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tests/manual/test.mutator-long.R
In OncoSimulR: Forward Genetic Simulation of Cancer Progression with Epistasis
## This test takes about 12 to 13 minutes in my laptop
cat(paste("\n Starting test.mutator-long.R test at", date()))
cat(paste("\n a runif ", runif(1), "\n"))
## RNGkind("L'Ecuyer-CMRG") ## for the mclapplies
date()
mutsPerClone <- function(x, per.pop.mean = TRUE) {
perCl <- function(z)
unlist(lapply(z$GenotypesWDistinctOrderEff, length))
perCl2 <- function(z)
mean(unlist(lapply(z$GenotypesWDistinctOrderEff, length)))
if(per.pop.mean)
unlist(lapply(x, function(u) perCl2(u)))
else
lapply(x, function(u) perCl(u))
}
## Minifunctions for testing
medianNClones <- function(x) {
median(summary(x)$NumClones)
}
NClones <- function(x) {
summary(x)$NumClones
}
enom <- function(name, mu, ni = no, pp = pops) {
## expected without a given name for init
ii <- which(names(mu) == name)
out <- ni * pp * mu[-ii]
out[order(names(out))]
}
pnom <- function(name, mu, ni = no, pp = pops) {
ee <- enom(name, mu, ni, pp)
ee/sum(ee)
}
snom <- function(name, out) {
## observed without the init
cs <- colSums(OncoSimulR:::geneCounts(out))
ii <- which(names(cs) == name)
cs <- cs[-ii]
cs[order(names(cs))]
}
sm <- function(name, out) {
## totals for a given gene
cs <- colSums(OncoSimulR:::geneCounts(out))
ii <- which(names(cs) == name)
cs[ii]
}
totalind <- function(out) {
## total num indivs
sum(unlist(lapply(out, function(x) x$TotalPopSize)))
}
smA <- function(out) {
## totals counts for all. So total mutated over all.
cs <- colSums(OncoSimulR:::geneCounts(out))
sum(cs)
}
smAPi <- function(out) {
## smAnom but divided by number of individuals.
smA(out)/totalind(out)
}
smAnom <- function(out, name) {
## totals counts for all. So total mutated over all.
## but remove one, the fixed starting one.
cs <- colSums(OncoSimulR:::geneCounts(out))
ii <- which(names(cs) == name)
cs <- cs[-ii]
sum(cs)
}
smAnomPi <- function(out, name) {
## smAnom but divided by number of individuals.
smAnom(out, name)/totalind(out)
}
p.value.threshold <- 0.001
cat(paste("\n a second runif ", runif(1), "\n"))
date()
test_that("MCFL Relative ordering of number of clones with init mutant of mutator effects", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## Here we stop on finalTime, not popSize
## Can occasionally blow up with pE.f: pE not finite.
cat("\n mcx2bc: a runif is", runif(1), "\n")
pops <- 50
ni <- rep(0.01, 50)
names(ni) <- c("a", "b", "c", "d", paste0("n", 1:46))
fe <- allFitnessEffects(noIntGenes = ni)
fm6 <- allMutatorEffects(noIntGenes = c("a" = .05,
"b" = 1,
"c" = 10,
"d" = 50))
nca <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "a",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2, model = "McFL")
ncb <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "b",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2, model = "McFL")
ncc <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "c",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2, model = "McFL")
ncd <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "d",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2, model = "McFL")
T1 <- ( wilcox.test(summary(nca)$NumClones,
summary(ncb)$NumClones, alternative = "less")$p.value < p.value.threshold)
T2 <- (wilcox.test(summary(ncb)$NumClones ,
summary(ncc)$NumClones, alternative = "less")$p.value < p.value.threshold)
T3 <- ( wilcox.test(summary(ncc)$NumClones,
summary(ncd)$NumClones, alternative = "less")$p.value < p.value.threshold)
summary(nca)[, c(1:3, 8:9)]
summary(ncb)[, c(1:3, 8:9)]
summary(ncc)[, c(1:3, 8:9)]
summary(ncd)[, c(1:3, 8:9)]
T4 <- (t.test(mutsPerClone(nca), mutsPerClone(ncb), alternative = "less")$p.value < p.value.threshold)
T5 <- (t.test(mutsPerClone(ncb), mutsPerClone(ncc), alternative = "less")$p.value < p.value.threshold)
T6 <- (t.test(mutsPerClone(ncc), mutsPerClone(ncd), alternative = "less")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that(" Init with different mutators", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
cat("\n z2: a runif is", runif(1), "\n")
pops <- 60
ft <- .005
lni <- 50
no <- 1e7
ni <- c(0, 0, 0, rep(0, lni))
## scramble around names
names(ni) <- c("hereisoneagene",
"oreoisasabgene",
"nnhsisthecgene",
replicate(lni,
paste(sample(letters, 12), collapse = "")))
ni <- ni[order(names(ni))]
fe <- allFitnessEffects(noIntGenes = ni)
mutator1 <- mutator2 <- rep(1, lni + 3)
pg1 <- rep(5e-6, lni + 3)
## scramble names of mutator and per-gene too
names(mutator1) <- sample(names(ni))
names(pg1) <- sample(names(ni))
mutator1["hereisoneagene"] <- 100
mutator1["oreoisasabgene"] <- 1
mutator1["nnhsisthecgene"] <- 0.01
m1 <- allMutatorEffects(noIntGenes = mutator1)
m1.pg1.a <- oncoSimulPop(pops,
fe,
mu = pg1,
muEF = m1,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,
initMutant = "hereisoneagene",
sampleEvery = 0.01, keepEvery = 5, seed = NULL,
onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
m1.pg1.b <- oncoSimulPop(pops,
fe,
mu = pg1,
muEF = m1,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,
initMutant = "oreoisasabgene",
sampleEvery = 0.01, keepEvery = 5, seed = NULL,
onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
m1.pg1.c <- oncoSimulPop(pops,
fe,
mu = pg1,
muEF = m1,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,
initMutant = "nnhsisthecgene",
sampleEvery = 0.01, keepEvery = 5, seed = NULL,
onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
T1 <- (wilcox.test(NClones(m1.pg1.a), NClones(m1.pg1.b), alternative = "greater")$p.value < p.value.threshold)
T2 <- (wilcox.test(NClones(m1.pg1.b), NClones(m1.pg1.c), alternative = "greater")$p.value < p.value.threshold)
T3 <- (t.test(mutsPerClone(m1.pg1.a), mutsPerClone(m1.pg1.b), alternative = "greater")$p.value < p.value.threshold)
T4 <- (t.test(mutsPerClone(m1.pg1.b), mutsPerClone(m1.pg1.c), alternative = "greater")$p.value < p.value.threshold)
T5 <- (smAnomPi(m1.pg1.a, "hereisoneagene") >
smAnomPi(m1.pg1.b, "oreoisasabgene"))
T6 <- (smAnomPi(m1.pg1.b, "oreoisasabgene") >
smAnomPi(m1.pg1.c, "nnhsisthecgene"))
summary(m1.pg1.a)[, c(1:3, 8:9)]
summary(m1.pg1.b)[, c(1:3, 8:9)]
summary(m1.pg1.c)[, c(1:3, 8:9)]
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("Same mu vector, different mutator; diffs in number muts, tiny t", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## Here, there is no reproduction or death. Just mutation. And no double
## mutants either.
## We test:
## - mutator increases mutation rates as seen in:
## - number of clones created
## - number of total mutation events
cat("\n nm0: a runif is", runif(1), "\n")
pops <- 40
ft <- .0001
lni <- 100
no <- 1e7
fi <- rep(0, lni)
muvector <- rep(5e-6, lni)
## scrambling names
names(fi) <- replicate(lni,
paste(sample(letters, 12), collapse = ""))
names(muvector) <- sample(names(fi))
## choose something for mutator
mutator10 <- mutator100 <- fi[5]
mutator10[] <- 10
mutator100[] <- 100
fe <- allFitnessEffects(noIntGenes = fi)
m10 <- allMutatorEffects(noIntGenes = mutator10)
m100 <- allMutatorEffects(noIntGenes = mutator100)
pop10 <- oncoSimulPop(pops,
fe,
mu = muvector,
muEF = m10,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,
initMutant = names(mutator10),
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
pop100 <- oncoSimulPop(pops,
fe,
mu = muvector,
muEF = m100,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,
initMutant = names(mutator10),
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## number of total mutations
T1 <- (smAnomPi(pop10, names(mutator10)) < smAnomPi(pop100, names(mutator100)))
## number of clones
T2 <- (wilcox.test(NClones(pop100), NClones(pop10), alternative = "greater")$p.value < p.value.threshold)
T3 <- (t.test(mutsPerClone(pop100), mutsPerClone(pop10), alternative = "greater")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("Same mu vector, different mutator; diffs in number muts, larger t", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## reproduction, death, and double and possibly triple mutants. We
## decrease init pop size to make this fast.
cat("\n nm1: a runif is", runif(1), "\n")
pops <- 40
ft <- 1
lni <- 100
no <- 1e5
fi <- rep(0, lni)
muvector <- rep(5e-6, lni)
## scrambling names
names(fi) <- replicate(lni,
paste(sample(letters, 12), collapse = ""))
names(muvector) <- sample(names(fi))
## choose something for mutator
mutator10 <- mutator100 <- fi[5]
mutator10[] <- 10
mutator100[] <- 100
fe <- allFitnessEffects(noIntGenes = fi)
m10 <- allMutatorEffects(noIntGenes = mutator10)
m100 <- allMutatorEffects(noIntGenes = mutator100)
pop10 <- oncoSimulPop(pops,
fe,
mu = muvector,
muEF = m10,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,
initMutant = names(mutator10),
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
pop100 <- oncoSimulPop(pops,
fe,
mu = muvector,
muEF = m100,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,
initMutant = names(mutator10),
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## number of total mutations
T1 <- (smAnomPi(pop10, names(mutator10)) < smAnomPi(pop100, names(mutator100)))
## number of clones
T2 <- (wilcox.test(NClones(pop100), NClones(pop10), alternative = "greater")$p.value < p.value.threshold)
T3 <- (t.test(mutsPerClone(pop100), mutsPerClone(pop10), alternative = "greater")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("Relative ordering of number of clones with mut prop growth and init and scrambled names", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## Can occasionally blow up with pE.f: pE not finite.
cat("\n x2ef: a runif is", runif(1), "\n")
pops <- 90
ft <- 1
lni <- 200
no <- 5e3
ni <- c(5, 2, rep(0, lni))
## scramble around names
names(ni) <- c("thisistheagene",
"thisisthebgene",
replicate(lni,
paste(sample(letters, 12), collapse = "")))
ni <- ni[order(names(ni))]
fe <- allFitnessEffects(noIntGenes = ni)
fm1 <- allMutatorEffects(noIntGenes = c("thisistheagene" = 5))
mpg <- oncoSimulPop(pops, fe, muEF = fm1,
finalTime = ft,
mutationPropGrowth = TRUE,
initSize = no, sampleEvery = 0.01,
initMutant = "thisistheagene",
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
mnpg <- oncoSimulPop(pops, fe, muEF = fm1,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,sampleEvery = 0.01,
initMutant = "thisistheagene",
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
pg <- oncoSimulPop(pops, fe,
finalTime = ft,
mutationPropGrowth = TRUE,
initSize = no, sampleEvery = 0.01,
initMutant = "thisistheagene",
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
npg <- oncoSimulPop(pops, fe,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no, sampleEvery = 0.01,
initMutant = "thisistheagene",
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## These are the real tests
T1 <- ( wilcox.test(summary(mpg)$NumClones,
summary(mnpg)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T2 <- (wilcox.test(summary(mpg)$NumClones,
summary(pg)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T3 <- ( wilcox.test(summary(mnpg)$NumClones,
summary(npg)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T4 <- ( wilcox.test(summary(pg)$NumClones,
summary(npg)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T5 <- (t.test(mutsPerClone(mpg), mutsPerClone(mnpg), alternative = "greater")$p.value < p.value.threshold)
T6 <- (t.test(mutsPerClone(mpg), mutsPerClone(pg), alternative = "greater")$p.value < p.value.threshold)
T7 <- (t.test(mutsPerClone(mnpg), mutsPerClone(npg), alternative = "greater")$p.value < p.value.threshold)
T8 <- (t.test(mutsPerClone(pg), mutsPerClone(npg), alternative = "greater")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("Expect freq genotypes, mutator and var mut rates", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## increase mutator, decrease max mu
cat("\n u8_1: a runif is", runif(1), "\n")
pops <- 120
ft <- .0001
lni <- 80
no <- 5e7
ni <- c(0, 0, 0, rep(0, lni))
## scramble around names
names(ni) <- c("hereisoneagene",
"oreoisasabgene",
"nnhsisthecgene",
replicate(lni,
paste(sample(letters, 12), collapse = "")))
ni <- ni[order(names(ni))]
fe <- allFitnessEffects(noIntGenes = ni)
mutator1 <- rep(1, lni + 3)
pg1 <- runif(lni + 3, min = 1e-9, max = 1e-5) ## max should not be
## huge here as mutator
## is 34. Can get beyond
## 1
names(mutator1) <- sample(names(ni))
names(pg1) <- sample(names(ni))
mutator1["oreoisasabgene"] <- 200
m1 <- allMutatorEffects(noIntGenes = mutator1)
pg1["hereisoneagene"] <- 1e-3 ## 1e-3
m1.pg1.b <- oncoSimulPop(pops,
fe,
mu = pg1,
muEF = m1,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,
initMutant ="oreoisasabgene",
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
expect_true(sm("oreoisasabgene", m1.pg1.b) == totalind(m1.pg1.b))
## If you want to see the numbers
## enom("oreoisasabgene", pg1)
## snom("oreoisasabgene", m1.pg1.b)
p.fail <- 1e-3
T1 <- (chisq.test(snom("oreoisasabgene", m1.pg1.b),
p = pnom("oreoisasabgene", pg1, no, pops))$p.value > p.fail)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("Relative ordering of number of clones with init mutant of mutator effects and s = 0", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## Can occasionally blow up with pE.f: pE not finite.
cat("\n x2cd: a runif is", runif(1), "\n")
pops <- 50
ni <- rep(0, 50)
names(ni) <- c("a", "b", "c", "d", paste0("n", 1:46))
fe <- allFitnessEffects(noIntGenes = ni)
fm6 <- allMutatorEffects(noIntGenes = c("a" = .05,
"b" = 1,
"c" = 10,
"d" = 50))
nca <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "a",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
ncb <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "b",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
ncc <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "c",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
ncd <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "d",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## I once saw a weird thing
expect_true(var(summary(nca)$NumClones) > 1e-4)
expect_true(var(summary(ncb)$NumClones) > 1e-4)
expect_true(var(summary(ncc)$NumClones) > 1e-4)
expect_true(var(summary(ncd)$NumClones) > 1e-4)
## These are the real tests
T1 <- ( wilcox.test(summary(nca)$NumClones,
summary(ncb)$NumClones, alternative = "less")$p.value < p.value.threshold)
T2 <- (wilcox.test(summary(ncb)$NumClones,
summary(ncc)$NumClones, alternative = "less")$p.value < p.value.threshold)
T3 <- ( wilcox.test(summary(ncc)$NumClones,
summary(ncd)$NumClones, alternative = "less")$p.value < p.value.threshold)
T4 <- (t.test(mutsPerClone(nca), mutsPerClone(ncb), alternative = "less")$p.value < p.value.threshold)
T5 <- (t.test(mutsPerClone(ncb), mutsPerClone(ncc), alternative = "less")$p.value < p.value.threshold)
T6 <- (t.test(mutsPerClone(ncc), mutsPerClone(ncd), alternative = "less")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("Relative ordering of number of clones with init mutant of mutator effects", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## Here we stop on finalTime, not popSize
## Can occasionally blow up with pE.f: pE not finite.
cat("\n x2bc: a runif is", runif(1), "\n")
pops <- 60
ni <- rep(0.01, 50)
names(ni) <- c("a", "b", "c", "d", paste0("n", 1:46))
fe <- allFitnessEffects(noIntGenes = ni)
fm6 <- allMutatorEffects(noIntGenes = c("a" = .05,
"b" = 1,
"c" = 10,
"d" = 50))
nca <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "a",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
ncb <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "b",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
ncc <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "c",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
ncd <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "d",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
T1 <- ( wilcox.test(summary(nca)$NumClones,
summary(ncb)$NumClones, alternative = "less")$p.value < p.value.threshold)
T2 <- (wilcox.test(summary(ncb)$NumClones,
summary(ncc)$NumClones, alternative = "less")$p.value < p.value.threshold)
T3 <- ( wilcox.test(summary(ncc)$NumClones,
summary(ncd)$NumClones, alternative = "less")$p.value < p.value.threshold)
T4 <- (t.test(mutsPerClone(nca), mutsPerClone(ncb), alternative = "less")$p.value < p.value.threshold)
T5 <- (t.test(mutsPerClone(ncb), mutsPerClone(ncc), alternative = "less")$p.value < p.value.threshold)
T6 <- (t.test(mutsPerClone(ncc), mutsPerClone(ncd), alternative = "less")$p.value < p.value.threshold)
summary(nca)[, c(1:3, 8:9)]
summary(ncb)[, c(1:3, 8:9)]
summary(ncc)[, c(1:3, 8:9)]
summary(ncd)[, c(1:3, 8:9)]
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("MCFL Relative ordering of number of clones with mutator effects", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## Can occasionally blow up with pE.f: pE not finite.
