, detectionDrivers = 99,
sampleEvery = sampleEvery, initSize = initSize, s = s, sh = sh,
K = K, keepEvery = -9, minDetectDrvCloneSz = "auto", extraTime = 0,
finalTime = finalTime, onlyCancer = onlyCancer, keepPhylog = keepPhylog,
mutationPropGrowth = mutationPropGrowth, max.memory = max.memory,
max.wall.time = max.wall.time.total, max.num.tries = possibleAttempts,
errorHitWallTime = TRUE, errorHitMaxTries = TRUE, verbosity = verbosity,
initMutant = initMutant, seed = seed)), structure(list(pops.by.time = structure(c(3,
16000), .Dim = 1:2), NumClones = 1, TotalPopSize = 16000, Genotypes = structure(c(1L,
0L, 0L, 0L, 0L, 0L, 0L), .Dim = c(7L, 1L)), GenotypesWDistinctOrderEff = list(
1L), GenotypesLabels = "a", MaxNumDrivers = 0L, MaxDriversLast = 0L,
NumDriversLargestPop = 0L, LargestClone = 16000, PropLargestPopLast = 1,
FinalTime = 3, NumIter = 30L, HittedWallTime = FALSE, HittedMaxTries = FALSE,
TotalPresentDrivers = 0L, CountByDriver = integer(0), OccurringDrivers = "",
PerSampleStats = structure(c(16000, 16000, 1, 0, 0), .Dim = c(1L,
5L)), other = structure(list(attemptsUsed = 1L, errorMF = -99,
errorMF_size = 0, minDMratio = 41666.6666666667,
minBMratio = 166666.666666667, PhylogDF = structure(list(
parent = structure(integer(0), .Label = character(0), class = "factor"),
child = structure(integer(0), .Label = character(0), class = "factor"),
time = numeric(0)), .Names = c("parent", "child",
"time"), row.names = integer(0), class = "data.frame"),
UnrecoverExcept = FALSE), .Names = c("attemptsUsed",
"errorMF", "errorMF_size", "minDMratio", "minBMratio",
"PhylogDF", "UnrecoverExcept")), Drivers = integer(0),
geneNames = c("a", "b", "n1", "n2", "n3", "n4", "n5")), .Names = c("pops.by.time",
"NumClones", "TotalPopSize", "Genotypes", "GenotypesWDistinctOrderEff",
"GenotypesLabels", "MaxNumDrivers", "MaxDriversLast", "NumDriversLargestPop",
"LargestClone", "PropLargestPopLast", "FinalTime", "NumIter",
"HittedWallTime", "HittedMaxTries", "TotalPresentDrivers", "CountByDriver",
"OccurringDrivers", "PerSampleStats", "other", "Drivers", "geneNames"
), class = c("oncosimul", "oncosimul2"), call = oncoSimulIndiv(fp = fp,
model = "Exp", numPassengers = numPassengers, mu = mu, detectionSize = 1e9, detectionDrivers = 99,
sampleEvery = sampleEvery, initSize = initSize, s = s, sh = sh,
K = K, keepEvery = -9, minDetectDrvCloneSz = "auto", extraTime = 0,
finalTime = finalTime, onlyCancer = onlyCancer, keepPhylog = keepPhylog,
mutationPropGrowth = mutationPropGrowth, max.memory = max.memory,
max.wall.time = max.wall.time.total, max.num.tries = possibleAttempts,
errorHitWallTime = TRUE, errorHitMaxTries = TRUE, verbosity = verbosity,
initMutant = initMutant, seed = seed)), structure(list(pops.by.time = structure(c(3,
65810, 4), .Dim = c(1L, 3L)), NumClones = 2, TotalPopSize = 65814,
Genotypes = structure(c(1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 1L, 0L, 0L), .Dim = c(7L, 2L)), GenotypesWDistinctOrderEff = list(
1L, c(1L, 5L)), GenotypesLabels = c("a", "a, n3"), MaxNumDrivers = 0L,
MaxDriversLast = 0L, NumDriversLargestPop = 0L, LargestClone = 65810,
PropLargestPopLast = 0.999939222657793, FinalTime = 3, NumIter = 31L,
HittedWallTime = FALSE, HittedMaxTries = FALSE, TotalPresentDrivers = 0L,
CountByDriver = integer(0), OccurringDrivers = "", PerSampleStats = structure(c(65814,
65810, 0.999939222657793, 0, 0), .Dim = c(1L, 5L)), other = structure(list(
attemptsUsed = 1L, errorMF = -99, errorMF_size = 0,
minDMratio = 41666.6666666667, minBMratio = 166666.666666667,
PhylogDF = structure(list(parent = structure(integer(0), .Label = character(0), class = "factor"),
child = structure(integer(0), .Label = character(0), class = "factor"),
time = numeric(0)), .Names = c("parent", "child",
"time"), row.names = integer(0), class = "data.frame"),
UnrecoverExcept = FALSE), .Names = c("attemptsUsed",
"errorMF", "errorMF_size", "minDMratio", "minBMratio",
"PhylogDF", "UnrecoverExcept")), Drivers = integer(0),
geneNames = c("a", "b", "n1", "n2", "n3", "n4", "n5")), .Names = c("pops.by.time",
"NumClones", "TotalPopSize", "Genotypes", "GenotypesWDistinctOrderEff",
"GenotypesLabels", "MaxNumDrivers", "MaxDriversLast", "NumDriversLargestPop",
"LargestClone", "PropLargestPopLast", "FinalTime", "NumIter",
"HittedWallTime", "HittedMaxTries", "TotalPresentDrivers", "CountByDriver",
"OccurringDrivers", "PerSampleStats", "other", "Drivers", "geneNames"
), class = c("oncosimul", "oncosimul2"), call = oncoSimulIndiv(fp = fp,
model = "Exp", numPassengers = numPassengers, mu = mu, detectionSize = 1e9, detectionDrivers = 99,
sampleEvery = sampleEvery, initSize = initSize, s = s, sh = sh,
K = K, keepEvery = -9, minDetectDrvCloneSz = "auto", extraTime = 0,
finalTime = finalTime, onlyCancer = onlyCancer, keepPhylog = keepPhylog,
mutationPropGrowth = mutationPropGrowth, max.memory = max.memory,
max.wall.time = max.wall.time.total, max.num.tries = possibleAttempts,
errorHitWallTime = TRUE, errorHitMaxTries = TRUE, verbosity = verbosity,
initMutant = initMutant, seed = seed)), structure(list(pops.by.time = structure(c(3,
51024), .Dim = 1:2), NumClones = 1, TotalPopSize = 51024, Genotypes = structure(c(1L,
0L, 0L, 0L, 0L, 0L, 0L), .Dim = c(7L, 1L)), GenotypesWDistinctOrderEff = list(
1L), GenotypesLabels = "a", MaxNumDrivers = 0L, MaxDriversLast = 0L,
NumDriversLargestPop = 0L, LargestClone = 51024, PropLargestPopLast = 1,
FinalTime = 3, NumIter = 30L, HittedWallTime = FALSE, HittedMaxTries = FALSE,
TotalPresentDrivers = 0L, CountByDriver = integer(0), OccurringDrivers = "",
PerSampleStats = structure(c(51024, 51024, 1, 0, 0), .Dim = c(1L,
5L)), other = structure(list(attemptsUsed = 1L, errorMF = -99,
errorMF_size = 0, minDMratio = 41666.6666666667,
minBMratio = 166666.666666667, PhylogDF = structure(list(
parent = structure(integer(0), .Label = character(0), class = "factor"),
child = structure(integer(0), .Label = character(0), class = "factor"),
time = numeric(0)), .Names = c("parent", "child",
"time"), row.names = integer(0), class = "data.frame"),
UnrecoverExcept = FALSE), .Names = c("attemptsUsed",
"errorMF", "errorMF_size", "minDMratio", "minBMratio",
"PhylogDF", "UnrecoverExcept")), Drivers = integer(0),
geneNames = c("a", "b", "n1", "n2", "n3", "n4", "n5")), .Names = c("pops.by.time",
"NumClones", "TotalPopSize", "Genotypes", "GenotypesWDistinctOrderEff",
"GenotypesLabels", "MaxNumDrivers", "MaxDriversLast", "NumDriversLargestPop",
"LargestClone", "PropLargestPopLast", "FinalTime", "NumIter",
"HittedWallTime", "HittedMaxTries", "TotalPresentDrivers", "CountByDriver",
"OccurringDrivers", "PerSampleStats", "other", "Drivers", "geneNames"
), class = c("oncosimul", "oncosimul2"), call = oncoSimulIndiv(fp = fp,
model = "Exp", numPassengers = numPassengers, mu = mu, detectionSize = 1e9, detectionDrivers = 99,
sampleEvery = sampleEvery, initSize = initSize, s = s, sh = sh,
K = K, keepEvery = -9, minDetectDrvCloneSz = "auto", extraTime = 0,
finalTime = finalTime, onlyCancer = onlyCancer, keepPhylog = keepPhylog,
mutationPropGrowth = mutationPropGrowth, max.memory = max.memory,
max.wall.time = max.wall.time.total, max.num.tries = possibleAttempts,
errorHitWallTime = TRUE, errorHitMaxTries = TRUE, verbosity = verbosity,
initMutant = initMutant, seed = seed)), structure(list(pops.by.time = structure(c(3,
64375, 1), .Dim = c(1L, 3L)), NumClones = 2, TotalPopSize = 64376,
Genotypes = structure(c(1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
1L, 0L, 0L, 0L, 0L), .Dim = c(7L, 2L)), GenotypesWDistinctOrderEff = list(
1L, c(1L, 3L)), GenotypesLabels = c("a", "a, n1"), MaxNumDrivers = 0L,
MaxDriversLast = 0L, NumDriversLargestPop = 0L, LargestClone = 64375,
PropLargestPopLast = 0.999984466260718, FinalTime = 3, NumIter = 31L,
HittedWallTime = FALSE, HittedMaxTries = FALSE, TotalPresentDrivers = 0L,
CountByDriver = integer(0), OccurringDrivers = "", PerSampleStats = structure(c(64376,
64375, 0.999984466260718, 0, 0), .Dim = c(1L, 5L)), other = structure(list(
attemptsUsed = 1L, errorMF = -99, errorMF_size = 0,
minDMratio = 41666.6666666667, minBMratio = 166666.666666667,
PhylogDF = structure(list(parent = structure(integer(0), .Label = character(0), class = "factor"),
child = structure(integer(0), .Label = character(0), class = "factor"),
time = numeric(0)), .Names = c("parent", "child",
"time"), row.names = integer(0), class = "data.frame"),
UnrecoverExcept = FALSE), .Names = c("attemptsUsed",
"errorMF", "errorMF_size", "minDMratio", "minBMratio",
"PhylogDF", "UnrecoverExcept")), Drivers = integer(0),
geneNames = c("a", "b", "n1", "n2", "n3", "n4", "n5")), .Names = c("pops.by.time",
"NumClones", "TotalPopSize", "Genotypes", "GenotypesWDistinctOrderEff",
"GenotypesLabels", "MaxNumDrivers", "MaxDriversLast", "NumDriversLargestPop",
"LargestClone", "PropLargestPopLast", "FinalTime", "NumIter",
"HittedWallTime", "HittedMaxTries", "TotalPresentDrivers", "CountByDriver",
"OccurringDrivers", "PerSampleStats", "other", "Drivers", "geneNames"
), class = c("oncosimul", "oncosimul2"), call = oncoSimulIndiv(fp = fp,
model = "Exp", numPassengers = numPassengers, mu = mu, detectionSize = 1e9, detectionDrivers = 99,
sampleEvery = sampleEvery, initSize = initSize, s = s, sh = sh,
K = K, keepEvery = -9, minDetectDrvCloneSz = "auto", extraTime = 0,
finalTime = finalTime, onlyCancer = onlyCancer, keepPhylog = keepPhylog,
mutationPropGrowth = mutationPropGrowth, max.memory = max.memory,
max.wall.time = max.wall.time.total, max.num.tries = possibleAttempts,
errorHitWallTime = TRUE, errorHitMaxTries = TRUE, verbosity = verbosity,
initMutant = initMutant, seed = seed))), class = "oncosimulpop")
cn <- c("a", "b", paste0("n", 1:5))
m1 <- matrix(c(rep(1, 5), rep(0, 30)),
ncol = 7,
dimnames = list(c(NULL), cn))
expect_identical( samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 0.5),
m1
)
m1 <- matrix(1, nrow = 5,
ncol = 7,
dimnames = list(c(NULL), cn))
expect_identical( samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 0),
m1
)
## from s1[[5]], the two possible differentiating thresholds, right on the
## equality
m1 <- matrix(c(rep(1, 5), rep(0, 30)),
ncol = 7,
dimnames = list(c(NULL), cn))
m1[rbind(c(1, 2), c(1, 5), c(1, 6), c(1, 7), c(3, 5), c(5, 3))] <- 1
expect_identical(samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 1/64376),
m1)
m1 <- matrix(c(rep(1, 5), rep(0, 30)),
ncol = 7,
dimnames = list(c(NULL), cn))
m1[rbind(c(1, 2), c(1, 5), c(1, 6), c(1, 7), c(3, 5))] <- 1
expect_identical(samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 1/64375),
m1)
## from s1[[3]], the two possible differentiating thresholds, right on the
## equality; also affects first population
m1 <- matrix(c(rep(1, 5), rep(0, 30)),
ncol = 7,
dimnames = list(c(NULL), cn))
m1[rbind( c(1, 5), c(1, 7), c(3, 5))] <- 1
expect_identical(samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 4/65814),
m1)
m1 <- matrix(c(rep(1, 5), rep(0, 30)),
ncol = 7,
dimnames = list(c(NULL), cn))
m1[rbind(c(1, 7))] <- 1
expect_identical(samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 5/65814),
m1)
## from s1[[1]], the two possible differentiating thresholds, right on the
## equality; also affects first population
m1 <- matrix(c(rep(1, 5), rep(0, 30)),
ncol = 7,
dimnames = list(c(NULL), cn))
m1[rbind(c(1, 2), c(1, 5), c(1, 6), c(1, 7), c(3, 5))] <- 1
expect_identical(samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 1/62989),
m1)
m1 <- matrix(c(rep(1, 5), rep(0, 30)),
ncol = 7,
dimnames = list(c(NULL), cn))
m1[rbind(c(1, 2), c(1, 5), c(1, 7), c(3, 5))] <- 1
expect_identical(samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 2/62989),
m1)
m1 <- matrix(c(rep(1, 5), rep(0, 30)),
ncol = 7,
dimnames = list(c(NULL), cn))
m1[rbind(c(1, 5), c(1, 7))] <- 1
expect_identical(samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 4/62989),
m1)
m1 <- matrix(c(rep(1, 5), rep(0, 30)),
ncol = 7,
dimnames = list(c(NULL), cn))
m1[rbind(c(1, 7))] <- 1
expect_identical(samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 57/62989),
m1)
m1 <- matrix(c(rep(1, 5), rep(0, 30)),
ncol = 7,
dimnames = list(c(NULL), cn))
expect_identical(samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 58/62989),
m1)
})
test_that("exercising oncoSimulIndiv, new format, extra time", {
pancr <- allFitnessEffects(data.frame(parent = c("Root", rep("KRAS", 4), "SMAD4", "CDNK2A",
"TP53", "TP53", "MLL3"),
child = c("KRAS","SMAD4", "CDNK2A",
"TP53", "MLL3",
rep("PXDN", 3), rep("TGFBR2", 2)),
s = 0.05,
sh = -0.3,
typeDep = "MN"))
expect_silent(
pSs <- oncoSimulIndiv(pancr,
sampleEvery = 0.03,
detectionSize = 1e3,
finalTime = 6,
extraTime = 5,
onlyCancer = FALSE))
expect_output(print(pSs), "Individual OncoSimul trajectory",
fixed = TRUE)
})
test_that("exercising oncoSimulIndiv, hit max ram", {
p1 <- allFitnessEffects(noIntGenes = rep(.1, 10))
expect_output(pSs <- oncoSimulIndiv(p1,
initSize = 1e4,
sampleEvery = 3,
detectionSize = 1e5,
finalTime = 1000,
extraTime = 5,
onlyCancer = FALSE,
max.memory = .01),
"Return outNS object > maxram",
fixed = TRUE)
})
test_that("exercising oncoSimulIndiv, verbosity", {
ii <- rep(.1, 20)
names(ii) <- letters[1:20]
p1 <- allFitnessEffects(noIntGenes = ii)
expect_output(pSs <- oncoSimulIndiv(p1,
initMutant = "a",
model = "McFL",
initSize = 1e2,
sampleEvery = 1,
detectionSize = 1e10,
finalTime = 2000,
extraTime = 5,
verbosity = 6,
onlyCancer = FALSE),
"Looping through", fixed = TRUE)