cat("\n mcx1: a runif is", runif(1), "\n")
pops <- 60
fe <- allFitnessEffects(noIntGenes = c("a" = 0.12,
"b" = 0.14,
"c" = 0.16,
"d" = 0.11))
fm6 <- allMutatorEffects(noIntGenes = c("a" = 30,
"b" = 30,
"c" = 30,
"d" = 30))
nc1 <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =100,
mutationPropGrowth = FALSE,
sampleEvery = 0.001,
keepEvery = 5,
initSize = 1e6, mc.cores = 2, model = "McFL",
onlyCancer = FALSE, detectionProb = NA, seed = NULL)
fm8 <- allMutatorEffects(noIntGenes = c("a" = 1,
"b" = 1,
"c" = 1,
"d" = 1))
nc2 <- oncoSimulPop(pops, fe, muEF = fm8, finalTime =100,
mutationPropGrowth = FALSE,
sampleEvery = 0.001,
keepEvery = 5,
initSize = 1e6, mc.cores = 2, model = "McFL",
onlyCancer = FALSE, detectionProb = NA, seed = NULL)
fm7 <- allMutatorEffects(noIntGenes = c("a" = 1e-3,
"b" = 1e-3,
"c" = 1e-3,
"d" = 1e-3))
nc3 <- oncoSimulPop(pops, fe, muEF = fm7, finalTime =100,
mutationPropGrowth = FALSE,
sampleEvery = 0.001,
keepEvery = 5,
initSize = 1e6, mc.cores = 2, model = "McFL",
onlyCancer = FALSE, detectionProb = NA, seed = NULL)
T1 <- (wilcox.test(summary(nc1)$NumClones, summary(nc2)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T2 <- (wilcox.test(summary(nc2)$NumClones, summary(nc3)$NumClones, alternative = "greater")$p.value < p.value.threshold)
summary(nc1)[, c(1:3, 8:9)]
summary(nc2)[, c(1:3, 8:9)]
summary(nc3)[, c(1:3, 8:9)]
T3 <- (t.test(mutsPerClone(nc1), mutsPerClone(nc2), alternative = "greater")$p.value < p.value.threshold)
T4 <- (t.test(mutsPerClone(nc2), mutsPerClone(nc3), alternative = "greater")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("MCFL Relative ordering of number of clones with init mutant of mutator effects and s = 0", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## Can occasionally blow up with pE.f: pE not finite.
cat("\n mcx2cd: a runif is", runif(1), "\n")
pops <- 60
ni <- rep(0, 50)
names(ni) <- c("a", "b", "c", "d", paste0("n", 1:46))
fe <- allFitnessEffects(noIntGenes = ni)
fm6 <- allMutatorEffects(noIntGenes = c("a" = .05,
"b" = 1,
"c" = 10,
"d" = 50))
nca <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "a",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2, model = "McFL")
ncb <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "b",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2, model = "McFL")
ncc <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "c",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2, model = "McFL")
ncd <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "d",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2, model = "McFL")
## I once saw a weird thing
expect_true(var(summary(nca)$NumClones) > 1e-4)
expect_true(var(summary(ncb)$NumClones) > 1e-4)
expect_true(var(summary(ncc)$NumClones) > 1e-4)
expect_true(var(summary(ncd)$NumClones) > 1e-4)
## These are the real tests
T1 <- ( wilcox.test(summary(nca)$NumClones,
summary(ncb)$NumClones, alternative = "less")$p.value < p.value.threshold)
T2 <- (wilcox.test(summary(ncb)$NumClones,
summary(ncc)$NumClones, alternative = "less")$p.value < p.value.threshold)
T3 <- ( wilcox.test(summary(ncc)$NumClones,
summary(ncd)$NumClones, alternative = "less")$p.value < p.value.threshold)
T4 <- (t.test(mutsPerClone(nca), mutsPerClone(ncb), alternative = "less")$p.value < p.value.threshold)
T5 <- (t.test(mutsPerClone(ncb), mutsPerClone(ncc), alternative = "less")$p.value < p.value.threshold)
T6 <- (t.test(mutsPerClone(ncc), mutsPerClone(ncd), alternative = "less")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
## Slow (~ 5 seconds) but tests modules of mutator nicely.
date()
test_that("Mutator modules differences", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
cat("\n mmd1: a runif is", runif(1), "\n")
reps <- 60
no <- 5e3
ft <- 100
mu <- 1e-5
lni <- 50
m1 <- 1
m2 <- 25
m3 <- 50
ni <- rep(0, lni)
gn <- paste0("a", 1:lni)
names(ni) <- gn
gn <- paste(gn, collapse = ", ")
mut1 <- allMutatorEffects(epistasis = c("A" = m1),
geneToModule = c("A" = gn))
mut2 <- allMutatorEffects(epistasis = c("A" = m2),
geneToModule = c("A" = gn))
mut3 <- allMutatorEffects(epistasis = c("A" = m3),
geneToModule = c("A" = gn))
f1 <- allFitnessEffects(noIntGenes = ni)
b1 <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut1,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
gc()
b2 <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut2,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
gc()
b3 <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut3,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
gc()
summary(b3)[, c(1:3, 8:9)]
summary(b2)[, c(1:3, 8:9)]
summary(b1)[, c(1:3, 8:9)]
## mean(mutsPerClone(b3))
## mean(mutsPerClone(b2))
## mean(mutsPerClone(b1))
## This is, of course, affected by sampling only at end: we do not see
## the many intermediate events.
T1 <- ( wilcox.test(summary(b3)$NumClones,
summary(b2)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T2 <- ( wilcox.test(summary(b2)$NumClones,
summary(b1)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T3 <- (t.test(mutsPerClone(b3), mutsPerClone(b2), alternative = "greater")$p.value < p.value.threshold)
T4 <- (t.test(mutsPerClone(b2), mutsPerClone(b1), alternative = "greater")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("McFL: Mutator modules differences", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
cat("\n MCFLmmd1: a runif is", runif(1), "\n")
reps <- 60
no <- 5e3
ft <- 100
mu <- 1e-5
lni <- 50
m1 <- 1
m2 <- 25
m3 <- 50
ni <- rep(0, lni)
gn <- paste0("a", 1:lni)
names(ni) <- gn
gn <- paste(gn, collapse = ", ")
mut1 <- allMutatorEffects(epistasis = c("A" = m1),
geneToModule = c("A" = gn))
mut2 <- allMutatorEffects(epistasis = c("A" = m2),
geneToModule = c("A" = gn))
mut3 <- allMutatorEffects(epistasis = c("A" = m3),
geneToModule = c("A" = gn))
f1 <- allFitnessEffects(noIntGenes = ni)
b1 <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut1,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5, model = "McFL",
seed = NULL, mc.cores = 2
)
gc()
b2 <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut2,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5, model = "McFL",
seed = NULL, mc.cores = 2
)
gc()
b3 <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut3,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5, model = "McFL",
seed = NULL, mc.cores = 2
)
gc()
summary(b3)[, c(1:3, 8:9)]
summary(b2)[, c(1:3, 8:9)]
summary(b1)[, c(1:3, 8:9)]
## mean(mutsPerClone(b3))
## mean(mutsPerClone(b2))
## mean(mutsPerClone(b1))
## This is, of course, affected by sampling only at end: we do not see
## the many intermediate events.
T1 <- ( wilcox.test(summary(b3)$NumClones,
summary(b2)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T2 <- ( wilcox.test(summary(b2)$NumClones,
summary(b1)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T3 <- (t.test(mutsPerClone(b3), mutsPerClone(b2), alternative = "greater")$p.value < p.value.threshold)
T4 <- (t.test(mutsPerClone(b2), mutsPerClone(b1), alternative = "greater")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("Relative ordering of number of clones with mutator effects", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## Can occasionally blow up with pE.f: pE not finite.
cat("\n x1-910: a runif is", runif(1), "\n")
pops <- 80
fe <- allFitnessEffects(noIntGenes = c("a" = 0.12,
"b" = 0.14,
"c" = 0.16,
"d" = 0.11))
fm6 <- allMutatorEffects(noIntGenes = c("a" = 5,
"b" = 10,
"c" = 12,
"d" = 14))
nc1 <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =250,
mutationPropGrowth = FALSE,
sampleEvery = 0.001,
keepEvery = 5,
initSize = 1e6, mc.cores = 2,
onlyCancer = FALSE, detectionProb = NA, seed = NULL)
fm8 <- allMutatorEffects(noIntGenes = c("a" = 1,
"b" = 1,
"c" = 1,
"d" = 1))
nc2 <- oncoSimulPop(pops, fe, muEF = fm8, finalTime =250,
mutationPropGrowth = FALSE,
sampleEvery = 0.001,
keepEvery = 5,
initSize = 1e6, mc.cores = 2,
onlyCancer = FALSE, detectionProb = NA, seed = NULL)
fm7 <- allMutatorEffects(noIntGenes = c("a" = 1e-3,
"b" = 1e-3,
"c" = 1e-3,
"d" = 1e-3))
nc3 <- oncoSimulPop(pops, fe, muEF = fm7, finalTime =250,
mutationPropGrowth = FALSE,
sampleEvery = 0.001,
keepEvery = 5,
initSize = 1e6, mc.cores = 2,
onlyCancer = FALSE, detectionProb = NA, seed = NULL)
T1 <- (wilcox.test(summary(nc1)$NumClones, summary(nc2)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T2 <- (wilcox.test(summary(nc2)$NumClones, summary(nc3)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T3 <- (t.test(mutsPerClone(nc1), mutsPerClone(nc2), alternative = "greater")$p.value < p.value.threshold)
T4 <- (t.test(mutsPerClone(nc2), mutsPerClone(nc3), alternative = "greater")$p.value < p.value.threshold)
summary(nc1)[, c(1:3, 8:9)]
summary(nc2)[, c(1:3, 8:9)]
summary(nc3)[, c(1:3, 8:9)]
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
## very slow, because huge number of clones. But tests several phenomena comprehensively.
## same with McFL below
date()
test_that("per-gene-mut rates and mutator", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
cat("\n oss11-x970: a runif is", runif(1), "\n")
ng <- 10
ni <- rep(0, ng)
m1 <- runif(ng, min = 1e-7, max = 5e-6)
m2 <- runif(ng, min = 1e-5, max = 1e-4)
names(ni) <- names(m1) <- names(m2) <- c(replicate(ng,
paste(sample(letters, 12), collapse = "")))
fe1 <- allFitnessEffects(noIntGenes = ni)
ft <- 50
no <- 5e5
reps <- 100
gn <- paste(names(ni), collapse = ", ")
## MUs used to be 25 and 100. Way too slow.
mutator1 <- allMutatorEffects(epistasis = c("MU" = 15),
geneToModule = c("MU" = gn))
mutator2 <- allMutatorEffects(epistasis = c("MU" = 30),
geneToModule = c("MU" = gn))
m1.mutator0 <- oncoSimulPop(reps,
fe1,
mu = m1,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
m1.mutator1 <- oncoSimulPop(reps,
fe1,
mu = m1,
muEF = mutator1,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
runif(1)
m1.mutator2 <- oncoSimulPop(reps,
fe1,
mu = m1,
muEF = mutator2,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
m2.mutator0 <- oncoSimulPop(reps,
fe1,
mu = m2,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
m2.mutator1 <- oncoSimulPop(reps,
fe1,
mu = m2,
muEF = mutator1,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
m2.mutator2 <- oncoSimulPop(reps,
fe1,
mu = m2,
muEF = mutator2,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
summary(m1.mutator0)[, c(1:3, 8:9)]
summary(m1.mutator1)[, c(1:3, 8:9)]
summary(m1.mutator2)[, c(1:3, 8:9)]
summary(m2.mutator0)[, c(1:3, 8:9)]
summary(m2.mutator1)[, c(1:3, 8:9)]
summary(m2.mutator2)[, c(1:3, 8:9)]
## Mutator increases if larger mutator and compared to no mutator
## within levels of per-gene mutation rates
T1 <- ( wilcox.test(summary(m1.mutator2)$NumClones,
summary(m1.mutator1)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T2 <- ( wilcox.test(summary(m1.mutator1)$NumClones,
summary(m1.mutator0)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T3 <- ( wilcox.test(summary(m2.mutator2)$NumClones,
summary(m2.mutator1)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T4 <- ( wilcox.test(summary(m2.mutator1)$NumClones,
summary(m2.mutator0)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T5 <- (t.test(mutsPerClone(m1.mutator2), mutsPerClone(m1.mutator1), alternative = "greater")$p.value < p.value.threshold)
T6 <- (t.test(mutsPerClone(m1.mutator1), mutsPerClone(m1.mutator0), alternative = "greater")$p.value < p.value.threshold)
T7 <- (t.test(mutsPerClone(m2.mutator2), mutsPerClone(m2.mutator1), alternative = "greater")$p.value < p.value.threshold)
T8 <- (t.test(mutsPerClone(m2.mutator1), mutsPerClone(m2.mutator0), alternative = "greater")$p.value < p.value.threshold)