## This is too much: can take a minute.
## ii <- rep(.01, 1000)
## names(ii) <- paste0("n", 1:1000)
## p1 <- allFitnessEffects(noIntGenes = ii)
## expect_output(pSs <- oncoSimulIndiv(p1,
## initMutant = "n1",
## model = "Exp",
## initSize = 1e6,
## sampleEvery = 2,
## detectionSize = 1e7,
## finalTime = 50,
## extraTime = 5,
## verbosity = 2,
## onlyCancer = FALSE),
## "Looping through", fixed = TRUE)
})
test_that("oncoSimulIndiv miscell C++ warnings", {
pancri <- allFitnessEffects(
data.frame(parent = c("Root", rep("KRAS", 4), "SMAD4", "CDNK2A",
"TP53", "TP53", "MLL3"),
child = c("KRAS","SMAD4", "CDNK2A",
"TP53", "MLL3",
rep("PXDN", 3), rep("TGFBR2", 2)),
s = Inf,
sh = Inf,
typeDep = "MN"))
## This are messages in R, not R warnings, as from Rcpp::Rcout
expect_output(oncoSimulIndiv(pancri,
initSize = 1,
keepEvery = 5,
onlyCancer = FALSE),
"at least one sh is positive infinite",
fixed = TRUE)
expect_output(oncoSimulIndiv(pancri,
initSize = 1,
keepEvery = 5,
onlyCancer = FALSE),
"at least one s is infinite",
fixed = TRUE)
})
test_that("using old poset format: Bozic and sh < 0", {
data(examplePosets)
p701 <- examplePosets[["p701"]]
expect_silent(oncoSimulIndiv(p701, sh = -0.3,
model = "Bozic",
onlyCancer = FALSE))
})
test_that("using old poset format, extra time", {
data(examplePosets)
p701 <- examplePosets[["p701"]]
expect_silent(oncoSimulIndiv(p701, sh = 0.3,
initSize = 1000,
detectionSize = 1100,
model = "McFL",
finalTime = 1000,
extraTime = 3.17,
onlyCancer = FALSE))
expect_silent(oncoSimulIndiv(p701, sh = 0,
model = "Exp",
initSize = 1e4,
detectionSize = 1e6,
extraTime = 10,
onlyCancer = FALSE))
expect_silent(oncoSimulIndiv(p701, sh = -0.01,
model = "Bozic",
initSize = 1e4,
detectionSize = 1e6,
extraTime = 10,
onlyCancer = FALSE))
})
test_that("using old poset format, no initMutant", {
data(examplePosets)
p701 <- examplePosets[["p701"]]
expect_warning(p1 <- oncoSimulIndiv(p701, sh = 0.3,
initSize = 1000,
detectionSize = 1100,
model = "Exp",
finalTime = 1000,
extraTime = 3.17,
initMutant = 2,
onlyCancer = FALSE),
"With the old poset format you can no longer use initMutant",
fixed = TRUE)
expect_warning(p1 <- oncoSimulIndiv(p701, sh = 0.3,
initSize = 1000,
detectionSize = 1100,
model = "Exp",
finalTime = 1000,
extraTime = 3.17,
initMutant = c(2, 5),
onlyCancer = FALSE),
"With the old poset format you can no longer use initMutant",
fixed = TRUE)
})
test_that("using old poset format, exercise verbosity", {
data(examplePosets)
p701 <- examplePosets[["p701"]]
expect_output(oncoSimulIndiv(p701, sh = 0,
initSize = 10000,
detectionSize = 30000,
model = "Exp",
finalTime = 2000,
extraTime = 3.17,
verbosity = 10,
onlyCancer = FALSE),
"Looping", fixed = TRUE)
})
test_that("exercising verbosity, new format", {
gg <- rep(0.1, 20)
names(gg) <- letters[1:20]
ii <- allFitnessEffects(noIntGenes = gg)
expect_output(
pSs <- oncoSimulIndiv(ii,
sampleEvery = .5,
initSize = 1e4,
detectionSize = 5e5,
finalTime = 1000,
extraTime = 5,
keepEvery = 50,
verbosity = 10,
onlyCancer = FALSE),
"Looping",
fixed = TRUE)
})
test_that("old format: at most 64 genes", {
p1 <- cbind(1L, 2L)
expect_error(p1 <- oncoSimulIndiv(p1,
numPassengers = 66,
sh = 0,
initSize = 1e5,
sampleEvery = 0.02,
detectionSize = 1e9,
model = "Exp",
finalTime = 2000,
extraTime = 3.17,
onlyCancer = FALSE,
seed = NULL),
"Largest possible number of genes",
fixed = TRUE)
})
test_that("samplePop deals with failures in simuls", {
fe <- allFitnessEffects(noIntGenes = c(-0.1, -0.2, -0.3))
uu <- oncoSimulIndiv(fe, max.wall.time = 0.2, max.num.tries = 5)
uup <- oncoSimulPop(4, fe, max.wall.time = 0.2, max.num.tries = 5,
mc.cores = 2)
expect_warning(uus <- samplePop(uu),
"It looks like this simulation never completed",
fixed = TRUE)
expect_warning(uups <- samplePop(uup),
"It looks like this simulation never completed",
fixed = TRUE)
## And it works when only some fail
fe2 <- allFitnessEffects(noIntGenes = c(0.1, 0.2, 0.3))
uu2 <- oncoSimulIndiv(fe2)
u3 <- list(uu, uu2)
class(u3) <- "oncosimulpop"
expect_warning(uu3ps <- samplePop(u3),
"It looks like this simulation never completed",
fixed = TRUE)
})
test_that("summary.oncosimulepop deals with failures in simuls", {
fe <- allFitnessEffects(noIntGenes = c(-0.1, -0.2, -0.3))
uup <- oncoSimulPop(4, fe, max.wall.time = 0.2, max.num.tries = 5, mc.cores = 2)
expect_warning(uus <- summary(uup),
"All simulations failed",
fixed = TRUE)
## And it works when only some fail
fe2 <- allFitnessEffects(noIntGenes = c(0.1, 0.2, 0.3))
uu2 <- oncoSimulPop(2, fe2, mc.cores = 2)
uu <- oncoSimulPop(2, fe, max.wall.time = 0.2, max.num.tries = 5, mc.cores = 2)
u3 <- c(uu, uu2)
class(u3) <- "oncosimulpop"
expect_warning(uu3ps <- summary(u3),
"Some simulations seem to have failed",
fixed = TRUE)
})
test_that("AND_DrvProbExit warnings and errors work" , {
fe <- allFitnessEffects(noIntGenes = c("A" = 0.1, "B" = 0.2, "C" = 0.3),
drvNames = c("A", "B", "C"))
expect_warning(u <- oncoSimulIndiv(fe, detectionDrivers = 1,
detectionProb = "default",
AND_DrvProbExit = TRUE),
"With AND_DrvProbExit = TRUE, detectionSize is ignored",
fixed = TRUE)
expect_error(u <- oncoSimulIndiv(fe, detectionDrivers = NA,
detectionProb = "default",
AND_DrvProbExit = TRUE),
"AND_DrvProbExit is TRUE: both of detectionProb",
fixed = TRUE)
data(examplePosets)
p701 <- examplePosets[["p701"]]
expect_error(u <- oncoSimulIndiv(p701, detectionDrivers = 1,
detectionProb = "default",
detectionSize = NA,
AND_DrvProbExit = TRUE),
"The AND_DrvProbExit = TRUE setting is invalid with the old poset",
fixed = TRUE)
}
)
test_that("AND_DrvProbExit exercising and test it works" , {
## RNGkind("Mersenne-Twister")
## set.seed(1)
fe <- allFitnessEffects(noIntGenes = c("A" = 0.1, "B" = 0.2, "C" = 0.3),
drvNames = c("A", "B"))
## set.seed(1)
u0 <- oncoSimulIndiv(fe, detectionDrivers = 1,
detectionProb = "default",
AND_DrvProbExit = FALSE,
detectionSize = NA)
## set.seed(1)
u <- oncoSimulIndiv(fe, detectionDrivers = 1,
detectionProb = "default",
AND_DrvProbExit = TRUE,
detectionSize = NA)
expect_true(u$TotalPresentDrivers >= 1)
## set.seed(1)
u2 <- oncoSimulIndiv(fe, detectionDrivers = 1,
detectionProb = "default",
AND_DrvProbExit = TRUE,
minDetectDrvCloneSz = 500,
detectionSize = NA)
expect_true(u2$TotalPresentDrivers >= 1)
## set.seed(2)
## set.seed(2)
## m00 <- oncoSimulIndiv(fe, detectionDrivers = NA, model = "McFL",
## detectionProb = "default",
## AND_DrvProbExit = FALSE,
## detectionSize = NA,
## initMutant = "C")
## set.seed(2)
m <- oncoSimulIndiv(fe, detectionDrivers = 1, model = "McFL",
detectionProb = "default",
AND_DrvProbExit = TRUE,
detectionSize = NA)
expect_true(m$TotalPresentDrivers >= 1)
## set.seed(2)
m1 <- oncoSimulIndiv(fe, detectionDrivers = 1, model = "McFL",
detectionProb = "default",
AND_DrvProbExit = TRUE,
minDetectDrvCloneSz = 10,
detectionSize = NA)
expect_true(m1$TotalPresentDrivers >= 1)
m1p <- m1$pops.by.time[, -c(1, 2), drop = FALSE]
expect_true(sum(m1p[nrow(m1p), , drop = FALSE]) >= 10)
## set.seed(2)
m2 <- oncoSimulIndiv(fe, detectionDrivers = 1, model = "McFL",
detectionProb = "default",
AND_DrvProbExit = TRUE,
minDetectDrvCloneSz = 100,
detectionSize = NA)
expect_true(m2$TotalPresentDrivers >= 1)
m2p <- m2$pops.by.time[, -c(1, 2), drop = FALSE]
expect_true(sum(m2p[nrow(m2p), , drop = FALSE]) >= 100)
## set.seed(2)
## slow
## m3 <- oncoSimulIndiv(fe, detectionDrivers = 1, model = "McFL",
## detectionProb = "default",
## AND_DrvProbExit = TRUE,
## minDetectDrvCloneSz = 1200,
## detectionSize = NA)
m3 <- oncoSimulIndiv(fe, detectionDrivers = 1, model = "McFL",
detectionProb = "default",
extraTime = 3,
AND_DrvProbExit = TRUE,
minDetectDrvCloneSz = 100,
detectionSize = NA)
expect_true(m3$TotalPresentDrivers >= 1)
m3p <- m3$pops.by.time[, -c(1, 2), drop = FALSE]
expect_true(sum(m3p[nrow(m3p), , drop = FALSE]) >= 100)
m3 <- oncoSimulIndiv(fe, detectionDrivers = 1, model = "McFL",
detectionProb = "default",
extraTime = 10,
AND_DrvProbExit = TRUE,
minDetectDrvCloneSz = 100,
detectionSize = NA)
expect_true(m3$TotalPresentDrivers >= 1)
m3p <- m3$pops.by.time[, -c(1, 2), drop = FALSE]
expect_true(sum(m3p[nrow(m3p), , drop = FALSE]) >= 100)
}
)
test_that("exercising oncoSimulIndiv, max size warning", {
p1 <- allFitnessEffects(noIntGenes = rep(.1, 10))
expect_output(oncoSimulIndiv(p1, initSize = 1.5e15, verbosity = 1,
onlyCancer = FALSE, mu= 1e-7))
data(examplePosets)
p701 <- examplePosets[["p701"]]
expect_output(oncoSimulIndiv(p701, initSize = 4.1e15, verbosity = 1,
onlyCancer = FALSE, mu= 1e-7))
})
cat(paste("\n Ending oncoSimulIndiv-miscell tests", date(), "\n"))
cat(paste(" Took ", round(difftime(Sys.time(), inittime, units = "secs"), 2), "\n\n"))
rm(inittime)
Try the OncoSimulR package in your browser
Any scripts or data that you put into this service are public.
OncoSimulR documentation built on Nov. 8, 2020, 8:31 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
inittime <- Sys.time()
cat(paste("\n Starting oncoSimulIndiv-miscell tests", date(), "\n"))
test_that("sampleEvery must have a value", {
oi <- allFitnessEffects(orderEffects =
c("F > D" = -0.3, "D > F" = 0.4),
noIntGenes = rexp(5, 10),
geneToModule =
c("Root" = "Root",
"F" = "f1, f2, f3",
"D" = "d1, d2") )
expect_error(oncoSimulIndiv(pi, sampleEvery = NA),
"sampleEvery cannot be NULL or NA",
fixed = TRUE)
})
## RNGkind("Mersenne-Twister")
test_that("can start from 1 individual but error if McFL", {
oi <- allFitnessEffects(orderEffects =
c("F > D" = -0.3, "D > F" = 0.4),
noIntGenes = rexp(5, 10),
geneToModule =
c("Root" = "Root",
"F" = "f1, f2, f3",
"D" = "d1, d2") )
expect_output(print(oncoSimulIndiv(oi, initSize = 1,
sampleEvery = 0.03,
keepEvery = 5,
onlyCancer = FALSE)),
"Individual OncoSimul trajectory", fixed = TRUE)
expect_error(oncoSimulIndiv(oi, initSize = 1,
onlyCancer = FALSE, model = "McFL"),
"Using McFarland's model: K cannot be < 1",
fixed = TRUE)
})
test_that("oncoSimulSample and oncoSimulPop require >= 1 indiv", {
pancr <- allFitnessEffects(data.frame(parent = c("Root", rep("KRAS", 4), "SMAD4", "CDNK2A",
"TP53", "TP53", "MLL3"),
child = c("KRAS","SMAD4", "CDNK2A",
"TP53", "MLL3",
rep("PXDN", 3), rep("TGFBR2", 2)),
s = 0.05,
sh = -0.3,
typeDep = "MN"))
expect_error(pS <- oncoSimulSample(0, pancr),
"Nindiv must be >= 1",
fixed = TRUE)
expect_error(pS <- oncoSimulPop(0, pancr),
"Nindiv must be >= 1",
fixed = TRUE)
expect_error(pS <- oncoSimulSample(-3, pancr),
"Nindiv must be >= 1",
fixed = TRUE)
expect_error(pS <- oncoSimulPop(-4, pancr),
"Nindiv must be >= 1",
fixed = TRUE)
})
test_that("numPassengers no effect with fitnessEffects objects", {
oi <- allFitnessEffects(orderEffects =
c("F > D" = -0.3, "D > F" = 0.4),
noIntGenes = rexp(5, 10),
geneToModule =
c("Root" = "Root",
"F" = "f1, f2, f3",
"D" = "d1, d2") )
expect_warning(oi1 <- oncoSimulIndiv(oi,
sampleEvery = 0.03,
keepEvery = 5,
numPassengers = 10),
"Specifying numPassengers", fixed = TRUE)
})
test_that("initSize cannot be less than 1", {
np <- 20
s <- 0.015
spp <- 0.01
nd <- 5
mcf1 <- allFitnessEffects(noIntGenes = rep(spp, np),
drvNames = integer(0))
expect_error(oncoSimulIndiv(mcf1,
initSize = 0),
"initSize < 1", fixed = TRUE)
expect_error(oncoSimulIndiv(mcf1,
initSize = -3),
"initSize < 1", fixed = TRUE)
})
test_that("samplePop with oncoSimulIndiv object", {
oi <- allFitnessEffects(orderEffects =
c("F > D" = -0.3, "D > F" = 0.4),
noIntGenes = rexp(5, 10),
geneToModule =
c("Root" = "Root",
"F" = "f1, f2, f3",
"D" = "d1, d2") )
oiI1 <- oncoSimulIndiv(oi,
sampleEvery = 0.03,
keepEvery = 5,
model = "Exp")
expect_message(out <- samplePop(oiI1),
"Subjects by Genes matrix of 1 subjects and 10 genes.",
fixed = TRUE)
expect_true(ncol(out) == 10)
expect_true(nrow(out) == 1)
})
test_that("McFl warning with small sampleEvery", {
oi <- allFitnessEffects(orderEffects =
c("F > D" = -0.3, "D > F" = 0.4),
noIntGenes = rexp(5, 10),
geneToModule =
c("Root" = "Root",
"F" = "f1, f2, f3",
"D" = "d1, d2") )
expect_warning(oiI1 <- oncoSimulIndiv(oi, model = "McFL",
sampleEvery = 0.06,
keepEvery = 5,
onlyCancer = FALSE),
"With the McFarland model you often want smaller sampleEvery")
})
test_that("mu < 0 error", {
oi <- allFitnessEffects(orderEffects =
c("F > D" = -0.3, "D > F" = 0.4),
noIntGenes = rexp(5, 10),
geneToModule =
c("Root" = "Root",
"F" = "f1, f2, f3",
"D" = "d1, d2") )
expect_error(oiI1 <- oncoSimulIndiv(oi, mu = -1),
"mutation rate (mu) is negative",
fixed = TRUE)
})
test_that("keepEevery and sampleEvery consistency", {
oi <- allFitnessEffects(orderEffects =
c("F > D" = -0.3, "D > F" = 0.4),
noIntGenes = rexp(5, 10),
geneToModule =
c("Root" = "Root",
"F" = "f1, f2, f3",
"D" = "d1, d2") )
expect_warning(oiI1 <- oncoSimulIndiv(oi,
finalTime = 0.5,
keepEvery = 0.25,
onlyCancer = FALSE,
sampleEvery = 0.3),
"setting keepEvery <- sampleEvery",
fixed = TRUE)
})
test_that("using old poset format: error if need param null", {
data(examplePosets)
p701 <- examplePosets[["p701"]]
expect_error(oncoSimulIndiv(p701, s = NULL),
"You must specify all of")
})
test_that("using old poset format: error if need param null", {
data(examplePosets)
p701 <- examplePosets[["p701"]]
expect_error(oncoSimulIndiv(p701, sh = NULL),
"You must specify all of")
})
test_that("using old poset format: error if need param null", {
data(examplePosets)
p701 <- examplePosets[["p701"]]
expect_error(oncoSimulIndiv(p701, numPassengers = NULL),
"You must specify all of")
})
test_that("verbosity options", {
oi <- allFitnessEffects(orderEffects =
c("F > D" = -0.3, "D > F" = 0.4),
noIntGenes = rexp(5, 10),
geneToModule =
c("Root" = "Root",
"F" = "f1, f2, f3",
"D" = "d1, d2") )
expect_output(oncoSimulIndiv(oi, verbosity = 2,
detectionSize = 1e3,
sampleEvery = 0.03,
keepEvery = 2,
finalTime = 3,
onlyCancer = FALSE),
"Total Pop Size = ", fixed = TRUE)
})
test_that("printing oncosimul object", {
oi <- allFitnessEffects(orderEffects =
c("F > D" = -0.3, "D > F" = 0.4),
noIntGenes = rexp(5, 10),
geneToModule =
c("Root" = "Root",
"F" = "f1, f2, f3",
"D" = "d1, d2") )
out <- oncoSimulIndiv(oi,
sampleEvery = 0.03,
keepEvery = 2,
detectionSize = 1e3,
finalTime = 3,
onlyCancer = FALSE)
expect_output(print(out),
"Individual OncoSimul trajectory with call")
})
test_that("printing oncosimul pop object", {
oi <- allFitnessEffects(orderEffects =
c("F > D" = -0.3, "D > F" = 0.4),
noIntGenes = rexp(5, 10),
geneToModule =
c("Root" = "Root",
"F" = "f1, f2, f3",
"D" = "d1, d2") )
out <- oncoSimulPop(4,
oi,
sampleEvery = 0.03,
keepEvery = 2,
finalTime = 3,
detectionSize = 1e3,
onlyCancer = FALSE, mc.cores = 2)
expect_output(print(out),
"Population of OncoSimul trajectories of 4 individuals")
})
test_that("exercising oncoSimulSample, old format", {
data(examplePosets)
p701 <- examplePosets[["p701"]]
expect_message(ofw <- oncoSimulSample(2, p701,
sampleEvery = 0.03,
detectionSize = 1e3,
finalTime = 3,
onlyCancer = FALSE,
showProgress = TRUE),
"Successfully sampled 2 individuals")
expect_message(ofs <- oncoSimulSample(2, p701,
sampleEvery = 0.03,
detectionSize = 1e3,
finalTime = 3,
onlyCancer = FALSE,
typeSample = "single"),
"Successfully sampled 2 individuals")
expect_equal(dim(ofw$popSample), c(2, 7))
expect_equal(dim(ofs$popSample), c(2, 7))