## Increases in mutation rates increase clones, etc, within levels of
## mutator.
Ta <- ( wilcox.test(summary(m1.mutator0)$NumClones,
summary(m2.mutator0)$NumClones, alternative = "less")$p.value < p.value.threshold)
Tb <- ( wilcox.test(summary(m1.mutator1)$NumClones,
summary(m2.mutator1)$NumClones, alternative = "less")$p.value < p.value.threshold)
Tc <- ( wilcox.test(summary(m1.mutator2)$NumClones,
summary(m2.mutator2)$NumClones, alternative = "less")$p.value < p.value.threshold)
Td <- (t.test(mutsPerClone(m1.mutator0), mutsPerClone(m2.mutator0), alternative = "less")$p.value < p.value.threshold)
Te <- (t.test(mutsPerClone(m1.mutator1), mutsPerClone(m2.mutator1), alternative = "less")$p.value < p.value.threshold)
Tf <- (t.test(mutsPerClone(m1.mutator2), mutsPerClone(m2.mutator2), alternative = "less")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8 &&
Ta && Tb && Tc && Td && Te && Tf) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8 &&
Ta && Tb && Tc && Td && Te && Tf)
})
date()
date()
test_that("McFL: per-gene-mut rates and mutator", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
cat("\n mcfloss11: a runif is", runif(1), "\n")
ng <- 10
ni <- rep(0, ng)
m1 <- runif(ng, min = 1e-7, max = 5e-6)
m2 <- runif(ng, min = 1e-5, max = 1e-4)
names(ni) <- names(m1) <- names(m2) <- c(replicate(ng,
paste(sample(letters, 12), collapse = "")))
fe1 <- allFitnessEffects(noIntGenes = ni)
ft <- 50
no <- 5e5
reps <- 150
gn <- paste(names(ni), collapse = ", ")
mutator1 <- allMutatorEffects(epistasis = c("MU" = 15),
geneToModule = c("MU" = gn))
mutator2 <- allMutatorEffects(epistasis = c("MU" = 30),
geneToModule = c("MU" = gn))
m1.mutator0 <- oncoSimulPop(reps,
fe1,
mu = m1,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2, model = "McFL"
)
m1.mutator1 <- oncoSimulPop(reps,
fe1,
mu = m1,
muEF = mutator1,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2, model = "McFL"
)
m1.mutator2 <- oncoSimulPop(reps,
fe1,
mu = m1,
muEF = mutator2,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2, model = "McFL"
)
cat("\n starting m2\n")
m2.mutator0 <- oncoSimulPop(reps,
fe1,
mu = m2,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2, model = "McFL"
)
m2.mutator1 <- oncoSimulPop(reps,
fe1,
mu = m2,
muEF = mutator1,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2, model = "McFL"
)
m2.mutator2 <- oncoSimulPop(reps,
fe1,
mu = m2,
muEF = mutator2,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2, model = "McFL"
)
summary(m1.mutator0)[, c(1:3, 8:9)]
summary(m1.mutator1)[, c(1:3, 8:9)]
summary(m1.mutator2)[, c(1:3, 8:9)]
summary(m2.mutator0)[, c(1:3, 8:9)]
summary(m2.mutator1)[, c(1:3, 8:9)]
summary(m2.mutator2)[, c(1:3, 8:9)]
## Mutator increases if larger mutator and compared to no mutator
## within levels of per-gene mutation rates
T1 <- ( wilcox.test(summary(m1.mutator2)$NumClones,
summary(m1.mutator1)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T2 <- ( wilcox.test(summary(m1.mutator1)$NumClones,
summary(m1.mutator0)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T3 <- ( wilcox.test(summary(m2.mutator2)$NumClones,
summary(m2.mutator1)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T4 <- ( wilcox.test(summary(m2.mutator1)$NumClones,
summary(m2.mutator0)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T5 <- (t.test(mutsPerClone(m1.mutator2), mutsPerClone(m1.mutator1), alternative = "greater")$p.value < p.value.threshold)
T6 <- (t.test(mutsPerClone(m1.mutator1), mutsPerClone(m1.mutator0), alternative = "greater")$p.value < p.value.threshold)
T7 <- (t.test(mutsPerClone(m2.mutator2), mutsPerClone(m2.mutator1), alternative = "greater")$p.value < p.value.threshold)
T8 <- (t.test(mutsPerClone(m2.mutator1), mutsPerClone(m2.mutator0), alternative = "greater")$p.value < p.value.threshold)
## Increases in mutation rates increase clones, etc, within levels of
## mutator.
Ta <- ( wilcox.test(summary(m1.mutator0)$NumClones,
summary(m2.mutator0)$NumClones, alternative = "less")$p.value < p.value.threshold)
Tb <- ( wilcox.test(summary(m1.mutator1)$NumClones,
summary(m2.mutator1)$NumClones, alternative = "less")$p.value < p.value.threshold)
Tc <- ( wilcox.test(summary(m1.mutator2)$NumClones,
summary(m2.mutator2)$NumClones, alternative = "less")$p.value < p.value.threshold)
Td <- (t.test(mutsPerClone(m1.mutator0), mutsPerClone(m2.mutator0), alternative = "less")$p.value < p.value.threshold)
Te <- (t.test(mutsPerClone(m1.mutator1), mutsPerClone(m2.mutator1), alternative = "less")$p.value < p.value.threshold)
Tf <- (t.test(mutsPerClone(m1.mutator2), mutsPerClone(m2.mutator2), alternative = "less")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8 &&
Ta && Tb && Tc && Td && Te && Tf) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8 &&
Ta && Tb && Tc && Td && Te && Tf)
})
date()
date()
test_that("Mutator, several modules differences, McFL", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
cat("\n mmdSMMC1: a runif is", runif(1), "\n")
reps <- 50
no <- 5e3
ft <- 50 ## you need it large enough to get enough hits
mu <- 1e-5
ln <- 50
m1 <- 5 ## if this is too large, easy to get it to blow.
ni <- rep(0, 3 * ln)
gna <- paste0("a", 1:ln)
gnb <- paste0("b", 1:ln)
gnc <- paste0("c", 1:ln)
names(ni) <- c(gna, gnb, gnc)
gn1 <- paste(c(gna, gnb, gnc), collapse = ", ")
gna <- paste(gna, collapse = ", ")
gnb <- paste(gnb, collapse = ", ")
gnc <- paste(gnc, collapse = ", ")
mut1 <- allMutatorEffects(epistasis = c("A" = m1),
geneToModule = c("A" = gn1))
mut2 <- allMutatorEffects(epistasis = c("A" = m1,
"B" = m1,
"C" = m1),
geneToModule = c("A" = gna,
"B" = gnb,
"C" = gnc))
f1 <- allFitnessEffects(noIntGenes = ni)
b1 <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut1,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
detectionSize = 1e9,
detectionDrivers = 9999,
seed = NULL, mc.cores = 2, model = "McFL"
)
gc()
b2 <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut2,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
detectionSize = 1e9,
detectionDrivers = 9999,
seed = NULL, mc.cores = 2, model = "McFL"
)
gc()
summary(b2)[, c(1:3, 8:9)]
summary(b1)[, c(1:3, 8:9)]
mean(mutsPerClone(b2))
mean(mutsPerClone(b1))
## This is, of course, affected by sampling only at end: we do not see
## the many intermediate events.
T1 <- ( wilcox.test(summary(b2)$NumClones,
summary(b1)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T2 <- (t.test(mutsPerClone(b2), mutsPerClone(b1), alternative = "greater")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("McFL: Expect freq genotypes, mutator and var mut rates", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## increase mutator
cat("\n u8: a runif is", runif(1), "\n")
pops <- 300
ft <- .0001
lni <- 80
no <- 5e7
ni <- c(0, 0, 0, rep(0, lni))
## scramble around names
names(ni) <- c("hereisoneagene",
"oreoisasabgene",
"nnhsisthecgene",
replicate(lni,
paste(sample(letters, 12), collapse = "")))
ni <- ni[order(names(ni))]
fe <- allFitnessEffects(noIntGenes = ni)
pg1 <- runif(lni + 3, min = 1e-7, max = 1e-4) ## max should not be
## huge here as mutator
## is 34. Can get beyond
## 1
names(pg1) <- sample(names(ni))
mutator1 <- c("oreoisasabgene" = 50)
m1 <- allMutatorEffects(noIntGenes = mutator1)
pg1["hereisoneagene"] <- 1e-3 ## to compare with a laarge one
m1.pg1.b <- oncoSimulPop(pops,
fe,
mu = pg1,
muEF = m1,
model = "McFL",
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,
initMutant ="oreoisasabgene",
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
expect_true(sm("oreoisasabgene", m1.pg1.b) == totalind(m1.pg1.b))
## enom("oreoisasabgene", pg1)
## snom("oreoisasabgene", m1.pg1.b)
p.fail <- 1e-3
T1 <- (chisq.test(snom("oreoisasabgene", m1.pg1.b),
p = pnom("oreoisasabgene", pg1, no, pops))$p.value > p.fail)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("Expect freq genotypes, mutator and var mut rates", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## Similar to above, but mutator has a single element, not the whole
## vector.
cat("\n u7_1: a runif is", runif(1), "\n")
pops <- 300
ft <- .0001
lni <- 80
no <- 5e7
ni <- c(0, 0, 0, rep(0, lni))
## scramble around names
names(ni) <- c("hereisoneagene",
"oreoisasabgene",
"nnhsisthecgene",
replicate(lni,
paste(sample(letters, 12), collapse = "")))
ni <- ni[order(names(ni))]
fe <- allFitnessEffects(noIntGenes = ni)
pg1 <- runif(lni + 3, min = 1e-7, max = 1e-4) ## max should not be
## huge here as mutator
## is 34. Can get beyond
## 1
names(pg1) <- sample(names(ni))
mutator1 <- c("oreoisasabgene" = 50)
m1 <- allMutatorEffects(noIntGenes = mutator1)
pg1["hereisoneagene"] <- 1e-3 ## to compare with a laarge one
m1.pg1.b <- oncoSimulPop(pops,
fe,
mu = pg1,
muEF = m1,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,
initMutant ="oreoisasabgene",
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
expect_true(sm("oreoisasabgene", m1.pg1.b) == totalind(m1.pg1.b))
enom("oreoisasabgene", pg1, no, pops)
snom("oreoisasabgene", m1.pg1.b)
p.fail <- 1e-3
T1 <- (chisq.test(snom("oreoisasabgene", m1.pg1.b),
p = pnom("oreoisasabgene", pg1, no, pops))$p.value > p.fail)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("Mutator and mutPropGrowth", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## we stop on size
cat("\n mmpg: a runif is", runif(1), "\n")
reps <- 200
no <- 5e3
ds <- 7e4
ft <- 1000
mu <- 1e-5
ln <- 60
m1 <- 1 ## if this is too large, easy to get it to blow.
m50 <- 50
gna <- paste0("a", 1:ln)
gna <- paste(gna, collapse = ", ")
f1 <- allFitnessEffects(epistasis = c("A" = 0.4),
geneToModule = c("A" = gna))
mut1 <- allMutatorEffects(epistasis = c("M" = m1),
geneToModule = c("M" = gna))
mut50 <- allMutatorEffects(epistasis = c("M" = m50),
geneToModule = c("M" = gna))
m1.pg <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut1,
mutationPropGrowth = TRUE,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
detectionSize = ds,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
## gc()
m1.npg <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut1,
mutationPropGrowth = FALSE,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
detectionSize = ds,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
##gc()
m50.pg <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut50,
mutationPropGrowth = TRUE,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
detectionSize = ds,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
## gc()
m50.npg <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut50,
mutationPropGrowth = FALSE,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
detectionSize = ds,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
##gc()
summary(m1.pg)[, c(1:3, 8:9)]
summary(m50.pg)[, c(1:3, 8:9)]
summary(m1.npg)[, c(1:3, 8:9)]
summary(m50.npg)[, c(1:3, 8:9)]
## Over mutator, as we have mutPropGrowth, clones, etc, increase
T1 <- ( wilcox.test(summary(m1.pg)$NumClones,
summary(m1.npg)$NumClones,
alternative = "greater")$p.value < p.value.threshold)
T2 <- (t.test(mutsPerClone(m1.pg),
mutsPerClone(m1.npg),
alternative = "greater")$p.value < p.value.threshold)
T3 <- ( wilcox.test(summary(m50.pg)$NumClones,
summary(m50.npg)$NumClones,
alternative = "greater")$p.value < p.value.threshold)
T4 <- (t.test(mutsPerClone(m50.pg),
mutsPerClone(m50.npg),
alternative = "greater")$p.value < p.value.threshold)
## Over mutPropGrowth, as we increase mutator, clones, etc, increase
T5 <- ( wilcox.test(summary(m50.pg)$NumClones,
summary(m1.pg)$NumClones,
alternative = "greater")$p.value < p.value.threshold)
T6 <- (t.test(mutsPerClone(m50.pg),
mutsPerClone(m1.pg),
alternative = "greater")$p.value < p.value.threshold)
T7 <- ( wilcox.test(summary(m50.npg)$NumClones,
summary(m1.npg)$NumClones,
alternative = "greater")$p.value < p.value.threshold)
T8 <- (t.test(mutsPerClone(m50.npg),
mutsPerClone(m1.npg),
alternative = "greater")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
cat(paste("\n Finished test.mutator-long.R test at", date(), "\n"))
## ### These are too convoluted and trying to pick too tiny diffs. Substitute
## ### by diffs initMutant and by per-gene-mut rates and mutator
## date()
## test_that("Num clones: Mutator and var mut rates and init and really scrambled names", {
##
##
## cat("\n z2: a runif is", runif(1), "\n")
## pops <- 40
## ft <- .005
## lni <- 50
## no <- 1e7
## ni <- c(0, 0, 0, rep(0, lni))
## ## scramble around names
## names(ni) <- c("hereisoneagene",
## "oreoisasabgene",
## "nnhsisthecgene",
## replicate(lni,
## paste(sample(letters, 12), collapse = "")))
## ni <- ni[order(names(ni))]
## fe <- allFitnessEffects(noIntGenes = ni)
## mutator1 <- mutator2 <- rep(1, lni + 3)
## pg1 <- pg2 <- rep(5e-6, lni + 3)
## ## scramble names of mutator and per-gene too
## names(mutator1) <- sample(names(ni))
## names(mutator2) <- sample(names(ni))
## names(pg1) <- sample(names(ni))
## names(pg2) <- sample(names(ni))
## mutator1["hereisoneagene"] <- 100
## mutator2["hereisoneagene"] <- 1
## mutator1["oreoisasabgene"] <- 1
## mutator2["oreoisasabgene"] <- 0.01
## mutator1["nnhsisthecgene"] <- 0.01
## mutator2["nnhsisthecgene"] <- 100
## m1 <- allMutatorEffects(noIntGenes = mutator1)
## m2 <- allMutatorEffects(noIntGenes = mutator2)
## pg1["hereisoneagene"] <- 1e-3
## pg2["hereisoneagene"] <- 1e-14
## pg1["oreoisasabgene"] <- 1e-7
## pg2["oreoisasabgene"] <- 1e-7
## pg1["nnhsisthecgene"] <- 1e-14
## pg2["nnhsisthecgene"] <- 1e-3
## m1.pg1.a <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m1.pg2.a <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m1.pg1.c <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m1.pg2.c <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m1.pg1.b <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "oreoisasabgene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## expect_true(medianNClones(m1.pg1.a) < medianNClones(m1.pg2.a))
## ## Too tiny diffs
## ## expect_true(medianNClones(m1.pg1.c) > medianNClones(m1.pg2.c))
## expect_true(medianNClones(m1.pg1.a) > medianNClones(m1.pg1.c))
## expect_true(medianNClones(m1.pg2.a) > medianNClones(m1.pg2.c))
## expect_true(medianNClones(m1.pg1.a) > medianNClones(m1.pg1.b))
## m2.pg1.a <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m2,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m2.pg2.a <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m2,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m2.pg1.c <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m2,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m2.pg2.c <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m2,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## ## too tiny
## ## expect_true(medianNClones(m2.pg1.a) < medianNClones(m2.pg2.a))
## expect_true(medianNClones(m2.pg1.c) > medianNClones(m2.pg2.c))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m2.pg2.a) < medianNClones(m2.pg2.c))
## expect_true(medianNClones(m1.pg1.c) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m1.pg1.c) < medianNClones(m2.pg2.c))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m1.pg1.a))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m1.pg2.a))
## expect_true(medianNClones(m2.pg2.a) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m1.pg2.a) > medianNClones(m1.pg1.c))
## expect_true(smAnomPi(m2.pg1.a, "hereisoneagene") <
## smAnomPi(m2.pg1.c, "nnhsisthecgene"))
## expect_true(smAnomPi(m2.pg1.a, "hereisoneagene") <
## smAnomPi(m1.pg1.a, "hereisoneagene"))
## })
## date()
## ## This is slow and very fragile, because unless we want to increase pops
## ## a lots, we can often miss differences in
## ## medianNClones(m1.pg1.a) < medianNClones(m1.pg2.a)
## ## and
## ## medianNClones(m1.pg1.c) > medianNClones(m1.pg2.c)
## ## and
## ## medianNClones(m2.pg1.c) > medianNClones(m2.pg2.c)
## date()
## test_that("McFL: Num clones: Mutator and var mut rates and init and really scrambled names", {
## pseed <- sample(300:400, 1)
##
## cat("\n x3: a runif is", runif(1), "\n")
## pops <- 80
## ft <- .002
## lni <- 20
## no <- 3e5
## ni <- c(0, 0, 0, rep(0, lni))
## ## scramble around names
## names(ni) <- c("hereisoneagene",
## "oreoisasabgene",
## "nnhsisthecgene",
## replicate(lni,
## paste(sample(letters, 12), collapse = "")))
## ni <- ni[order(names(ni))]
## fe <- allFitnessEffects(noIntGenes = ni)
## mutator1 <- mutator2 <- rep(1, lni + 3)
## pg1 <- pg2 <- rep(1e-4, lni + 3)
## ## this: runif(lni + 3, min = 1e-6, max = 1e-5)
## ## adds too much variation in mutation rates between runs
## ## scramble names of mutator and per-gene too
## names(mutator1) <- sample(names(ni))
## names(mutator2) <- sample(names(ni))
## names(pg1) <- sample(names(ni))
## names(pg2) <- sample(names(ni))
## mutator1["hereisoneagene"] <- 50
## mutator2["hereisoneagene"] <- 5 # 1
## mutator1["oreoisasabgene"] <- 1
## mutator2["oreoisasabgene"] <- 0.01
## mutator1["nnhsisthecgene"] <- 1 # 0.5
## mutator2["nnhsisthecgene"] <- 50
## m1 <- allMutatorEffects(noIntGenes = mutator1)
## m2 <- allMutatorEffects(noIntGenes = mutator2)
## pg1["hereisoneagene"] <- 5e-3
## pg2["hereisoneagene"] <- 5e-15
## pg1["oreoisasabgene"] <- 1e-7
## pg2["oreoisasabgene"] <- 1e-7
## pg1["nnhsisthecgene"] <- 5e-15
## pg2["nnhsisthecgene"] <- 5e-3
## m1.pg1.a <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m1.pg2.a <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m1,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m1.pg1.c <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m1.pg2.c <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m1,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m1.pg1.b <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "oreoisasabgene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## expect_true(medianNClones(m1.pg1.a) < medianNClones(m1.pg2.a))
## ## Too tiny diffs
## ## expect_true(medianNClones(m1.pg1.c) > medianNClones(m1.pg2.c))
## expect_true(medianNClones(m1.pg1.a) > medianNClones(m1.pg1.c))
## expect_true(medianNClones(m1.pg2.a) > medianNClones(m1.pg2.c))
## expect_true(medianNClones(m1.pg1.a) > medianNClones(m1.pg1.b))
## m2.pg1.a <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m2,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m2.pg2.a <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m2,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m2.pg1.c <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m2,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m2.pg2.c <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m2,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## ## too small.