})
test_that("exercising oncoSimulSample, new format", {
pancr <- allFitnessEffects(data.frame(parent = c("Root", rep("KRAS", 4), "SMAD4", "CDNK2A",
"TP53", "TP53", "MLL3"),
child = c("KRAS","SMAD4", "CDNK2A",
"TP53", "MLL3",
rep("PXDN", 3), rep("TGFBR2", 2)),
s = 0.05,
sh = -0.3,
typeDep = "MN"))
expect_message(pS <- oncoSimulSample(2, pancr, sampleEvery = 0.03,
detectionSize = 1e3,
finalTime = 3,
onlyCancer = FALSE),
"Successfully sampled 2 individuals")
expect_message(
pSs <- oncoSimulSample(2,
pancr,
sampleEvery = 0.03,
detectionSize = 1e3,
finalTime = 3,
onlyCancer = FALSE,
typeSample = "single",
showProgress = TRUE),
"Successfully sampled 2 individuals")
expect_equal(dim(pS$popSample), c(2, 7))
expect_equal(dim(pSs$popSample), c(2, 7))
})
test_that("check error unknown timeSample", {
data(examplePosets)
p701 <- examplePosets[["p701"]]
r1 <- oncoSimulIndiv(p701, onlyCancer = TRUE, max.num.tries = 5000)
expect_error(samplePop(r1, timeSample = "uniformo"),
"Unknown timeSample option")
expect_error(samplePop(r1, timeSample = "uni"),
"Unknown timeSample option")
expect_error(samplePop(r1, timeSample = "lasto"),
"Unknown timeSample option")
expect_error(samplePop(r1, timeSample = "whole"),
"Unknown timeSample option")
expect_error(samplePop(r1, timeSample = "single"),
"Unknown timeSample option")
expect_error(samplePop(r1, timeSample = "cucu"),
"Unknown timeSample option")
})
test_that("check error unknown typeSample", {
data(examplePosets)
p701 <- examplePosets[["p701"]]
r1 <- oncoSimulIndiv(p701, onlyCancer = TRUE, max.num.tries = 5000)
expect_error(samplePop(r1, typeSample = "uniformo"),
"Unknown typeSample option")
expect_error(samplePop(r1, typeSample = "uni"),
"Unknown typeSample option")
expect_error(samplePop(r1, typeSample = "lasto"),
"Unknown typeSample option")
expect_error(samplePop(r1, typeSample = "uniform"),
"Unknown typeSample option")
expect_error(samplePop(r1, typeSample = "last"),
"Unknown typeSample option")
expect_error(samplePop(r1, typeSample = "wholo"),
"Unknown typeSample option")
expect_error(samplePop(r1, typeSample = "whola"),
"Unknown typeSample option")
expect_error(samplePop(r1, typeSample = "singlo"),
"Unknown typeSample option")
expect_error(samplePop(r1, typeSample = "cell"),
"Unknown typeSample option")
expect_error(samplePop(r1, typeSample = "cucu"),
"Unknown typeSample option")
})
test_that("oncosimul sample without drivers", {
np <- 20
s <- 0.015
spp <- 0.01
nd <- 5
mcf1 <- allFitnessEffects(noIntGenes = rep(spp, np),
drvNames = integer(0))
mcf2 <- allFitnessEffects(noIntGenes = c(rep(s, nd), rep(spp, np)),
drvNames = character(0))
expect_message(o1 <- oncoSimulSample(2, mcf1, sampleEvery = 0.03,
detectionSize = 1e3,
finalTime = 3,
onlyCancer = FALSE),
"Successfully sampled 2 individuals")
expect_message(o2 <- oncoSimulSample(5, mcf2, sampleEvery = 0.03,
detectionSize = 1e3,
finalTime = 3,
onlyCancer = FALSE),
"Successfully sampled 5 individuals")
})
test_that("samplePop, a few examples with time last and whole pop", {
## Why am I testing this? No particular reason. But once I got
## confused and thought this function, which is the basis of
## oncoSimulSample, had a bug. The bug was in my understanding.
initSize <- 10 ## as it is passed below, in the structure; the rest
## for the same reason
K <- 1
mu <- 1e-6
sampleEvery <- 0.1
ni <- c(3, 1, rep(0, 5))
names(ni) <- c("a", "b", paste0("n", seq.int(5)))
fe <- allFitnessEffects(noIntGenes = ni)
fp <- fe
numPassengers <- 0
seed <- NULL
verbosity <- 0
initMutant <- NULL
finalTime <- 3.0
max.memory <- 2000
mutationPropGrowth <- TRUE
max.wall.time.total <- 600
possibleAttempts <- max.num.tries.total <- 500 * 10
onlyCancer <- FALSE
keepPhylog <- FALSE
s1 <- structure(list(structure(list(pops.by.time = structure(c(3, 62925,
2, 4, 1, 57), .Dim = c(1L, 6L)), NumClones = 5, TotalPopSize = 62989,
Genotypes = structure(c(1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L), .Dim = c(7L,
5L)), GenotypesWDistinctOrderEff = list(1L, 1:2, c(1L, 5L
), c(1L, 6L), c(1L, 7L)), GenotypesLabels = c("a", "a, b",
"a, n3", "a, n4", "a, n5"), MaxNumDrivers = 0L, MaxDriversLast = 0L,
NumDriversLargestPop = 0L, LargestClone = 62925, PropLargestPopLast = 0.998983949578498,
FinalTime = 3, NumIter = 34L, HittedWallTime = FALSE, HittedMaxTries = FALSE,
TotalPresentDrivers = 0L, CountByDriver = integer(0), OccurringDrivers = "",
PerSampleStats = structure(c(62989, 62925, 0.998983949578498,
0, 0), .Dim = c(1L, 5L)), other = structure(list(attemptsUsed = 1L,
errorMF = -99, errorMF_size = 0, minDMratio = 25000,
minBMratio = 166666.666666667, PhylogDF = structure(list(
parent = structure(integer(0), .Label = character(0), class = "factor"),
child = structure(integer(0), .Label = character(0), class = "factor"),
time = numeric(0)), .Names = c("parent", "child",
"time"), row.names = integer(0), class = "data.frame"),
UnrecoverExcept = FALSE), .Names = c("attemptsUsed",
"errorMF", "errorMF_size", "minDMratio", "minBMratio",
"PhylogDF", "UnrecoverExcept")), Drivers = integer(0),
geneNames = c("a", "b", "n1", "n2", "n3", "n4", "n5")), .Names = c("pops.by.time",
"NumClones", "TotalPopSize", "Genotypes", "GenotypesWDistinctOrderEff",
"GenotypesLabels", "MaxNumDrivers", "MaxDriversLast", "NumDriversLargestPop",
"LargestClone", "PropLargestPopLast", "FinalTime", "NumIter",
"HittedWallTime", "HittedMaxTries", "TotalPresentDrivers", "CountByDriver",
"OccurringDrivers", "PerSampleStats", "other", "Drivers", "geneNames"
), class = c("oncosimul", "oncosimul2"), call = oncoSimulIndiv(fp = fp,
model = "Exp", numPassengers = numPassengers, mu = mu, detectionSize = 1e9, detectionDrivers = 99,
sampleEvery = sampleEvery, initSize = initSize, s = s, sh = sh,
K = K, keepEvery = -9, minDetectDrvCloneSz = "auto", extraTime = 0,
finalTime = finalTime, onlyCancer = onlyCancer, keepPhylog = keepPhylog,
mutationPropGrowth = mutationPropGrowth, max.memory = max.memory,
max.wall.time = max.wall.time.total, max.num.tries = possibleAttempts,
errorHitWallTime = TRUE, errorHitMaxTries = TRUE, verbosity = verbosity,
initMutant = initMutant, seed = seed)), structure(list(pops.by.time = structure(c(3,
16000), .Dim = 1:2), NumClones = 1, TotalPopSize = 16000, Genotypes = structure(c(1L,
0L, 0L, 0L, 0L, 0L, 0L), .Dim = c(7L, 1L)), GenotypesWDistinctOrderEff = list(