## ## expect_true(medianNClones(m2.pg1.a) < medianNClones(m2.pg2.a))
## expect_true(medianNClones(m2.pg1.c) > medianNClones(m2.pg2.c))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m2.pg2.a) < medianNClones(m2.pg2.c))
## expect_true(medianNClones(m1.pg1.c) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m1.pg1.c) < medianNClones(m2.pg2.c))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m1.pg1.a))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m1.pg2.a))
## expect_true(medianNClones(m2.pg2.a) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m1.pg2.a) > medianNClones(m1.pg1.c))
## expect_true(smAnomPi(m2.pg1.a, "hereisoneagene") <
## smAnomPi(m2.pg1.c, "nnhsisthecgene"))
## expect_true(smAnomPi(m2.pg1.a, "hereisoneagene") <
## smAnomPi(m1.pg1.a, "hereisoneagene"))
## })
## date()
## ## Tiny diffs often missed. Again, culprits are often
## ## * Not expected: medianNClones(m1.pg1.a) < medianNClones(m1.pg2.a) isn't true.
## ## * Not expected: medianNClones(m1.pg1.c) > medianNClones(m1.pg2.c) isn't true.
## ## * Not expected: medianNClones(m2.pg1.c) > medianNClones(m2.pg2.c) isn't true.
## date()
## test_that("Num clones: Mutator and var mut rates and init and really scrambled names, mutPropgrowth", {
## pseed <- sample(1:100, 1)
##
## cat("\n x4: a runif is", runif(1), "\n")
## pops <- 100 ## the more, the better, but takes long.
## ft <- .001
## lni <- 30 ## 200
## no <- 1e7
## ni <- c(0, 0, 0, rep(0, lni))
## ## scramble around names
## names(ni) <- c("hereisoneagene",
## "oreoisasabgene",
## "nnhsisthecgene",
## replicate(lni,
## paste(sample(letters, 12), collapse = "")))
## ni <- ni[order(names(ni))]
## fe <- allFitnessEffects(noIntGenes = ni)
## mutator1 <- mutator2 <- rep(1, lni + 3)
## pg1 <- pg2 <- rep(1e-5, lni + 3)
## ## scramble names of mutator and per-gene too
## names(mutator1) <- sample(names(ni))
## names(mutator2) <- sample(names(ni))
## names(pg1) <- sample(names(ni))
## names(pg2) <- sample(names(ni))
## mutator1["hereisoneagene"] <- 100
## mutator2["hereisoneagene"] <- 1
## mutator1["oreoisasabgene"] <- 1
## mutator2["oreoisasabgene"] <- 0.01
## mutator1["nnhsisthecgene"] <- 0.5
## mutator2["nnhsisthecgene"] <- 100
## m1 <- allMutatorEffects(noIntGenes = mutator1)
## m2 <- allMutatorEffects(noIntGenes = mutator2)
## pg1["hereisoneagene"] <- 1e-3
## pg2["hereisoneagene"] <- 1e-14
## pg1["oreoisasabgene"] <- 1e-7
## pg2["oreoisasabgene"] <- 1e-7
## pg1["nnhsisthecgene"] <- 1e-14
## pg2["nnhsisthecgene"] <- 1e-3
## m1.pg1.a <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m1.pg2.a <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m1.pg1.c <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m1.pg2.c <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m1.pg1.b <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "oreoisasabgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## expect_true(medianNClones(m1.pg1.a) < medianNClones(m1.pg2.a))
## ## next can fail sometimes. Similar to why
## ## expect_true(medianNClones(m2.pg1.a) < medianNClones(m2.pg2.a)) can fail
## ## but here, differences might even be smaller. So commented out.
## ## expect_true(medianNClones(m1.pg1.c) > medianNClones(m1.pg2.c))
## expect_true(medianNClones(m1.pg1.a) > medianNClones(m1.pg1.c))
## expect_true(medianNClones(m1.pg2.a) > medianNClones(m1.pg2.c))
## expect_true(medianNClones(m1.pg1.a) > medianNClones(m1.pg1.b))
## m2.pg1.a <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m2,
## finalTime = ft,
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m2.pg2.a <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m2,
## finalTime = ft,
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m2.pg1.c <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m2,
## finalTime = ft,
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m2.pg2.c <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m2,
## finalTime = ft,
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## ## next test sometimes fails; it can, as we use a small difference in total mut. rate.
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m2.pg2.a))
## expect_true(medianNClones(m2.pg1.c) > medianNClones(m2.pg2.c))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m2.pg2.a) < medianNClones(m2.pg2.c))
## expect_true(medianNClones(m1.pg1.c) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m1.pg1.c) < medianNClones(m2.pg2.c))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m1.pg1.a))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m1.pg2.a))
## expect_true(medianNClones(m2.pg2.a) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m1.pg2.a) > medianNClones(m1.pg1.c))
## expect_true(smAnomPi(m2.pg1.a, "hereisoneagene") <
## smAnomPi(m2.pg1.c, "nnhsisthecgene"))
## expect_true(smAnomPi(m2.pg1.a, "hereisoneagene") <
## smAnomPi(m1.pg1.a, "hereisoneagene"))
## })
## date()
## date()
## test_that("McFL: Num clones: Mutator and var mut rates and init and really scrambled names, mutPropGrowt", {
## pseed <- sample(1:100, 1)
##
## cat("\n x5: a runif is", runif(1), "\n")
## pops <- 200
## ft <- .051
## lni <- 200
## no <- 1e5
## ni <- c(0, 0, 0, rep(0, lni))
## ## scramble around names
## names(ni) <- c("hereisoneagene",
## "oreoisasabgene",
## "nnhsisthecgene",
## replicate(lni,
## paste(sample(letters, 12), collapse = "")))
## ni <- ni[order(names(ni))]
## fe <- allFitnessEffects(noIntGenes = ni)
## mutator1 <- mutator2 <- rep(1, lni + 3)
## pg1 <- pg2 <- rep(1e-6, lni + 3)
## ## scramble names of mutator and per-gene too
## names(mutator1) <- sample(names(ni))
## names(mutator2) <- sample(names(ni))
## names(pg1) <- sample(names(ni))
## names(pg2) <- sample(names(ni))
## mutator1["hereisoneagene"] <- 100
## mutator2["hereisoneagene"] <- 1
## mutator1["oreoisasabgene"] <- 1
## mutator2["oreoisasabgene"] <- 0.01
## mutator1["nnhsisthecgene"] <- 0.01
## mutator2["nnhsisthecgene"] <- 100
## m1 <- allMutatorEffects(noIntGenes = mutator1)
## m2 <- allMutatorEffects(noIntGenes = mutator2)
## pg1["hereisoneagene"] <- 1e-3
## pg2["hereisoneagene"] <- 1e-14
## pg1["oreoisasabgene"] <- 1e-7
## pg2["oreoisasabgene"] <- 1e-7
## pg1["nnhsisthecgene"] <- 1e-14
## pg2["nnhsisthecgene"] <- 1e-3
## m1.pg1.a <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m1.pg2.a <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m1,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m1.pg1.c <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m1.pg2.c <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m1,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m1.pg1.b <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "oreoisasabgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## expect_true(medianNClones(m1.pg1.a) < medianNClones(m1.pg2.a))
## ## Again, too tiny diffs to be detectable with reasonable time
## ## expect_true(medianNClones(m1.pg1.c) > medianNClones(m1.pg2.c))
## expect_true(medianNClones(m1.pg1.a) > medianNClones(m1.pg1.c))
## expect_true(medianNClones(m1.pg2.a) > medianNClones(m1.pg2.c))
## expect_true(medianNClones(m1.pg1.a) > medianNClones(m1.pg1.b))
## m2.pg1.a <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m2,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m2.pg2.a <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m2,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m2.pg1.c <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m2,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m2.pg2.c <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m2,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m2.pg2.a))
## expect_true(medianNClones(m2.pg1.c) > medianNClones(m2.pg2.c))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m2.pg2.a) < medianNClones(m2.pg2.c))
## expect_true(medianNClones(m1.pg1.c) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m1.pg1.c) < medianNClones(m2.pg2.c))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m1.pg1.a))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m1.pg2.a))
## expect_true(medianNClones(m2.pg2.a) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m1.pg2.a) > medianNClones(m1.pg1.c))
## ## some of these differences are actually tiny, as the largest clone
## ## is the initial one, with a single mutation, which is the same for
## ## both in many cases
## ## expect_true(smA(m2.pg1.a) < smA(m2.pg1.c))
## ## expect_true(smA(m2.pg1.a) < smA(m1.pg1.a))
## expect_true(smAnomPi(m2.pg1.a, "hereisoneagene") <
## smAnomPi(m2.pg1.c, "nnhsisthecgene"))
## expect_true(smAnomPi(m2.pg1.a, "hereisoneagene") <
## smAnomPi(m1.pg1.a, "hereisoneagene"))
## })
## date()
## ## This is probably getting silly. Too many of the same thing.
## ## And done more cleanly in "Init with different mutators"
## test_that("Yet another test of ordering, add scrambling", {
## pseed <- sample(6789:26789, 1)
##
## cat("\n u5: a runif is", runif(1), "\n")
## pops <- 10
## ft <- 1
## lni <- 100
## no <- 1e5
## ni <- c(0.1, 0.01, 0.1, rep(0, lni))
## ## scramble around names
## names(ni) <- c("hereisoneagene",
## "oreoisasabgene",
## "nnhsisthecgene",
## replicate(lni,
## paste(sample(letters, 12), collapse = "")))
## ni <- ni[order(names(ni))]
## fe <- allFitnessEffects(noIntGenes = ni)
## pg1 <- runif(lni + 3, min = 1e-8, max = 1e-6)
## names(pg1) <- sample(names(ni))
## m1 <- allMutatorEffects(noIntGenes = c("oreoisasabgene" = 5))
## m1.pg1.b <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant ="oreoisasabgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## expect_true(sm("oreoisasabgene", m1.pg1.b) == totalind(m1.pg1.b))
## m1.pg1.b
## m2 <- allMutatorEffects(noIntGenes = c("oreoisasabgene" = 20))
## m2.pg1.b <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m2,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant ="oreoisasabgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## expect_true(sm("oreoisasabgene", m2.pg1.b) == totalind(m2.pg1.b))
## m2.pg1.b
## expect_true(medianNClones(m2.pg1.b) > medianNClones(m1.pg1.b))
## ## the next is swamped again by the initMutant gene
## ## expect_true(smA(m2.pg1.b) > smA(m1.pg1.b))
## expect_true(smAnomPi(m2.pg1.b, "oreoisasabgene") >
## smAnomPi(m1.pg1.b, "oreoisasabgene"))
## })
## date()
## ## Too convoluted, and sampling at end covered by oncoSimulSample
## date()
## test_that("Mutator, several modules differences, sample at end only, and smaller effect", {
## ## we also change the effect (smaller) and finalTime (longer)
## ## you can increase ln, but then occasionally will take very long.
## ## or increase my, or increase m1, etc.
## pseed <- sample(99999999, 1)
##
## cat("\n l-mmd2: a runif is", runif(1), "\n")
## reps <- 10
## no <- 5e3
## ft <- 1000 ## you need it large enough to get enough hits
## mu <- 1e-5
## ln <- 20
## m1 <- 2 ## if this is too large, easy to get it to blow.
## ni <- rep(0, 3 * ln)
## gna <- paste0("a", 1:ln)
## gnb <- paste0("b", 1:ln)
## gnc <- paste0("c", 1:ln)
## names(ni) <- c(gna, gnb, gnc)
## gn1 <- paste(c(gna, gnb, gnc), collapse = ", ")
## gna <- paste(gna, collapse = ", ")
## gnb <- paste(gnb, collapse = ", ")
## gnc <- paste(gnc, collapse = ", ")
## mut1 <- allMutatorEffects(epistasis = c("A" = m1),
## geneToModule = c("A" = gn1))
## mut2 <- allMutatorEffects(epistasis = c("A" = m1,
## "B" = m1,
## "C" = m1),
## geneToModule = c("A" = gna,
## "B" = gnb,
## "C" = gnc))
## f1 <- allFitnessEffects(noIntGenes = ni)
## b1 <- oncoSimulPop(reps,
## f1,
## mu = mu,
## muEF = mut1,
## onlyCancer = FALSE, detectionProb = NA,
## initSize = no,
## finalTime = ft,
## seed = NULL, keepEvery = ft
## )
## gc()
## b2 <- oncoSimulPop(reps,
## f1,
## mu = mu,
## muEF = mut2,
## onlyCancer = FALSE, detectionProb = NA,
## initSize = no,
## finalTime = ft,
## seed = NULL, keepEvery = ft
## )
## gc()
## summary(b2)[, c(1:3, 8:9)]
## summary(b1)[, c(1:3, 8:9)]
## mean(mutsPerClone(b2))
## mean(mutsPerClone(b1))
## ## This is, of course, affected by sampling only at end: we do not see
## ## the many intermediate events.
## expect_true( median(summary(b2)$NumClones) >
## median(summary(b1)$NumClones))
## expect_true( mean(mutsPerClone(b2)) >
## mean(mutsPerClone(b1)))
## })
## date()
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## This test takes about 12 to 13 minutes in my laptop
cat(paste("\n Starting test.mutator-long.R test at", date()))
cat(paste("\n a runif ", runif(1), "\n"))
## RNGkind("L'Ecuyer-CMRG") ## for the mclapplies
date()
mutsPerClone <- function(x, per.pop.mean = TRUE) {
perCl <- function(z)
unlist(lapply(z$GenotypesWDistinctOrderEff, length))
perCl2 <- function(z)
mean(unlist(lapply(z$GenotypesWDistinctOrderEff, length)))
if(per.pop.mean)
unlist(lapply(x, function(u) perCl2(u)))
else
lapply(x, function(u) perCl(u))
}
## Minifunctions for testing
medianNClones <- function(x) {
median(summary(x)$NumClones)
}
NClones <- function(x) {
summary(x)$NumClones
}
enom <- function(name, mu, ni = no, pp = pops) {
## expected without a given name for init
ii <- which(names(mu) == name)
out <- ni * pp * mu[-ii]
out[order(names(out))]
}
pnom <- function(name, mu, ni = no, pp = pops) {
ee <- enom(name, mu, ni, pp)
ee/sum(ee)
}
snom <- function(name, out) {
## observed without the init
cs <- colSums(OncoSimulR:::geneCounts(out))
ii <- which(names(cs) == name)
cs <- cs[-ii]
cs[order(names(cs))]
}
sm <- function(name, out) {
## totals for a given gene
cs <- colSums(OncoSimulR:::geneCounts(out))
ii <- which(names(cs) == name)
cs[ii]
}
totalind <- function(out) {
## total num indivs
sum(unlist(lapply(out, function(x) x$TotalPopSize)))
}
smA <- function(out) {
## totals counts for all. So total mutated over all.
cs <- colSums(OncoSimulR:::geneCounts(out))
sum(cs)
}
smAPi <- function(out) {
## smAnom but divided by number of individuals.
smA(out)/totalind(out)
}
smAnom <- function(out, name) {
## totals counts for all. So total mutated over all.
## but remove one, the fixed starting one.
cs <- colSums(OncoSimulR:::geneCounts(out))
ii <- which(names(cs) == name)
cs <- cs[-ii]
sum(cs)
}
smAnomPi <- function(out, name) {
## smAnom but divided by number of individuals.
smAnom(out, name)/totalind(out)
}
p.value.threshold <- 0.001
cat(paste("\n a second runif ", runif(1), "\n"))
date()
test_that("MCFL Relative ordering of number of clones with init mutant of mutator effects", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## Here we stop on finalTime, not popSize
## Can occasionally blow up with pE.f: pE not finite.
cat("\n mcx2bc: a runif is", runif(1), "\n")
pops <- 50
ni <- rep(0.01, 50)
names(ni) <- c("a", "b", "c", "d", paste0("n", 1:46))
fe <- allFitnessEffects(noIntGenes = ni)
fm6 <- allMutatorEffects(noIntGenes = c("a" = .05,
"b" = 1,
"c" = 10,
"d" = 50))
nca <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "a",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2, model = "McFL")
ncb <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "b",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2, model = "McFL")
ncc <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "c",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2, model = "McFL")
ncd <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "d",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2, model = "McFL")
T1 <- ( wilcox.test(summary(nca)$NumClones,
summary(ncb)$NumClones, alternative = "less")$p.value < p.value.threshold)
T2 <- (wilcox.test(summary(ncb)$NumClones ,
summary(ncc)$NumClones, alternative = "less")$p.value < p.value.threshold)
T3 <- ( wilcox.test(summary(ncc)$NumClones,
summary(ncd)$NumClones, alternative = "less")$p.value < p.value.threshold)
summary(nca)[, c(1:3, 8:9)]
summary(ncb)[, c(1:3, 8:9)]
summary(ncc)[, c(1:3, 8:9)]
summary(ncd)[, c(1:3, 8:9)]
T4 <- (t.test(mutsPerClone(nca), mutsPerClone(ncb), alternative = "less")$p.value < p.value.threshold)
T5 <- (t.test(mutsPerClone(ncb), mutsPerClone(ncc), alternative = "less")$p.value < p.value.threshold)
T6 <- (t.test(mutsPerClone(ncc), mutsPerClone(ncd), alternative = "less")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that(" Init with different mutators", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
cat("\n z2: a runif is", runif(1), "\n")
pops <- 60
ft <- .005
lni <- 50
no <- 1e7
ni <- c(0, 0, 0, rep(0, lni))
## scramble around names
names(ni) <- c("hereisoneagene",
"oreoisasabgene",
"nnhsisthecgene",
replicate(lni,
paste(sample(letters, 12), collapse = "")))
ni <- ni[order(names(ni))]
fe <- allFitnessEffects(noIntGenes = ni)
mutator1 <- mutator2 <- rep(1, lni + 3)
pg1 <- rep(5e-6, lni + 3)
## scramble names of mutator and per-gene too
names(mutator1) <- sample(names(ni))
names(pg1) <- sample(names(ni))
mutator1["hereisoneagene"] <- 100
mutator1["oreoisasabgene"] <- 1
mutator1["nnhsisthecgene"] <- 0.01
m1 <- allMutatorEffects(noIntGenes = mutator1)
m1.pg1.a <- oncoSimulPop(pops,
fe,
mu = pg1,
muEF = m1,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,
initMutant = "hereisoneagene",
sampleEvery = 0.01, keepEvery = 5, seed = NULL,
onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
m1.pg1.b <- oncoSimulPop(pops,
fe,
mu = pg1,
muEF = m1,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,
initMutant = "oreoisasabgene",
sampleEvery = 0.01, keepEvery = 5, seed = NULL,
onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
m1.pg1.c <- oncoSimulPop(pops,
fe,
mu = pg1,
muEF = m1,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,
initMutant = "nnhsisthecgene",
sampleEvery = 0.01, keepEvery = 5, seed = NULL,
onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
T1 <- (wilcox.test(NClones(m1.pg1.a), NClones(m1.pg1.b), alternative = "greater")$p.value < p.value.threshold)
T2 <- (wilcox.test(NClones(m1.pg1.b), NClones(m1.pg1.c), alternative = "greater")$p.value < p.value.threshold)
T3 <- (t.test(mutsPerClone(m1.pg1.a), mutsPerClone(m1.pg1.b), alternative = "greater")$p.value < p.value.threshold)
T4 <- (t.test(mutsPerClone(m1.pg1.b), mutsPerClone(m1.pg1.c), alternative = "greater")$p.value < p.value.threshold)
T5 <- (smAnomPi(m1.pg1.a, "hereisoneagene") >
smAnomPi(m1.pg1.b, "oreoisasabgene"))
T6 <- (smAnomPi(m1.pg1.b, "oreoisasabgene") >
smAnomPi(m1.pg1.c, "nnhsisthecgene"))
summary(m1.pg1.a)[, c(1:3, 8:9)]
summary(m1.pg1.b)[, c(1:3, 8:9)]
summary(m1.pg1.c)[, c(1:3, 8:9)]
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("Same mu vector, different mutator; diffs in number muts, tiny t", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## Here, there is no reproduction or death. Just mutation. And no double
## mutants either.
## We test:
## - mutator increases mutation rates as seen in:
## - number of clones created
## - number of total mutation events
cat("\n nm0: a runif is", runif(1), "\n")
pops <- 40
ft <- .0001
lni <- 100
no <- 1e7
fi <- rep(0, lni)
muvector <- rep(5e-6, lni)
## scrambling names
names(fi) <- replicate(lni,
paste(sample(letters, 12), collapse = ""))
names(muvector) <- sample(names(fi))
## choose something for mutator
mutator10 <- mutator100 <- fi[5]
mutator10[] <- 10
mutator100[] <- 100
fe <- allFitnessEffects(noIntGenes = fi)
m10 <- allMutatorEffects(noIntGenes = mutator10)
m100 <- allMutatorEffects(noIntGenes = mutator100)
pop10 <- oncoSimulPop(pops,
fe,
mu = muvector,
muEF = m10,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,
initMutant = names(mutator10),
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
pop100 <- oncoSimulPop(pops,
fe,
mu = muvector,
muEF = m100,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,
initMutant = names(mutator10),
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## number of total mutations
T1 <- (smAnomPi(pop10, names(mutator10)) < smAnomPi(pop100, names(mutator100)))
## number of clones
T2 <- (wilcox.test(NClones(pop100), NClones(pop10), alternative = "greater")$p.value < p.value.threshold)
T3 <- (t.test(mutsPerClone(pop100), mutsPerClone(pop10), alternative = "greater")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("Same mu vector, different mutator; diffs in number muts, larger t", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## reproduction, death, and double and possibly triple mutants. We
## decrease init pop size to make this fast.
cat("\n nm1: a runif is", runif(1), "\n")
pops <- 40
ft <- 1
lni <- 100
no <- 1e5
fi <- rep(0, lni)
muvector <- rep(5e-6, lni)
## scrambling names
names(fi) <- replicate(lni,
paste(sample(letters, 12), collapse = ""))
names(muvector) <- sample(names(fi))
## choose something for mutator
mutator10 <- mutator100 <- fi[5]
mutator10[] <- 10
mutator100[] <- 100
fe <- allFitnessEffects(noIntGenes = fi)
m10 <- allMutatorEffects(noIntGenes = mutator10)
m100 <- allMutatorEffects(noIntGenes = mutator100)
pop10 <- oncoSimulPop(pops,
fe,
mu = muvector,
muEF = m10,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,
initMutant = names(mutator10),
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
pop100 <- oncoSimulPop(pops,
fe,
mu = muvector,
muEF = m100,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,
initMutant = names(mutator10),
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## number of total mutations
T1 <- (smAnomPi(pop10, names(mutator10)) < smAnomPi(pop100, names(mutator100)))
## number of clones
T2 <- (wilcox.test(NClones(pop100), NClones(pop10), alternative = "greater")$p.value < p.value.threshold)
T3 <- (t.test(mutsPerClone(pop100), mutsPerClone(pop10), alternative = "greater")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("Relative ordering of number of clones with mut prop growth and init and scrambled names", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## Can occasionally blow up with pE.f: pE not finite.
cat("\n x2ef: a runif is", runif(1), "\n")
pops <- 90
ft <- 1
lni <- 200
no <- 5e3
ni <- c(5, 2, rep(0, lni))
## scramble around names
names(ni) <- c("thisistheagene",
"thisisthebgene",
replicate(lni,
paste(sample(letters, 12), collapse = "")))
ni <- ni[order(names(ni))]
fe <- allFitnessEffects(noIntGenes = ni)
fm1 <- allMutatorEffects(noIntGenes = c("thisistheagene" = 5))
mpg <- oncoSimulPop(pops, fe, muEF = fm1,
finalTime = ft,
mutationPropGrowth = TRUE,
initSize = no, sampleEvery = 0.01,
initMutant = "thisistheagene",
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
mnpg <- oncoSimulPop(pops, fe, muEF = fm1,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,sampleEvery = 0.01,
initMutant = "thisistheagene",
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
pg <- oncoSimulPop(pops, fe,
finalTime = ft,
mutationPropGrowth = TRUE,
initSize = no, sampleEvery = 0.01,
initMutant = "thisistheagene",
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
npg <- oncoSimulPop(pops, fe,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no, sampleEvery = 0.01,
initMutant = "thisistheagene",
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## These are the real tests
T1 <- ( wilcox.test(summary(mpg)$NumClones,
summary(mnpg)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T2 <- (wilcox.test(summary(mpg)$NumClones,
summary(pg)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T3 <- ( wilcox.test(summary(mnpg)$NumClones,
summary(npg)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T4 <- ( wilcox.test(summary(pg)$NumClones,
summary(npg)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T5 <- (t.test(mutsPerClone(mpg), mutsPerClone(mnpg), alternative = "greater")$p.value < p.value.threshold)
T6 <- (t.test(mutsPerClone(mpg), mutsPerClone(pg), alternative = "greater")$p.value < p.value.threshold)
T7 <- (t.test(mutsPerClone(mnpg), mutsPerClone(npg), alternative = "greater")$p.value < p.value.threshold)
T8 <- (t.test(mutsPerClone(pg), mutsPerClone(npg), alternative = "greater")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("Expect freq genotypes, mutator and var mut rates", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## increase mutator, decrease max mu
cat("\n u8_1: a runif is", runif(1), "\n")
pops <- 120
ft <- .0001
lni <- 80
no <- 5e7
ni <- c(0, 0, 0, rep(0, lni))
## scramble around names
names(ni) <- c("hereisoneagene",
"oreoisasabgene",
"nnhsisthecgene",
replicate(lni,
paste(sample(letters, 12), collapse = "")))
ni <- ni[order(names(ni))]
fe <- allFitnessEffects(noIntGenes = ni)
mutator1 <- rep(1, lni + 3)
pg1 <- runif(lni + 3, min = 1e-9, max = 1e-5) ## max should not be
## huge here as mutator
## is 34. Can get beyond
## 1
names(mutator1) <- sample(names(ni))
names(pg1) <- sample(names(ni))
mutator1["oreoisasabgene"] <- 200
m1 <- allMutatorEffects(noIntGenes = mutator1)
pg1["hereisoneagene"] <- 1e-3 ## 1e-3
m1.pg1.b <- oncoSimulPop(pops,
fe,
mu = pg1,
muEF = m1,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,
initMutant ="oreoisasabgene",
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
expect_true(sm("oreoisasabgene", m1.pg1.b) == totalind(m1.pg1.b))
## If you want to see the numbers
## enom("oreoisasabgene", pg1)
## snom("oreoisasabgene", m1.pg1.b)
p.fail <- 1e-3
T1 <- (chisq.test(snom("oreoisasabgene", m1.pg1.b),
p = pnom("oreoisasabgene", pg1, no, pops))$p.value > p.fail)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("Relative ordering of number of clones with init mutant of mutator effects and s = 0", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## Can occasionally blow up with pE.f: pE not finite.
cat("\n x2cd: a runif is", runif(1), "\n")
pops <- 50
ni <- rep(0, 50)
names(ni) <- c("a", "b", "c", "d", paste0("n", 1:46))
fe <- allFitnessEffects(noIntGenes = ni)
fm6 <- allMutatorEffects(noIntGenes = c("a" = .05,
"b" = 1,
"c" = 10,
"d" = 50))
nca <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "a",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
ncb <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "b",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
ncc <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "c",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
ncd <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "d",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## I once saw a weird thing
expect_true(var(summary(nca)$NumClones) > 1e-4)
expect_true(var(summary(ncb)$NumClones) > 1e-4)
expect_true(var(summary(ncc)$NumClones) > 1e-4)
expect_true(var(summary(ncd)$NumClones) > 1e-4)
## These are the real tests
T1 <- ( wilcox.test(summary(nca)$NumClones,
summary(ncb)$NumClones, alternative = "less")$p.value < p.value.threshold)
T2 <- (wilcox.test(summary(ncb)$NumClones,
summary(ncc)$NumClones, alternative = "less")$p.value < p.value.threshold)
T3 <- ( wilcox.test(summary(ncc)$NumClones,
summary(ncd)$NumClones, alternative = "less")$p.value < p.value.threshold)
T4 <- (t.test(mutsPerClone(nca), mutsPerClone(ncb), alternative = "less")$p.value < p.value.threshold)
T5 <- (t.test(mutsPerClone(ncb), mutsPerClone(ncc), alternative = "less")$p.value < p.value.threshold)
T6 <- (t.test(mutsPerClone(ncc), mutsPerClone(ncd), alternative = "less")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("Relative ordering of number of clones with init mutant of mutator effects", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## Here we stop on finalTime, not popSize
## Can occasionally blow up with pE.f: pE not finite.
cat("\n x2bc: a runif is", runif(1), "\n")
pops <- 60
ni <- rep(0.01, 50)
names(ni) <- c("a", "b", "c", "d", paste0("n", 1:46))
fe <- allFitnessEffects(noIntGenes = ni)
fm6 <- allMutatorEffects(noIntGenes = c("a" = .05,
"b" = 1,
"c" = 10,
"d" = 50))
nca <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "a",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
ncb <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "b",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
ncc <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "c",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
ncd <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "d",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
T1 <- ( wilcox.test(summary(nca)$NumClones,
summary(ncb)$NumClones, alternative = "less")$p.value < p.value.threshold)
T2 <- (wilcox.test(summary(ncb)$NumClones,
summary(ncc)$NumClones, alternative = "less")$p.value < p.value.threshold)
T3 <- ( wilcox.test(summary(ncc)$NumClones,
summary(ncd)$NumClones, alternative = "less")$p.value < p.value.threshold)
T4 <- (t.test(mutsPerClone(nca), mutsPerClone(ncb), alternative = "less")$p.value < p.value.threshold)
T5 <- (t.test(mutsPerClone(ncb), mutsPerClone(ncc), alternative = "less")$p.value < p.value.threshold)
T6 <- (t.test(mutsPerClone(ncc), mutsPerClone(ncd), alternative = "less")$p.value < p.value.threshold)
summary(nca)[, c(1:3, 8:9)]
summary(ncb)[, c(1:3, 8:9)]
summary(ncc)[, c(1:3, 8:9)]
summary(ncd)[, c(1:3, 8:9)]
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("MCFL Relative ordering of number of clones with mutator effects", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## Can occasionally blow up with pE.f: pE not finite.