1L), GenotypesLabels = "a", MaxNumDrivers = 0L, MaxDriversLast = 0L,
NumDriversLargestPop = 0L, LargestClone = 16000, PropLargestPopLast = 1,
FinalTime = 3, NumIter = 30L, HittedWallTime = FALSE, HittedMaxTries = FALSE,
TotalPresentDrivers = 0L, CountByDriver = integer(0), OccurringDrivers = "",
PerSampleStats = structure(c(16000, 16000, 1, 0, 0), .Dim = c(1L,
5L)), other = structure(list(attemptsUsed = 1L, errorMF = -99,
errorMF_size = 0, minDMratio = 41666.6666666667,
minBMratio = 166666.666666667, PhylogDF = structure(list(
parent = structure(integer(0), .Label = character(0), class = "factor"),
child = structure(integer(0), .Label = character(0), class = "factor"),
time = numeric(0)), .Names = c("parent", "child",
"time"), row.names = integer(0), class = "data.frame"),
UnrecoverExcept = FALSE), .Names = c("attemptsUsed",
"errorMF", "errorMF_size", "minDMratio", "minBMratio",
"PhylogDF", "UnrecoverExcept")), Drivers = integer(0),
geneNames = c("a", "b", "n1", "n2", "n3", "n4", "n5")), .Names = c("pops.by.time",
"NumClones", "TotalPopSize", "Genotypes", "GenotypesWDistinctOrderEff",
"GenotypesLabels", "MaxNumDrivers", "MaxDriversLast", "NumDriversLargestPop",
"LargestClone", "PropLargestPopLast", "FinalTime", "NumIter",
"HittedWallTime", "HittedMaxTries", "TotalPresentDrivers", "CountByDriver",
"OccurringDrivers", "PerSampleStats", "other", "Drivers", "geneNames"
), class = c("oncosimul", "oncosimul2"), call = oncoSimulIndiv(fp = fp,
model = "Exp", numPassengers = numPassengers, mu = mu, detectionSize = 1e9, detectionDrivers = 99,
sampleEvery = sampleEvery, initSize = initSize, s = s, sh = sh,
K = K, keepEvery = -9, minDetectDrvCloneSz = "auto", extraTime = 0,
finalTime = finalTime, onlyCancer = onlyCancer, keepPhylog = keepPhylog,
mutationPropGrowth = mutationPropGrowth, max.memory = max.memory,
max.wall.time = max.wall.time.total, max.num.tries = possibleAttempts,
errorHitWallTime = TRUE, errorHitMaxTries = TRUE, verbosity = verbosity,
initMutant = initMutant, seed = seed)), structure(list(pops.by.time = structure(c(3,
65810, 4), .Dim = c(1L, 3L)), NumClones = 2, TotalPopSize = 65814,
Genotypes = structure(c(1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 1L, 0L, 0L), .Dim = c(7L, 2L)), GenotypesWDistinctOrderEff = list(
1L, c(1L, 5L)), GenotypesLabels = c("a", "a, n3"), MaxNumDrivers = 0L,
MaxDriversLast = 0L, NumDriversLargestPop = 0L, LargestClone = 65810,
PropLargestPopLast = 0.999939222657793, FinalTime = 3, NumIter = 31L,
HittedWallTime = FALSE, HittedMaxTries = FALSE, TotalPresentDrivers = 0L,
CountByDriver = integer(0), OccurringDrivers = "", PerSampleStats = structure(c(65814,
65810, 0.999939222657793, 0, 0), .Dim = c(1L, 5L)), other = structure(list(
attemptsUsed = 1L, errorMF = -99, errorMF_size = 0,
minDMratio = 41666.6666666667, minBMratio = 166666.666666667,
PhylogDF = structure(list(parent = structure(integer(0), .Label = character(0), class = "factor"),
child = structure(integer(0), .Label = character(0), class = "factor"),
time = numeric(0)), .Names = c("parent", "child",
"time"), row.names = integer(0), class = "data.frame"),
UnrecoverExcept = FALSE), .Names = c("attemptsUsed",
"errorMF", "errorMF_size", "minDMratio", "minBMratio",
"PhylogDF", "UnrecoverExcept")), Drivers = integer(0),
geneNames = c("a", "b", "n1", "n2", "n3", "n4", "n5")), .Names = c("pops.by.time",
"NumClones", "TotalPopSize", "Genotypes", "GenotypesWDistinctOrderEff",
"GenotypesLabels", "MaxNumDrivers", "MaxDriversLast", "NumDriversLargestPop",
"LargestClone", "PropLargestPopLast", "FinalTime", "NumIter",
"HittedWallTime", "HittedMaxTries", "TotalPresentDrivers", "CountByDriver",
"OccurringDrivers", "PerSampleStats", "other", "Drivers", "geneNames"
), class = c("oncosimul", "oncosimul2"), call = oncoSimulIndiv(fp = fp,
model = "Exp", numPassengers = numPassengers, mu = mu, detectionSize = 1e9, detectionDrivers = 99,
sampleEvery = sampleEvery, initSize = initSize, s = s, sh = sh,
K = K, keepEvery = -9, minDetectDrvCloneSz = "auto", extraTime = 0,
finalTime = finalTime, onlyCancer = onlyCancer, keepPhylog = keepPhylog,
mutationPropGrowth = mutationPropGrowth, max.memory = max.memory,
max.wall.time = max.wall.time.total, max.num.tries = possibleAttempts,
errorHitWallTime = TRUE, errorHitMaxTries = TRUE, verbosity = verbosity,
initMutant = initMutant, seed = seed)), structure(list(pops.by.time = structure(c(3,
51024), .Dim = 1:2), NumClones = 1, TotalPopSize = 51024, Genotypes = structure(c(1L,
0L, 0L, 0L, 0L, 0L, 0L), .Dim = c(7L, 1L)), GenotypesWDistinctOrderEff = list(
1L), GenotypesLabels = "a", MaxNumDrivers = 0L, MaxDriversLast = 0L,
NumDriversLargestPop = 0L, LargestClone = 51024, PropLargestPopLast = 1,
FinalTime = 3, NumIter = 30L, HittedWallTime = FALSE, HittedMaxTries = FALSE,
TotalPresentDrivers = 0L, CountByDriver = integer(0), OccurringDrivers = "",
PerSampleStats = structure(c(51024, 51024, 1, 0, 0), .Dim = c(1L,
5L)), other = structure(list(attemptsUsed = 1L, errorMF = -99,
errorMF_size = 0, minDMratio = 41666.6666666667,
minBMratio = 166666.666666667, PhylogDF = structure(list(
parent = structure(integer(0), .Label = character(0), class = "factor"),
child = structure(integer(0), .Label = character(0), class = "factor"),
time = numeric(0)), .Names = c("parent", "child",
"time"), row.names = integer(0), class = "data.frame"),
UnrecoverExcept = FALSE), .Names = c("attemptsUsed",
"errorMF", "errorMF_size", "minDMratio", "minBMratio",
"PhylogDF", "UnrecoverExcept")), Drivers = integer(0),
geneNames = c("a", "b", "n1", "n2", "n3", "n4", "n5")), .Names = c("pops.by.time",
"NumClones", "TotalPopSize", "Genotypes", "GenotypesWDistinctOrderEff",
"GenotypesLabels", "MaxNumDrivers", "MaxDriversLast", "NumDriversLargestPop",
"LargestClone", "PropLargestPopLast", "FinalTime", "NumIter",
"HittedWallTime", "HittedMaxTries", "TotalPresentDrivers", "CountByDriver",
"OccurringDrivers", "PerSampleStats", "other", "Drivers", "geneNames"
), class = c("oncosimul", "oncosimul2"), call = oncoSimulIndiv(fp = fp,
model = "Exp", numPassengers = numPassengers, mu = mu, detectionSize = 1e9, detectionDrivers = 99,
sampleEvery = sampleEvery, initSize = initSize, s = s, sh = sh,
K = K, keepEvery = -9, minDetectDrvCloneSz = "auto", extraTime = 0,
finalTime = finalTime, onlyCancer = onlyCancer, keepPhylog = keepPhylog,
mutationPropGrowth = mutationPropGrowth, max.memory = max.memory,