cat("\n mcx1: a runif is", runif(1), "\n")
pops <- 60
fe <- allFitnessEffects(noIntGenes = c("a" = 0.12,
"b" = 0.14,
"c" = 0.16,
"d" = 0.11))
fm6 <- allMutatorEffects(noIntGenes = c("a" = 30,
"b" = 30,
"c" = 30,
"d" = 30))
nc1 <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =100,
mutationPropGrowth = FALSE,
sampleEvery = 0.001,
keepEvery = 5,
initSize = 1e6, mc.cores = 2, model = "McFL",
onlyCancer = FALSE, detectionProb = NA, seed = NULL)
fm8 <- allMutatorEffects(noIntGenes = c("a" = 1,
"b" = 1,
"c" = 1,
"d" = 1))
nc2 <- oncoSimulPop(pops, fe, muEF = fm8, finalTime =100,
mutationPropGrowth = FALSE,
sampleEvery = 0.001,
keepEvery = 5,
initSize = 1e6, mc.cores = 2, model = "McFL",
onlyCancer = FALSE, detectionProb = NA, seed = NULL)
fm7 <- allMutatorEffects(noIntGenes = c("a" = 1e-3,
"b" = 1e-3,
"c" = 1e-3,
"d" = 1e-3))
nc3 <- oncoSimulPop(pops, fe, muEF = fm7, finalTime =100,
mutationPropGrowth = FALSE,
sampleEvery = 0.001,
keepEvery = 5,
initSize = 1e6, mc.cores = 2, model = "McFL",
onlyCancer = FALSE, detectionProb = NA, seed = NULL)
T1 <- (wilcox.test(summary(nc1)$NumClones, summary(nc2)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T2 <- (wilcox.test(summary(nc2)$NumClones, summary(nc3)$NumClones, alternative = "greater")$p.value < p.value.threshold)
summary(nc1)[, c(1:3, 8:9)]
summary(nc2)[, c(1:3, 8:9)]
summary(nc3)[, c(1:3, 8:9)]
T3 <- (t.test(mutsPerClone(nc1), mutsPerClone(nc2), alternative = "greater")$p.value < p.value.threshold)
T4 <- (t.test(mutsPerClone(nc2), mutsPerClone(nc3), alternative = "greater")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("MCFL Relative ordering of number of clones with init mutant of mutator effects and s = 0", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## Can occasionally blow up with pE.f: pE not finite.
cat("\n mcx2cd: a runif is", runif(1), "\n")
pops <- 60
ni <- rep(0, 50)
names(ni) <- c("a", "b", "c", "d", paste0("n", 1:46))
fe <- allFitnessEffects(noIntGenes = ni)
fm6 <- allMutatorEffects(noIntGenes = c("a" = .05,
"b" = 1,
"c" = 10,
"d" = 50))
nca <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "a",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2, model = "McFL")
ncb <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "b",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2, model = "McFL")
ncc <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "c",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2, model = "McFL")
ncd <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =50,
mutationPropGrowth = FALSE,
initSize = 1e4,
initMutant = "d",
sampleEvery = 0.01,
keepEvery = 5,
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2, model = "McFL")
## I once saw a weird thing
expect_true(var(summary(nca)$NumClones) > 1e-4)
expect_true(var(summary(ncb)$NumClones) > 1e-4)
expect_true(var(summary(ncc)$NumClones) > 1e-4)
expect_true(var(summary(ncd)$NumClones) > 1e-4)
## These are the real tests
T1 <- ( wilcox.test(summary(nca)$NumClones,
summary(ncb)$NumClones, alternative = "less")$p.value < p.value.threshold)
T2 <- (wilcox.test(summary(ncb)$NumClones,
summary(ncc)$NumClones, alternative = "less")$p.value < p.value.threshold)
T3 <- ( wilcox.test(summary(ncc)$NumClones,
summary(ncd)$NumClones, alternative = "less")$p.value < p.value.threshold)
T4 <- (t.test(mutsPerClone(nca), mutsPerClone(ncb), alternative = "less")$p.value < p.value.threshold)
T5 <- (t.test(mutsPerClone(ncb), mutsPerClone(ncc), alternative = "less")$p.value < p.value.threshold)
T6 <- (t.test(mutsPerClone(ncc), mutsPerClone(ncd), alternative = "less")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
## Slow (~ 5 seconds) but tests modules of mutator nicely.
date()
test_that("Mutator modules differences", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
cat("\n mmd1: a runif is", runif(1), "\n")
reps <- 60
no <- 5e3
ft <- 100
mu <- 1e-5
lni <- 50
m1 <- 1
m2 <- 25
m3 <- 50
ni <- rep(0, lni)
gn <- paste0("a", 1:lni)
names(ni) <- gn
gn <- paste(gn, collapse = ", ")
mut1 <- allMutatorEffects(epistasis = c("A" = m1),
geneToModule = c("A" = gn))
mut2 <- allMutatorEffects(epistasis = c("A" = m2),
geneToModule = c("A" = gn))
mut3 <- allMutatorEffects(epistasis = c("A" = m3),
geneToModule = c("A" = gn))
f1 <- allFitnessEffects(noIntGenes = ni)
b1 <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut1,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
gc()
b2 <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut2,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
gc()
b3 <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut3,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
gc()
summary(b3)[, c(1:3, 8:9)]
summary(b2)[, c(1:3, 8:9)]
summary(b1)[, c(1:3, 8:9)]
## mean(mutsPerClone(b3))
## mean(mutsPerClone(b2))
## mean(mutsPerClone(b1))
## This is, of course, affected by sampling only at end: we do not see
## the many intermediate events.
T1 <- ( wilcox.test(summary(b3)$NumClones,
summary(b2)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T2 <- ( wilcox.test(summary(b2)$NumClones,
summary(b1)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T3 <- (t.test(mutsPerClone(b3), mutsPerClone(b2), alternative = "greater")$p.value < p.value.threshold)
T4 <- (t.test(mutsPerClone(b2), mutsPerClone(b1), alternative = "greater")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("McFL: Mutator modules differences", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
cat("\n MCFLmmd1: a runif is", runif(1), "\n")
reps <- 60
no <- 5e3
ft <- 100
mu <- 1e-5
lni <- 50
m1 <- 1
m2 <- 25
m3 <- 50
ni <- rep(0, lni)
gn <- paste0("a", 1:lni)
names(ni) <- gn
gn <- paste(gn, collapse = ", ")
mut1 <- allMutatorEffects(epistasis = c("A" = m1),
geneToModule = c("A" = gn))
mut2 <- allMutatorEffects(epistasis = c("A" = m2),
geneToModule = c("A" = gn))
mut3 <- allMutatorEffects(epistasis = c("A" = m3),
geneToModule = c("A" = gn))
f1 <- allFitnessEffects(noIntGenes = ni)
b1 <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut1,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5, model = "McFL",
seed = NULL, mc.cores = 2
)
gc()
b2 <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut2,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5, model = "McFL",
seed = NULL, mc.cores = 2
)
gc()
b3 <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut3,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5, model = "McFL",
seed = NULL, mc.cores = 2
)
gc()
summary(b3)[, c(1:3, 8:9)]
summary(b2)[, c(1:3, 8:9)]
summary(b1)[, c(1:3, 8:9)]
## mean(mutsPerClone(b3))
## mean(mutsPerClone(b2))
## mean(mutsPerClone(b1))
## This is, of course, affected by sampling only at end: we do not see
## the many intermediate events.
T1 <- ( wilcox.test(summary(b3)$NumClones,
summary(b2)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T2 <- ( wilcox.test(summary(b2)$NumClones,
summary(b1)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T3 <- (t.test(mutsPerClone(b3), mutsPerClone(b2), alternative = "greater")$p.value < p.value.threshold)
T4 <- (t.test(mutsPerClone(b2), mutsPerClone(b1), alternative = "greater")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("Relative ordering of number of clones with mutator effects", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## Can occasionally blow up with pE.f: pE not finite.
cat("\n x1-910: a runif is", runif(1), "\n")
pops <- 80
fe <- allFitnessEffects(noIntGenes = c("a" = 0.12,
"b" = 0.14,
"c" = 0.16,
"d" = 0.11))
fm6 <- allMutatorEffects(noIntGenes = c("a" = 5,
"b" = 10,
"c" = 12,
"d" = 14))
nc1 <- oncoSimulPop(pops, fe, muEF = fm6, finalTime =250,
mutationPropGrowth = FALSE,
sampleEvery = 0.001,
keepEvery = 5,
initSize = 1e6, mc.cores = 2,
onlyCancer = FALSE, detectionProb = NA, seed = NULL)
fm8 <- allMutatorEffects(noIntGenes = c("a" = 1,
"b" = 1,
"c" = 1,
"d" = 1))
nc2 <- oncoSimulPop(pops, fe, muEF = fm8, finalTime =250,
mutationPropGrowth = FALSE,
sampleEvery = 0.001,
keepEvery = 5,
initSize = 1e6, mc.cores = 2,
onlyCancer = FALSE, detectionProb = NA, seed = NULL)
fm7 <- allMutatorEffects(noIntGenes = c("a" = 1e-3,
"b" = 1e-3,
"c" = 1e-3,
"d" = 1e-3))
nc3 <- oncoSimulPop(pops, fe, muEF = fm7, finalTime =250,
mutationPropGrowth = FALSE,
sampleEvery = 0.001,
keepEvery = 5,
initSize = 1e6, mc.cores = 2,
onlyCancer = FALSE, detectionProb = NA, seed = NULL)
T1 <- (wilcox.test(summary(nc1)$NumClones, summary(nc2)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T2 <- (wilcox.test(summary(nc2)$NumClones, summary(nc3)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T3 <- (t.test(mutsPerClone(nc1), mutsPerClone(nc2), alternative = "greater")$p.value < p.value.threshold)
T4 <- (t.test(mutsPerClone(nc2), mutsPerClone(nc3), alternative = "greater")$p.value < p.value.threshold)
summary(nc1)[, c(1:3, 8:9)]
summary(nc2)[, c(1:3, 8:9)]
summary(nc3)[, c(1:3, 8:9)]
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
## very slow, because huge number of clones. But tests several phenomena comprehensively.
## same with McFL below
date()
test_that("per-gene-mut rates and mutator", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
cat("\n oss11-x970: a runif is", runif(1), "\n")
ng <- 10
ni <- rep(0, ng)
m1 <- runif(ng, min = 1e-7, max = 5e-6)
m2 <- runif(ng, min = 1e-5, max = 1e-4)
names(ni) <- names(m1) <- names(m2) <- c(replicate(ng,
paste(sample(letters, 12), collapse = "")))
fe1 <- allFitnessEffects(noIntGenes = ni)
ft <- 50
no <- 5e5
reps <- 100
gn <- paste(names(ni), collapse = ", ")
## MUs used to be 25 and 100. Way too slow.
mutator1 <- allMutatorEffects(epistasis = c("MU" = 15),
geneToModule = c("MU" = gn))
mutator2 <- allMutatorEffects(epistasis = c("MU" = 30),
geneToModule = c("MU" = gn))
m1.mutator0 <- oncoSimulPop(reps,
fe1,
mu = m1,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
m1.mutator1 <- oncoSimulPop(reps,
fe1,
mu = m1,
muEF = mutator1,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
runif(1)
m1.mutator2 <- oncoSimulPop(reps,
fe1,
mu = m1,
muEF = mutator2,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
m2.mutator0 <- oncoSimulPop(reps,
fe1,
mu = m2,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
m2.mutator1 <- oncoSimulPop(reps,
fe1,
mu = m2,
muEF = mutator1,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
m2.mutator2 <- oncoSimulPop(reps,
fe1,
mu = m2,
muEF = mutator2,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
summary(m1.mutator0)[, c(1:3, 8:9)]
summary(m1.mutator1)[, c(1:3, 8:9)]
summary(m1.mutator2)[, c(1:3, 8:9)]
summary(m2.mutator0)[, c(1:3, 8:9)]
summary(m2.mutator1)[, c(1:3, 8:9)]
summary(m2.mutator2)[, c(1:3, 8:9)]
## Mutator increases if larger mutator and compared to no mutator
## within levels of per-gene mutation rates
T1 <- ( wilcox.test(summary(m1.mutator2)$NumClones,
summary(m1.mutator1)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T2 <- ( wilcox.test(summary(m1.mutator1)$NumClones,
summary(m1.mutator0)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T3 <- ( wilcox.test(summary(m2.mutator2)$NumClones,
summary(m2.mutator1)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T4 <- ( wilcox.test(summary(m2.mutator1)$NumClones,
summary(m2.mutator0)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T5 <- (t.test(mutsPerClone(m1.mutator2), mutsPerClone(m1.mutator1), alternative = "greater")$p.value < p.value.threshold)
T6 <- (t.test(mutsPerClone(m1.mutator1), mutsPerClone(m1.mutator0), alternative = "greater")$p.value < p.value.threshold)
T7 <- (t.test(mutsPerClone(m2.mutator2), mutsPerClone(m2.mutator1), alternative = "greater")$p.value < p.value.threshold)
T8 <- (t.test(mutsPerClone(m2.mutator1), mutsPerClone(m2.mutator0), alternative = "greater")$p.value < p.value.threshold)
## Increases in mutation rates increase clones, etc, within levels of
## mutator.
Ta <- ( wilcox.test(summary(m1.mutator0)$NumClones,
summary(m2.mutator0)$NumClones, alternative = "less")$p.value < p.value.threshold)
Tb <- ( wilcox.test(summary(m1.mutator1)$NumClones,
summary(m2.mutator1)$NumClones, alternative = "less")$p.value < p.value.threshold)
Tc <- ( wilcox.test(summary(m1.mutator2)$NumClones,
summary(m2.mutator2)$NumClones, alternative = "less")$p.value < p.value.threshold)
Td <- (t.test(mutsPerClone(m1.mutator0), mutsPerClone(m2.mutator0), alternative = "less")$p.value < p.value.threshold)
Te <- (t.test(mutsPerClone(m1.mutator1), mutsPerClone(m2.mutator1), alternative = "less")$p.value < p.value.threshold)
Tf <- (t.test(mutsPerClone(m1.mutator2), mutsPerClone(m2.mutator2), alternative = "less")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8 &&
Ta && Tb && Tc && Td && Te && Tf) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8 &&
Ta && Tb && Tc && Td && Te && Tf)
})
date()
date()
test_that("McFL: per-gene-mut rates and mutator", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
cat("\n mcfloss11: a runif is", runif(1), "\n")
ng <- 10
ni <- rep(0, ng)
m1 <- runif(ng, min = 1e-7, max = 5e-6)
m2 <- runif(ng, min = 1e-5, max = 1e-4)
names(ni) <- names(m1) <- names(m2) <- c(replicate(ng,
paste(sample(letters, 12), collapse = "")))
fe1 <- allFitnessEffects(noIntGenes = ni)
ft <- 50
no <- 5e5
reps <- 150
gn <- paste(names(ni), collapse = ", ")
mutator1 <- allMutatorEffects(epistasis = c("MU" = 15),
geneToModule = c("MU" = gn))
mutator2 <- allMutatorEffects(epistasis = c("MU" = 30),
geneToModule = c("MU" = gn))
m1.mutator0 <- oncoSimulPop(reps,
fe1,
mu = m1,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2, model = "McFL"
)
m1.mutator1 <- oncoSimulPop(reps,
fe1,
mu = m1,
muEF = mutator1,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2, model = "McFL"
)
m1.mutator2 <- oncoSimulPop(reps,
fe1,
mu = m1,
muEF = mutator2,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2, model = "McFL"
)
cat("\n starting m2\n")
m2.mutator0 <- oncoSimulPop(reps,
fe1,
mu = m2,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2, model = "McFL"
)
m2.mutator1 <- oncoSimulPop(reps,
fe1,
mu = m2,
muEF = mutator1,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2, model = "McFL"
)
m2.mutator2 <- oncoSimulPop(reps,
fe1,
mu = m2,
muEF = mutator2,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2, model = "McFL"
)
summary(m1.mutator0)[, c(1:3, 8:9)]
summary(m1.mutator1)[, c(1:3, 8:9)]
summary(m1.mutator2)[, c(1:3, 8:9)]
summary(m2.mutator0)[, c(1:3, 8:9)]
summary(m2.mutator1)[, c(1:3, 8:9)]
summary(m2.mutator2)[, c(1:3, 8:9)]
## Mutator increases if larger mutator and compared to no mutator
## within levels of per-gene mutation rates
T1 <- ( wilcox.test(summary(m1.mutator2)$NumClones,
summary(m1.mutator1)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T2 <- ( wilcox.test(summary(m1.mutator1)$NumClones,
summary(m1.mutator0)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T3 <- ( wilcox.test(summary(m2.mutator2)$NumClones,
summary(m2.mutator1)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T4 <- ( wilcox.test(summary(m2.mutator1)$NumClones,
summary(m2.mutator0)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T5 <- (t.test(mutsPerClone(m1.mutator2), mutsPerClone(m1.mutator1), alternative = "greater")$p.value < p.value.threshold)
T6 <- (t.test(mutsPerClone(m1.mutator1), mutsPerClone(m1.mutator0), alternative = "greater")$p.value < p.value.threshold)
T7 <- (t.test(mutsPerClone(m2.mutator2), mutsPerClone(m2.mutator1), alternative = "greater")$p.value < p.value.threshold)
T8 <- (t.test(mutsPerClone(m2.mutator1), mutsPerClone(m2.mutator0), alternative = "greater")$p.value < p.value.threshold)