max.wall.time = max.wall.time.total, max.num.tries = possibleAttempts,
errorHitWallTime = TRUE, errorHitMaxTries = TRUE, verbosity = verbosity,
initMutant = initMutant, seed = seed)), structure(list(pops.by.time = structure(c(3,
64375, 1), .Dim = c(1L, 3L)), NumClones = 2, TotalPopSize = 64376,
Genotypes = structure(c(1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
1L, 0L, 0L, 0L, 0L), .Dim = c(7L, 2L)), GenotypesWDistinctOrderEff = list(
1L, c(1L, 3L)), GenotypesLabels = c("a", "a, n1"), MaxNumDrivers = 0L,
MaxDriversLast = 0L, NumDriversLargestPop = 0L, LargestClone = 64375,
PropLargestPopLast = 0.999984466260718, FinalTime = 3, NumIter = 31L,
HittedWallTime = FALSE, HittedMaxTries = FALSE, TotalPresentDrivers = 0L,
CountByDriver = integer(0), OccurringDrivers = "", PerSampleStats = structure(c(64376,
64375, 0.999984466260718, 0, 0), .Dim = c(1L, 5L)), other = structure(list(
attemptsUsed = 1L, errorMF = -99, errorMF_size = 0,
minDMratio = 41666.6666666667, minBMratio = 166666.666666667,
PhylogDF = structure(list(parent = structure(integer(0), .Label = character(0), class = "factor"),
child = structure(integer(0), .Label = character(0), class = "factor"),
time = numeric(0)), .Names = c("parent", "child",
"time"), row.names = integer(0), class = "data.frame"),
UnrecoverExcept = FALSE), .Names = c("attemptsUsed",
"errorMF", "errorMF_size", "minDMratio", "minBMratio",
"PhylogDF", "UnrecoverExcept")), Drivers = integer(0),
geneNames = c("a", "b", "n1", "n2", "n3", "n4", "n5")), .Names = c("pops.by.time",
"NumClones", "TotalPopSize", "Genotypes", "GenotypesWDistinctOrderEff",
"GenotypesLabels", "MaxNumDrivers", "MaxDriversLast", "NumDriversLargestPop",
"LargestClone", "PropLargestPopLast", "FinalTime", "NumIter",
"HittedWallTime", "HittedMaxTries", "TotalPresentDrivers", "CountByDriver",
"OccurringDrivers", "PerSampleStats", "other", "Drivers", "geneNames"
), class = c("oncosimul", "oncosimul2"), call = oncoSimulIndiv(fp = fp,
model = "Exp", numPassengers = numPassengers, mu = mu, detectionSize = 1e9, detectionDrivers = 99,
sampleEvery = sampleEvery, initSize = initSize, s = s, sh = sh,
K = K, keepEvery = -9, minDetectDrvCloneSz = "auto", extraTime = 0,
finalTime = finalTime, onlyCancer = onlyCancer, keepPhylog = keepPhylog,
mutationPropGrowth = mutationPropGrowth, max.memory = max.memory,
max.wall.time = max.wall.time.total, max.num.tries = possibleAttempts,
errorHitWallTime = TRUE, errorHitMaxTries = TRUE, verbosity = verbosity,
initMutant = initMutant, seed = seed))), class = "oncosimulpop")
cn <- c("a", "b", paste0("n", 1:5))
m1 <- matrix(c(rep(1, 5), rep(0, 30)),
ncol = 7,
dimnames = list(c(NULL), cn))
expect_identical( samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 0.5),
m1
)
m1 <- matrix(1, nrow = 5,
ncol = 7,
dimnames = list(c(NULL), cn))
expect_identical( samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 0),
m1
)
## from s1[[5]], the two possible differentiating thresholds, right on the
## equality
m1 <- matrix(c(rep(1, 5), rep(0, 30)),
ncol = 7,
dimnames = list(c(NULL), cn))
m1[rbind(c(1, 2), c(1, 5), c(1, 6), c(1, 7), c(3, 5), c(5, 3))] <- 1
expect_identical(samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 1/64376),
m1)
m1 <- matrix(c(rep(1, 5), rep(0, 30)),
ncol = 7,
dimnames = list(c(NULL), cn))
m1[rbind(c(1, 2), c(1, 5), c(1, 6), c(1, 7), c(3, 5))] <- 1
expect_identical(samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 1/64375),
m1)
## from s1[[3]], the two possible differentiating thresholds, right on the
## equality; also affects first population
m1 <- matrix(c(rep(1, 5), rep(0, 30)),
ncol = 7,
dimnames = list(c(NULL), cn))
m1[rbind( c(1, 5), c(1, 7), c(3, 5))] <- 1
expect_identical(samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 4/65814),
m1)
m1 <- matrix(c(rep(1, 5), rep(0, 30)),
ncol = 7,
dimnames = list(c(NULL), cn))
m1[rbind(c(1, 7))] <- 1
expect_identical(samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 5/65814),
m1)
## from s1[[1]], the two possible differentiating thresholds, right on the
## equality; also affects first population
m1 <- matrix(c(rep(1, 5), rep(0, 30)),
ncol = 7,
dimnames = list(c(NULL), cn))
m1[rbind(c(1, 2), c(1, 5), c(1, 6), c(1, 7), c(3, 5))] <- 1
expect_identical(samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 1/62989),
m1)
m1 <- matrix(c(rep(1, 5), rep(0, 30)),
ncol = 7,
dimnames = list(c(NULL), cn))
m1[rbind(c(1, 2), c(1, 5), c(1, 7), c(3, 5))] <- 1
expect_identical(samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 2/62989),
m1)
m1 <- matrix(c(rep(1, 5), rep(0, 30)),
ncol = 7,
dimnames = list(c(NULL), cn))
m1[rbind(c(1, 5), c(1, 7))] <- 1
expect_identical(samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 4/62989),
m1)
m1 <- matrix(c(rep(1, 5), rep(0, 30)),
ncol = 7,
dimnames = list(c(NULL), cn))
m1[rbind(c(1, 7))] <- 1
expect_identical(samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 57/62989),
m1)
m1 <- matrix(c(rep(1, 5), rep(0, 30)),
ncol = 7,
dimnames = list(c(NULL), cn))
expect_identical(samplePop(s1, timeSample = "last", typeSample = "whole",
thresholdWhole = 58/62989),
m1)
})
test_that("exercising oncoSimulIndiv, new format, extra time", {
pancr <- allFitnessEffects(data.frame(parent = c("Root", rep("KRAS", 4), "SMAD4", "CDNK2A",
"TP53", "TP53", "MLL3"),
child = c("KRAS","SMAD4", "CDNK2A",
"TP53", "MLL3",
rep("PXDN", 3), rep("TGFBR2", 2)),
s = 0.05,
sh = -0.3,
typeDep = "MN"))
expect_silent(
pSs <- oncoSimulIndiv(pancr,
sampleEvery = 0.03,
detectionSize = 1e3,
finalTime = 6,
extraTime = 5,
onlyCancer = FALSE))
expect_output(print(pSs), "Individual OncoSimul trajectory",
fixed = TRUE)
})
test_that("exercising oncoSimulIndiv, hit max ram", {
p1 <- allFitnessEffects(noIntGenes = rep(.1, 10))
expect_output(pSs <- oncoSimulIndiv(p1,
initSize = 1e4,
sampleEvery = 3,
detectionSize = 1e5,
finalTime = 1000,
extraTime = 5,
onlyCancer = FALSE,
max.memory = .01),
"Return outNS object > maxram",
fixed = TRUE)
})
test_that("exercising oncoSimulIndiv, verbosity", {
ii <- rep(.1, 20)
names(ii) <- letters[1:20]
p1 <- allFitnessEffects(noIntGenes = ii)
expect_output(pSs <- oncoSimulIndiv(p1,
initMutant = "a",
model = "McFL",
initSize = 1e2,
sampleEvery = 1,
detectionSize = 1e10,
finalTime = 2000,
extraTime = 5,
verbosity = 6,
onlyCancer = FALSE),
"Looping through", fixed = TRUE)