## Increases in mutation rates increase clones, etc, within levels of
## mutator.
Ta <- ( wilcox.test(summary(m1.mutator0)$NumClones,
summary(m2.mutator0)$NumClones, alternative = "less")$p.value < p.value.threshold)
Tb <- ( wilcox.test(summary(m1.mutator1)$NumClones,
summary(m2.mutator1)$NumClones, alternative = "less")$p.value < p.value.threshold)
Tc <- ( wilcox.test(summary(m1.mutator2)$NumClones,
summary(m2.mutator2)$NumClones, alternative = "less")$p.value < p.value.threshold)
Td <- (t.test(mutsPerClone(m1.mutator0), mutsPerClone(m2.mutator0), alternative = "less")$p.value < p.value.threshold)
Te <- (t.test(mutsPerClone(m1.mutator1), mutsPerClone(m2.mutator1), alternative = "less")$p.value < p.value.threshold)
Tf <- (t.test(mutsPerClone(m1.mutator2), mutsPerClone(m2.mutator2), alternative = "less")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8 &&
Ta && Tb && Tc && Td && Te && Tf) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8 &&
Ta && Tb && Tc && Td && Te && Tf)
})
date()
date()
test_that("Mutator, several modules differences, McFL", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
cat("\n mmdSMMC1: a runif is", runif(1), "\n")
reps <- 50
no <- 5e3
ft <- 50 ## you need it large enough to get enough hits
mu <- 1e-5
ln <- 50
m1 <- 5 ## if this is too large, easy to get it to blow.
ni <- rep(0, 3 * ln)
gna <- paste0("a", 1:ln)
gnb <- paste0("b", 1:ln)
gnc <- paste0("c", 1:ln)
names(ni) <- c(gna, gnb, gnc)
gn1 <- paste(c(gna, gnb, gnc), collapse = ", ")
gna <- paste(gna, collapse = ", ")
gnb <- paste(gnb, collapse = ", ")
gnc <- paste(gnc, collapse = ", ")
mut1 <- allMutatorEffects(epistasis = c("A" = m1),
geneToModule = c("A" = gn1))
mut2 <- allMutatorEffects(epistasis = c("A" = m1,
"B" = m1,
"C" = m1),
geneToModule = c("A" = gna,
"B" = gnb,
"C" = gnc))
f1 <- allFitnessEffects(noIntGenes = ni)
b1 <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut1,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
detectionSize = 1e9,
detectionDrivers = 9999,
seed = NULL, mc.cores = 2, model = "McFL"
)
gc()
b2 <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut2,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
sampleEvery = 0.01,
detectionSize = 1e9,
detectionDrivers = 9999,
seed = NULL, mc.cores = 2, model = "McFL"
)
gc()
summary(b2)[, c(1:3, 8:9)]
summary(b1)[, c(1:3, 8:9)]
mean(mutsPerClone(b2))
mean(mutsPerClone(b1))
## This is, of course, affected by sampling only at end: we do not see
## the many intermediate events.
T1 <- ( wilcox.test(summary(b2)$NumClones,
summary(b1)$NumClones, alternative = "greater")$p.value < p.value.threshold)
T2 <- (t.test(mutsPerClone(b2), mutsPerClone(b1), alternative = "greater")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("McFL: Expect freq genotypes, mutator and var mut rates", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## increase mutator
cat("\n u8: a runif is", runif(1), "\n")
pops <- 300
ft <- .0001
lni <- 80
no <- 5e7
ni <- c(0, 0, 0, rep(0, lni))
## scramble around names
names(ni) <- c("hereisoneagene",
"oreoisasabgene",
"nnhsisthecgene",
replicate(lni,
paste(sample(letters, 12), collapse = "")))
ni <- ni[order(names(ni))]
fe <- allFitnessEffects(noIntGenes = ni)
pg1 <- runif(lni + 3, min = 1e-7, max = 1e-4) ## max should not be
## huge here as mutator
## is 34. Can get beyond
## 1
names(pg1) <- sample(names(ni))
mutator1 <- c("oreoisasabgene" = 50)
m1 <- allMutatorEffects(noIntGenes = mutator1)
pg1["hereisoneagene"] <- 1e-3 ## to compare with a laarge one
m1.pg1.b <- oncoSimulPop(pops,
fe,
mu = pg1,
muEF = m1,
model = "McFL",
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,
initMutant ="oreoisasabgene",
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
expect_true(sm("oreoisasabgene", m1.pg1.b) == totalind(m1.pg1.b))
## enom("oreoisasabgene", pg1)
## snom("oreoisasabgene", m1.pg1.b)
p.fail <- 1e-3
T1 <- (chisq.test(snom("oreoisasabgene", m1.pg1.b),
p = pnom("oreoisasabgene", pg1, no, pops))$p.value > p.fail)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("Expect freq genotypes, mutator and var mut rates", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## Similar to above, but mutator has a single element, not the whole
## vector.
cat("\n u7_1: a runif is", runif(1), "\n")
pops <- 300
ft <- .0001
lni <- 80
no <- 5e7
ni <- c(0, 0, 0, rep(0, lni))
## scramble around names
names(ni) <- c("hereisoneagene",
"oreoisasabgene",
"nnhsisthecgene",
replicate(lni,
paste(sample(letters, 12), collapse = "")))
ni <- ni[order(names(ni))]
fe <- allFitnessEffects(noIntGenes = ni)
pg1 <- runif(lni + 3, min = 1e-7, max = 1e-4) ## max should not be
## huge here as mutator
## is 34. Can get beyond
## 1
names(pg1) <- sample(names(ni))
mutator1 <- c("oreoisasabgene" = 50)
m1 <- allMutatorEffects(noIntGenes = mutator1)
pg1["hereisoneagene"] <- 1e-3 ## to compare with a laarge one
m1.pg1.b <- oncoSimulPop(pops,
fe,
mu = pg1,
muEF = m1,
finalTime = ft,
mutationPropGrowth = FALSE,
initSize = no,
initMutant ="oreoisasabgene",
onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
expect_true(sm("oreoisasabgene", m1.pg1.b) == totalind(m1.pg1.b))
enom("oreoisasabgene", pg1, no, pops)
snom("oreoisasabgene", m1.pg1.b)
p.fail <- 1e-3
T1 <- (chisq.test(snom("oreoisasabgene", m1.pg1.b),
p = pnom("oreoisasabgene", pg1, no, pops))$p.value > p.fail)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
date()
test_that("Mutator and mutPropGrowth", {
max.tries <- 4
for(tries in 1:max.tries) {
T1 <- T2 <- T3 <- T4 <- T5 <- T6 <- T7 <- T8 <- TRUE
## we stop on size
cat("\n mmpg: a runif is", runif(1), "\n")
reps <- 200
no <- 5e3
ds <- 7e4
ft <- 1000
mu <- 1e-5
ln <- 60
m1 <- 1 ## if this is too large, easy to get it to blow.
m50 <- 50
gna <- paste0("a", 1:ln)
gna <- paste(gna, collapse = ", ")
f1 <- allFitnessEffects(epistasis = c("A" = 0.4),
geneToModule = c("A" = gna))
mut1 <- allMutatorEffects(epistasis = c("M" = m1),
geneToModule = c("M" = gna))
mut50 <- allMutatorEffects(epistasis = c("M" = m50),
geneToModule = c("M" = gna))
m1.pg <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut1,
mutationPropGrowth = TRUE,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
detectionSize = ds,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
## gc()
m1.npg <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut1,
mutationPropGrowth = FALSE,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
detectionSize = ds,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
##gc()
m50.pg <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut50,
mutationPropGrowth = TRUE,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
detectionSize = ds,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
## gc()
m50.npg <- oncoSimulPop(reps,
f1,
mu = mu,
muEF = mut50,
mutationPropGrowth = FALSE,
onlyCancer = FALSE, detectionProb = NA,
initSize = no,
finalTime = ft,
detectionSize = ds,
sampleEvery = 0.01,
keepEvery = 5,
seed = NULL, mc.cores = 2
)
##gc()
summary(m1.pg)[, c(1:3, 8:9)]
summary(m50.pg)[, c(1:3, 8:9)]
summary(m1.npg)[, c(1:3, 8:9)]
summary(m50.npg)[, c(1:3, 8:9)]
## Over mutator, as we have mutPropGrowth, clones, etc, increase
T1 <- ( wilcox.test(summary(m1.pg)$NumClones,
summary(m1.npg)$NumClones,
alternative = "greater")$p.value < p.value.threshold)
T2 <- (t.test(mutsPerClone(m1.pg),
mutsPerClone(m1.npg),
alternative = "greater")$p.value < p.value.threshold)
T3 <- ( wilcox.test(summary(m50.pg)$NumClones,
summary(m50.npg)$NumClones,
alternative = "greater")$p.value < p.value.threshold)
T4 <- (t.test(mutsPerClone(m50.pg),
mutsPerClone(m50.npg),
alternative = "greater")$p.value < p.value.threshold)
## Over mutPropGrowth, as we increase mutator, clones, etc, increase
T5 <- ( wilcox.test(summary(m50.pg)$NumClones,
summary(m1.pg)$NumClones,
alternative = "greater")$p.value < p.value.threshold)
T6 <- (t.test(mutsPerClone(m50.pg),
mutsPerClone(m1.pg),
alternative = "greater")$p.value < p.value.threshold)
T7 <- ( wilcox.test(summary(m50.npg)$NumClones,
summary(m1.npg)$NumClones,
alternative = "greater")$p.value < p.value.threshold)
T8 <- (t.test(mutsPerClone(m50.npg),
mutsPerClone(m1.npg),
alternative = "greater")$p.value < p.value.threshold)
if(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8) break;
}
cat(paste("\n done tries", tries, "\n"))
expect_true(T1 && T2 && T3 && T4 && T5 && T6 && T7 && T8)
})
date()
cat(paste("\n Finished test.mutator-long.R test at", date(), "\n"))
## ### These are too convoluted and trying to pick too tiny diffs. Substitute
## ### by diffs initMutant and by per-gene-mut rates and mutator
## date()
## test_that("Num clones: Mutator and var mut rates and init and really scrambled names", {
##
##
## cat("\n z2: a runif is", runif(1), "\n")
## pops <- 40
## ft <- .005
## lni <- 50
## no <- 1e7
## ni <- c(0, 0, 0, rep(0, lni))
## ## scramble around names
## names(ni) <- c("hereisoneagene",
## "oreoisasabgene",
## "nnhsisthecgene",
## replicate(lni,
## paste(sample(letters, 12), collapse = "")))
## ni <- ni[order(names(ni))]
## fe <- allFitnessEffects(noIntGenes = ni)
## mutator1 <- mutator2 <- rep(1, lni + 3)
## pg1 <- pg2 <- rep(5e-6, lni + 3)
## ## scramble names of mutator and per-gene too
## names(mutator1) <- sample(names(ni))
## names(mutator2) <- sample(names(ni))
## names(pg1) <- sample(names(ni))
## names(pg2) <- sample(names(ni))
## mutator1["hereisoneagene"] <- 100
## mutator2["hereisoneagene"] <- 1
## mutator1["oreoisasabgene"] <- 1
## mutator2["oreoisasabgene"] <- 0.01
## mutator1["nnhsisthecgene"] <- 0.01
## mutator2["nnhsisthecgene"] <- 100
## m1 <- allMutatorEffects(noIntGenes = mutator1)
## m2 <- allMutatorEffects(noIntGenes = mutator2)
## pg1["hereisoneagene"] <- 1e-3
## pg2["hereisoneagene"] <- 1e-14
## pg1["oreoisasabgene"] <- 1e-7
## pg2["oreoisasabgene"] <- 1e-7
## pg1["nnhsisthecgene"] <- 1e-14
## pg2["nnhsisthecgene"] <- 1e-3
## m1.pg1.a <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m1.pg2.a <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m1.pg1.c <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m1.pg2.c <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m1.pg1.b <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "oreoisasabgene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## expect_true(medianNClones(m1.pg1.a) < medianNClones(m1.pg2.a))
## ## Too tiny diffs
## ## expect_true(medianNClones(m1.pg1.c) > medianNClones(m1.pg2.c))
## expect_true(medianNClones(m1.pg1.a) > medianNClones(m1.pg1.c))
## expect_true(medianNClones(m1.pg2.a) > medianNClones(m1.pg2.c))
## expect_true(medianNClones(m1.pg1.a) > medianNClones(m1.pg1.b))
## m2.pg1.a <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m2,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m2.pg2.a <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m2,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m2.pg1.c <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m2,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m2.pg2.c <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m2,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## ## too tiny
## ## expect_true(medianNClones(m2.pg1.a) < medianNClones(m2.pg2.a))
## expect_true(medianNClones(m2.pg1.c) > medianNClones(m2.pg2.c))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m2.pg2.a) < medianNClones(m2.pg2.c))
## expect_true(medianNClones(m1.pg1.c) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m1.pg1.c) < medianNClones(m2.pg2.c))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m1.pg1.a))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m1.pg2.a))
## expect_true(medianNClones(m2.pg2.a) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m1.pg2.a) > medianNClones(m1.pg1.c))
## expect_true(smAnomPi(m2.pg1.a, "hereisoneagene") <
## smAnomPi(m2.pg1.c, "nnhsisthecgene"))
## expect_true(smAnomPi(m2.pg1.a, "hereisoneagene") <
## smAnomPi(m1.pg1.a, "hereisoneagene"))
## })
## date()
## ## This is slow and very fragile, because unless we want to increase pops
## ## a lots, we can often miss differences in
## ## medianNClones(m1.pg1.a) < medianNClones(m1.pg2.a)
## ## and
## ## medianNClones(m1.pg1.c) > medianNClones(m1.pg2.c)
## ## and
## ## medianNClones(m2.pg1.c) > medianNClones(m2.pg2.c)
## date()
## test_that("McFL: Num clones: Mutator and var mut rates and init and really scrambled names", {
## pseed <- sample(300:400, 1)
##
## cat("\n x3: a runif is", runif(1), "\n")
## pops <- 80
## ft <- .002
## lni <- 20
## no <- 3e5
## ni <- c(0, 0, 0, rep(0, lni))
## ## scramble around names
## names(ni) <- c("hereisoneagene",
## "oreoisasabgene",
## "nnhsisthecgene",
## replicate(lni,
## paste(sample(letters, 12), collapse = "")))
## ni <- ni[order(names(ni))]
## fe <- allFitnessEffects(noIntGenes = ni)
## mutator1 <- mutator2 <- rep(1, lni + 3)
## pg1 <- pg2 <- rep(1e-4, lni + 3)
## ## this: runif(lni + 3, min = 1e-6, max = 1e-5)
## ## adds too much variation in mutation rates between runs
## ## scramble names of mutator and per-gene too
## names(mutator1) <- sample(names(ni))
## names(mutator2) <- sample(names(ni))
## names(pg1) <- sample(names(ni))
## names(pg2) <- sample(names(ni))
## mutator1["hereisoneagene"] <- 50
## mutator2["hereisoneagene"] <- 5 # 1
## mutator1["oreoisasabgene"] <- 1
## mutator2["oreoisasabgene"] <- 0.01
## mutator1["nnhsisthecgene"] <- 1 # 0.5
## mutator2["nnhsisthecgene"] <- 50
## m1 <- allMutatorEffects(noIntGenes = mutator1)
## m2 <- allMutatorEffects(noIntGenes = mutator2)
## pg1["hereisoneagene"] <- 5e-3
## pg2["hereisoneagene"] <- 5e-15
## pg1["oreoisasabgene"] <- 1e-7
## pg2["oreoisasabgene"] <- 1e-7
## pg1["nnhsisthecgene"] <- 5e-15
## pg2["nnhsisthecgene"] <- 5e-3
## m1.pg1.a <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m1.pg2.a <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m1,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m1.pg1.c <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m1.pg2.c <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m1,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m1.pg1.b <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "oreoisasabgene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## expect_true(medianNClones(m1.pg1.a) < medianNClones(m1.pg2.a))
## ## Too tiny diffs
## ## expect_true(medianNClones(m1.pg1.c) > medianNClones(m1.pg2.c))
## expect_true(medianNClones(m1.pg1.a) > medianNClones(m1.pg1.c))
## expect_true(medianNClones(m1.pg2.a) > medianNClones(m1.pg2.c))
## expect_true(medianNClones(m1.pg1.a) > medianNClones(m1.pg1.b))
## m2.pg1.a <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m2,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m2.pg2.a <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m2,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m2.pg1.c <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m2,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## m2.pg2.c <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m2,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, mc.cores = 2)