## This is too much: can take a minute.
## ii <- rep(.01, 1000)
## names(ii) <- paste0("n", 1:1000)
## p1 <- allFitnessEffects(noIntGenes = ii)
## expect_output(pSs <- oncoSimulIndiv(p1,
## initMutant = "n1",
## model = "Exp",
## initSize = 1e6,
## sampleEvery = 2,
## detectionSize = 1e7,
## finalTime = 50,
## extraTime = 5,
## verbosity = 2,
## onlyCancer = FALSE),
## "Looping through", fixed = TRUE)
})
test_that("oncoSimulIndiv miscell C++ warnings", {
pancri <- allFitnessEffects(
data.frame(parent = c("Root", rep("KRAS", 4), "SMAD4", "CDNK2A",
"TP53", "TP53", "MLL3"),
child = c("KRAS","SMAD4", "CDNK2A",
"TP53", "MLL3",
rep("PXDN", 3), rep("TGFBR2", 2)),
s = Inf,
sh = Inf,
typeDep = "MN"))
## This are messages in R, not R warnings, as from Rcpp::Rcout
expect_output(oncoSimulIndiv(pancri,
initSize = 1,
keepEvery = 5,
onlyCancer = FALSE),
"at least one sh is positive infinite",
fixed = TRUE)
expect_output(oncoSimulIndiv(pancri,
initSize = 1,
keepEvery = 5,
onlyCancer = FALSE),
"at least one s is infinite",
fixed = TRUE)
})
test_that("using old poset format: Bozic and sh < 0", {
data(examplePosets)
p701 <- examplePosets[["p701"]]
expect_silent(oncoSimulIndiv(p701, sh = -0.3,
model = "Bozic",
onlyCancer = FALSE))
})
test_that("using old poset format, extra time", {
data(examplePosets)
p701 <- examplePosets[["p701"]]
expect_silent(oncoSimulIndiv(p701, sh = 0.3,
initSize = 1000,
detectionSize = 1100,
model = "McFL",
finalTime = 1000,
extraTime = 3.17,
onlyCancer = FALSE))
expect_silent(oncoSimulIndiv(p701, sh = 0,
model = "Exp",
initSize = 1e4,
detectionSize = 1e6,
extraTime = 10,
onlyCancer = FALSE))
expect_silent(oncoSimulIndiv(p701, sh = -0.01,
model = "Bozic",
initSize = 1e4,
detectionSize = 1e6,
extraTime = 10,
onlyCancer = FALSE))
})
test_that("using old poset format, no initMutant", {
data(examplePosets)
p701 <- examplePosets[["p701"]]
expect_warning(p1 <- oncoSimulIndiv(p701, sh = 0.3,
initSize = 1000,
detectionSize = 1100,
model = "Exp",
finalTime = 1000,
extraTime = 3.17,
initMutant = 2,
onlyCancer = FALSE),
"With the old poset format you can no longer use initMutant",
fixed = TRUE)
expect_warning(p1 <- oncoSimulIndiv(p701, sh = 0.3,
initSize = 1000,
detectionSize = 1100,
model = "Exp",
finalTime = 1000,
extraTime = 3.17,
initMutant = c(2, 5),
onlyCancer = FALSE),
"With the old poset format you can no longer use initMutant",
fixed = TRUE)
})
test_that("using old poset format, exercise verbosity", {
data(examplePosets)
p701 <- examplePosets[["p701"]]
expect_output(oncoSimulIndiv(p701, sh = 0,
initSize = 10000,
detectionSize = 30000,
model = "Exp",
finalTime = 2000,
extraTime = 3.17,
verbosity = 10,
onlyCancer = FALSE),
"Looping", fixed = TRUE)
})
test_that("exercising verbosity, new format", {
gg <- rep(0.1, 20)
names(gg) <- letters[1:20]
ii <- allFitnessEffects(noIntGenes = gg)
expect_output(
pSs <- oncoSimulIndiv(ii,
sampleEvery = .5,
initSize = 1e4,
detectionSize = 5e5,
finalTime = 1000,
extraTime = 5,
keepEvery = 50,
verbosity = 10,
onlyCancer = FALSE),
"Looping",
fixed = TRUE)
})
test_that("old format: at most 64 genes", {
p1 <- cbind(1L, 2L)
expect_error(p1 <- oncoSimulIndiv(p1,
numPassengers = 66,
sh = 0,
initSize = 1e5,
sampleEvery = 0.02,
detectionSize = 1e9,
model = "Exp",
finalTime = 2000,
extraTime = 3.17,
onlyCancer = FALSE,
seed = NULL),
"Largest possible number of genes",
fixed = TRUE)
})
test_that("samplePop deals with failures in simuls", {
fe <- allFitnessEffects(noIntGenes = c(-0.1, -0.2, -0.3))
uu <- oncoSimulIndiv(fe, max.wall.time = 0.2, max.num.tries = 5)
uup <- oncoSimulPop(4, fe, max.wall.time = 0.2, max.num.tries = 5,
mc.cores = 2)
expect_warning(uus <- samplePop(uu),
"It looks like this simulation never completed",
fixed = TRUE)
expect_warning(uups <- samplePop(uup),
"It looks like this simulation never completed",
fixed = TRUE)
## And it works when only some fail
fe2 <- allFitnessEffects(noIntGenes = c(0.1, 0.2, 0.3))
uu2 <- oncoSimulIndiv(fe2)
u3 <- list(uu, uu2)
class(u3) <- "oncosimulpop"
expect_warning(uu3ps <- samplePop(u3),
"It looks like this simulation never completed",
fixed = TRUE)
})
test_that("summary.oncosimulepop deals with failures in simuls", {
fe <- allFitnessEffects(noIntGenes = c(-0.1, -0.2, -0.3))
uup <- oncoSimulPop(4, fe, max.wall.time = 0.2, max.num.tries = 5, mc.cores = 2)
expect_warning(uus <- summary(uup),
"All simulations failed",
fixed = TRUE)
## And it works when only some fail
fe2 <- allFitnessEffects(noIntGenes = c(0.1, 0.2, 0.3))
uu2 <- oncoSimulPop(2, fe2, mc.cores = 2)
uu <- oncoSimulPop(2, fe, max.wall.time = 0.2, max.num.tries = 5, mc.cores = 2)
u3 <- c(uu, uu2)
class(u3) <- "oncosimulpop"
expect_warning(uu3ps <- summary(u3),
"Some simulations seem to have failed",
fixed = TRUE)
})
test_that("AND_DrvProbExit warnings and errors work" , {
fe <- allFitnessEffects(noIntGenes = c("A" = 0.1, "B" = 0.2, "C" = 0.3),
drvNames = c("A", "B", "C"))
expect_warning(u <- oncoSimulIndiv(fe, detectionDrivers = 1,
detectionProb = "default",
AND_DrvProbExit = TRUE),
"With AND_DrvProbExit = TRUE, detectionSize is ignored",
fixed = TRUE)
expect_error(u <- oncoSimulIndiv(fe, detectionDrivers = NA,
detectionProb = "default",
AND_DrvProbExit = TRUE),
"AND_DrvProbExit is TRUE: both of detectionProb",
fixed = TRUE)
data(examplePosets)
p701 <- examplePosets[["p701"]]
expect_error(u <- oncoSimulIndiv(p701, detectionDrivers = 1,
detectionProb = "default",
detectionSize = NA,
AND_DrvProbExit = TRUE),
"The AND_DrvProbExit = TRUE setting is invalid with the old poset",
fixed = TRUE)
}
)
test_that("AND_DrvProbExit exercising and test it works" , {
## RNGkind("Mersenne-Twister")
## set.seed(1)
fe <- allFitnessEffects(noIntGenes = c("A" = 0.1, "B" = 0.2, "C" = 0.3),
drvNames = c("A", "B"))
## set.seed(1)
u0 <- oncoSimulIndiv(fe, detectionDrivers = 1,
detectionProb = "default",
AND_DrvProbExit = FALSE,
detectionSize = NA)
## set.seed(1)
u <- oncoSimulIndiv(fe, detectionDrivers = 1,
detectionProb = "default",
AND_DrvProbExit = TRUE,
detectionSize = NA)
expect_true(u$TotalPresentDrivers >= 1)
## set.seed(1)
u2 <- oncoSimulIndiv(fe, detectionDrivers = 1,
detectionProb = "default",
AND_DrvProbExit = TRUE,
minDetectDrvCloneSz = 500,
detectionSize = NA)
expect_true(u2$TotalPresentDrivers >= 1)
## set.seed(2)
## set.seed(2)
## m00 <- oncoSimulIndiv(fe, detectionDrivers = NA, model = "McFL",
## detectionProb = "default",
## AND_DrvProbExit = FALSE,
## detectionSize = NA,
## initMutant = "C")
## set.seed(2)
m <- oncoSimulIndiv(fe, detectionDrivers = 1, model = "McFL",
detectionProb = "default",
AND_DrvProbExit = TRUE,
detectionSize = NA)
expect_true(m$TotalPresentDrivers >= 1)
## set.seed(2)
m1 <- oncoSimulIndiv(fe, detectionDrivers = 1, model = "McFL",
detectionProb = "default",
AND_DrvProbExit = TRUE,
minDetectDrvCloneSz = 10,
detectionSize = NA)
expect_true(m1$TotalPresentDrivers >= 1)
m1p <- m1$pops.by.time[, -c(1, 2), drop = FALSE]
expect_true(sum(m1p[nrow(m1p), , drop = FALSE]) >= 10)
## set.seed(2)
m2 <- oncoSimulIndiv(fe, detectionDrivers = 1, model = "McFL",
detectionProb = "default",
AND_DrvProbExit = TRUE,
minDetectDrvCloneSz = 100,
detectionSize = NA)
expect_true(m2$TotalPresentDrivers >= 1)
m2p <- m2$pops.by.time[, -c(1, 2), drop = FALSE]
expect_true(sum(m2p[nrow(m2p), , drop = FALSE]) >= 100)
## set.seed(2)
## slow
## m3 <- oncoSimulIndiv(fe, detectionDrivers = 1, model = "McFL",
## detectionProb = "default",
## AND_DrvProbExit = TRUE,
## minDetectDrvCloneSz = 1200,
## detectionSize = NA)
m3 <- oncoSimulIndiv(fe, detectionDrivers = 1, model = "McFL",
detectionProb = "default",
extraTime = 3,
AND_DrvProbExit = TRUE,
minDetectDrvCloneSz = 100,
detectionSize = NA)
expect_true(m3$TotalPresentDrivers >= 1)
m3p <- m3$pops.by.time[, -c(1, 2), drop = FALSE]
expect_true(sum(m3p[nrow(m3p), , drop = FALSE]) >= 100)
m3 <- oncoSimulIndiv(fe, detectionDrivers = 1, model = "McFL",
detectionProb = "default",
extraTime = 10,
AND_DrvProbExit = TRUE,
minDetectDrvCloneSz = 100,
detectionSize = NA)
expect_true(m3$TotalPresentDrivers >= 1)
m3p <- m3$pops.by.time[, -c(1, 2), drop = FALSE]
expect_true(sum(m3p[nrow(m3p), , drop = FALSE]) >= 100)
}
)
test_that("exercising oncoSimulIndiv, max size warning", {
p1 <- allFitnessEffects(noIntGenes = rep(.1, 10))
expect_output(oncoSimulIndiv(p1, initSize = 1.5e15, verbosity = 1,
onlyCancer = FALSE, mu= 1e-7))
data(examplePosets)
p701 <- examplePosets[["p701"]]
expect_output(oncoSimulIndiv(p701, initSize = 4.1e15, verbosity = 1,
onlyCancer = FALSE, mu= 1e-7))
})
cat(paste("\n Ending oncoSimulIndiv-miscell tests", date(), "\n"))
cat(paste(" Took ", round(difftime(Sys.time(), inittime, units = "secs"), 2), "\n\n"))
rm(inittime)
Try the OncoSimulR package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.