## ## too small.
## ## expect_true(medianNClones(m2.pg1.a) < medianNClones(m2.pg2.a))
## expect_true(medianNClones(m2.pg1.c) > medianNClones(m2.pg2.c))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m2.pg2.a) < medianNClones(m2.pg2.c))
## expect_true(medianNClones(m1.pg1.c) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m1.pg1.c) < medianNClones(m2.pg2.c))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m1.pg1.a))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m1.pg2.a))
## expect_true(medianNClones(m2.pg2.a) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m1.pg2.a) > medianNClones(m1.pg1.c))
## expect_true(smAnomPi(m2.pg1.a, "hereisoneagene") <
## smAnomPi(m2.pg1.c, "nnhsisthecgene"))
## expect_true(smAnomPi(m2.pg1.a, "hereisoneagene") <
## smAnomPi(m1.pg1.a, "hereisoneagene"))
## })
## date()
## ## Tiny diffs often missed. Again, culprits are often
## ## * Not expected: medianNClones(m1.pg1.a) < medianNClones(m1.pg2.a) isn't true.
## ## * Not expected: medianNClones(m1.pg1.c) > medianNClones(m1.pg2.c) isn't true.
## ## * Not expected: medianNClones(m2.pg1.c) > medianNClones(m2.pg2.c) isn't true.
## date()
## test_that("Num clones: Mutator and var mut rates and init and really scrambled names, mutPropgrowth", {
## pseed <- sample(1:100, 1)
##
## cat("\n x4: a runif is", runif(1), "\n")
## pops <- 100 ## the more, the better, but takes long.
## ft <- .001
## lni <- 30 ## 200
## no <- 1e7
## ni <- c(0, 0, 0, rep(0, lni))
## ## scramble around names
## names(ni) <- c("hereisoneagene",
## "oreoisasabgene",
## "nnhsisthecgene",
## replicate(lni,
## paste(sample(letters, 12), collapse = "")))
## ni <- ni[order(names(ni))]
## fe <- allFitnessEffects(noIntGenes = ni)
## mutator1 <- mutator2 <- rep(1, lni + 3)
## pg1 <- pg2 <- rep(1e-5, lni + 3)
## ## scramble names of mutator and per-gene too
## names(mutator1) <- sample(names(ni))
## names(mutator2) <- sample(names(ni))
## names(pg1) <- sample(names(ni))
## names(pg2) <- sample(names(ni))
## mutator1["hereisoneagene"] <- 100
## mutator2["hereisoneagene"] <- 1
## mutator1["oreoisasabgene"] <- 1
## mutator2["oreoisasabgene"] <- 0.01
## mutator1["nnhsisthecgene"] <- 0.5
## mutator2["nnhsisthecgene"] <- 100
## m1 <- allMutatorEffects(noIntGenes = mutator1)
## m2 <- allMutatorEffects(noIntGenes = mutator2)
## pg1["hereisoneagene"] <- 1e-3
## pg2["hereisoneagene"] <- 1e-14
## pg1["oreoisasabgene"] <- 1e-7
## pg2["oreoisasabgene"] <- 1e-7
## pg1["nnhsisthecgene"] <- 1e-14
## pg2["nnhsisthecgene"] <- 1e-3
## m1.pg1.a <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m1.pg2.a <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m1.pg1.c <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m1.pg2.c <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m1.pg1.b <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "oreoisasabgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## expect_true(medianNClones(m1.pg1.a) < medianNClones(m1.pg2.a))
## ## next can fail sometimes. Similar to why
## ## expect_true(medianNClones(m2.pg1.a) < medianNClones(m2.pg2.a)) can fail
## ## but here, differences might even be smaller. So commented out.
## ## expect_true(medianNClones(m1.pg1.c) > medianNClones(m1.pg2.c))
## expect_true(medianNClones(m1.pg1.a) > medianNClones(m1.pg1.c))
## expect_true(medianNClones(m1.pg2.a) > medianNClones(m1.pg2.c))
## expect_true(medianNClones(m1.pg1.a) > medianNClones(m1.pg1.b))
## m2.pg1.a <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m2,
## finalTime = ft,
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m2.pg2.a <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m2,
## finalTime = ft,
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m2.pg1.c <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m2,
## finalTime = ft,
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m2.pg2.c <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m2,
## finalTime = ft,
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## ## next test sometimes fails; it can, as we use a small difference in total mut. rate.
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m2.pg2.a))
## expect_true(medianNClones(m2.pg1.c) > medianNClones(m2.pg2.c))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m2.pg2.a) < medianNClones(m2.pg2.c))
## expect_true(medianNClones(m1.pg1.c) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m1.pg1.c) < medianNClones(m2.pg2.c))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m1.pg1.a))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m1.pg2.a))
## expect_true(medianNClones(m2.pg2.a) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m1.pg2.a) > medianNClones(m1.pg1.c))
## expect_true(smAnomPi(m2.pg1.a, "hereisoneagene") <
## smAnomPi(m2.pg1.c, "nnhsisthecgene"))
## expect_true(smAnomPi(m2.pg1.a, "hereisoneagene") <
## smAnomPi(m1.pg1.a, "hereisoneagene"))
## })
## date()
## date()
## test_that("McFL: Num clones: Mutator and var mut rates and init and really scrambled names, mutPropGrowt", {
## pseed <- sample(1:100, 1)
##
## cat("\n x5: a runif is", runif(1), "\n")
## pops <- 200
## ft <- .051
## lni <- 200
## no <- 1e5
## ni <- c(0, 0, 0, rep(0, lni))
## ## scramble around names
## names(ni) <- c("hereisoneagene",
## "oreoisasabgene",
## "nnhsisthecgene",
## replicate(lni,
## paste(sample(letters, 12), collapse = "")))
## ni <- ni[order(names(ni))]
## fe <- allFitnessEffects(noIntGenes = ni)
## mutator1 <- mutator2 <- rep(1, lni + 3)
## pg1 <- pg2 <- rep(1e-6, lni + 3)
## ## scramble names of mutator and per-gene too
## names(mutator1) <- sample(names(ni))
## names(mutator2) <- sample(names(ni))
## names(pg1) <- sample(names(ni))
## names(pg2) <- sample(names(ni))
## mutator1["hereisoneagene"] <- 100
## mutator2["hereisoneagene"] <- 1
## mutator1["oreoisasabgene"] <- 1
## mutator2["oreoisasabgene"] <- 0.01
## mutator1["nnhsisthecgene"] <- 0.01
## mutator2["nnhsisthecgene"] <- 100
## m1 <- allMutatorEffects(noIntGenes = mutator1)
## m2 <- allMutatorEffects(noIntGenes = mutator2)
## pg1["hereisoneagene"] <- 1e-3
## pg2["hereisoneagene"] <- 1e-14
## pg1["oreoisasabgene"] <- 1e-7
## pg2["oreoisasabgene"] <- 1e-7
## pg1["nnhsisthecgene"] <- 1e-14
## pg2["nnhsisthecgene"] <- 1e-3
## m1.pg1.a <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m1.pg2.a <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m1,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m1.pg1.c <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m1.pg2.c <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m1,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m1.pg1.b <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "oreoisasabgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## expect_true(medianNClones(m1.pg1.a) < medianNClones(m1.pg2.a))
## ## Again, too tiny diffs to be detectable with reasonable time
## ## expect_true(medianNClones(m1.pg1.c) > medianNClones(m1.pg2.c))
## expect_true(medianNClones(m1.pg1.a) > medianNClones(m1.pg1.c))
## expect_true(medianNClones(m1.pg2.a) > medianNClones(m1.pg2.c))
## expect_true(medianNClones(m1.pg1.a) > medianNClones(m1.pg1.b))
## m2.pg1.a <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m2,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m2.pg2.a <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m2,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "hereisoneagene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m2.pg1.c <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m2,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## m2.pg2.c <- oncoSimulPop(pops,
## fe,
## mu = pg2,
## muEF = m2,
## finalTime = ft, model = "McFL",
## mutationPropGrowth = TRUE,
## initSize = no,
## initMutant = "nnhsisthecgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m2.pg2.a))
## expect_true(medianNClones(m2.pg1.c) > medianNClones(m2.pg2.c))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m2.pg2.a) < medianNClones(m2.pg2.c))
## expect_true(medianNClones(m1.pg1.c) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m1.pg1.c) < medianNClones(m2.pg2.c))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m1.pg1.a))
## expect_true(medianNClones(m2.pg1.a) < medianNClones(m1.pg2.a))
## expect_true(medianNClones(m2.pg2.a) < medianNClones(m2.pg1.c))
## expect_true(medianNClones(m1.pg2.a) > medianNClones(m1.pg1.c))
## ## some of these differences are actually tiny, as the largest clone
## ## is the initial one, with a single mutation, which is the same for
## ## both in many cases
## ## expect_true(smA(m2.pg1.a) < smA(m2.pg1.c))
## ## expect_true(smA(m2.pg1.a) < smA(m1.pg1.a))
## expect_true(smAnomPi(m2.pg1.a, "hereisoneagene") <
## smAnomPi(m2.pg1.c, "nnhsisthecgene"))
## expect_true(smAnomPi(m2.pg1.a, "hereisoneagene") <
## smAnomPi(m1.pg1.a, "hereisoneagene"))
## })
## date()
## ## This is probably getting silly. Too many of the same thing.
## ## And done more cleanly in "Init with different mutators"
## test_that("Yet another test of ordering, add scrambling", {
## pseed <- sample(6789:26789, 1)
##
## cat("\n u5: a runif is", runif(1), "\n")
## pops <- 10
## ft <- 1
## lni <- 100
## no <- 1e5
## ni <- c(0.1, 0.01, 0.1, rep(0, lni))
## ## scramble around names
## names(ni) <- c("hereisoneagene",
## "oreoisasabgene",
## "nnhsisthecgene",
## replicate(lni,
## paste(sample(letters, 12), collapse = "")))
## ni <- ni[order(names(ni))]
## fe <- allFitnessEffects(noIntGenes = ni)
## pg1 <- runif(lni + 3, min = 1e-8, max = 1e-6)
## names(pg1) <- sample(names(ni))
## m1 <- allMutatorEffects(noIntGenes = c("oreoisasabgene" = 5))
## m1.pg1.b <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m1,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant ="oreoisasabgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## expect_true(sm("oreoisasabgene", m1.pg1.b) == totalind(m1.pg1.b))
## m1.pg1.b
## m2 <- allMutatorEffects(noIntGenes = c("oreoisasabgene" = 20))
## m2.pg1.b <- oncoSimulPop(pops,
## fe,
## mu = pg1,
## muEF = m2,
## finalTime = ft,
## mutationPropGrowth = FALSE,
## initSize = no,
## initMutant ="oreoisasabgene",
## onlyCancer = FALSE, detectionProb = NA, seed = NULL, mc.cores = 2)
## expect_true(sm("oreoisasabgene", m2.pg1.b) == totalind(m2.pg1.b))
## m2.pg1.b
## expect_true(medianNClones(m2.pg1.b) > medianNClones(m1.pg1.b))
## ## the next is swamped again by the initMutant gene
## ## expect_true(smA(m2.pg1.b) > smA(m1.pg1.b))
## expect_true(smAnomPi(m2.pg1.b, "oreoisasabgene") >
## smAnomPi(m1.pg1.b, "oreoisasabgene"))
## })
## date()
## ## Too convoluted, and sampling at end covered by oncoSimulSample
## date()
## test_that("Mutator, several modules differences, sample at end only, and smaller effect", {
## ## we also change the effect (smaller) and finalTime (longer)
## ## you can increase ln, but then occasionally will take very long.
## ## or increase my, or increase m1, etc.
## pseed <- sample(99999999, 1)
##
## cat("\n l-mmd2: a runif is", runif(1), "\n")
## reps <- 10
## no <- 5e3
## ft <- 1000 ## you need it large enough to get enough hits
## mu <- 1e-5
## ln <- 20
## m1 <- 2 ## if this is too large, easy to get it to blow.
## ni <- rep(0, 3 * ln)
## gna <- paste0("a", 1:ln)
## gnb <- paste0("b", 1:ln)
## gnc <- paste0("c", 1:ln)
## names(ni) <- c(gna, gnb, gnc)
## gn1 <- paste(c(gna, gnb, gnc), collapse = ", ")
## gna <- paste(gna, collapse = ", ")
## gnb <- paste(gnb, collapse = ", ")
## gnc <- paste(gnc, collapse = ", ")
## mut1 <- allMutatorEffects(epistasis = c("A" = m1),
## geneToModule = c("A" = gn1))
## mut2 <- allMutatorEffects(epistasis = c("A" = m1,
## "B" = m1,
## "C" = m1),
## geneToModule = c("A" = gna,
## "B" = gnb,
## "C" = gnc))
## f1 <- allFitnessEffects(noIntGenes = ni)
## b1 <- oncoSimulPop(reps,
## f1,
## mu = mu,
## muEF = mut1,
## onlyCancer = FALSE, detectionProb = NA,
## initSize = no,
## finalTime = ft,
## seed = NULL, keepEvery = ft
## )
## gc()
## b2 <- oncoSimulPop(reps,
## f1,
## mu = mu,
## muEF = mut2,
## onlyCancer = FALSE, detectionProb = NA,
## initSize = no,
## finalTime = ft,
## seed = NULL, keepEvery = ft
## )
## gc()
## summary(b2)[, c(1:3, 8:9)]
## summary(b1)[, c(1:3, 8:9)]
## mean(mutsPerClone(b2))
## mean(mutsPerClone(b1))
## ## This is, of course, affected by sampling only at end: we do not see
## ## the many intermediate events.
## expect_true( median(summary(b2)$NumClones) >
## median(summary(b1)$NumClones))
## expect_true( mean(mutsPerClone(b2)) >
## mean(mutsPerClone(b1)))
## })
## date()
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