inittime <- Sys.time()
cat(paste("\n Starting init-mutant tests", date(), "\n"))
## RNGkind("Mersenne-Twister")
## Processing this file takes about 3 seconds on my laptop
test_that("initMutant crashes",
{
o3 <- allFitnessEffects(orderEffects = c(
"M > D > F" = 0.99,
"D > M > F" = 0.2,
"D > M" = 0.1,
"M > D" = 0.9),
noIntGenes = c(0.01, 0.01),
geneToModule =
c("Root" = "Root",
"M" = "m",
"F" = "f",
"D" = "d") )
expect_error(oncoSimulIndiv(o3, model = "McFL",
mu = 5e-5, finalTime = 500,
detectionDrivers = 3,
onlyCancer = FALSE,
initSize = 1000,
keepPhylog = TRUE
, initMutant = c("b, a") ## also "a" and "b" separate
),
"genes not in the fitness table")
expect_error(oncoSimulIndiv(o3, model = "McFL",
mu = 5e-5, finalTime = 500,
detectionDrivers = 3,
onlyCancer = FALSE,
initSize = 1000,
keepPhylog = TRUE
, initMutant = c("b", "a") ## also "a" and "b" separate
),
"genes not in the fitness table")
expect_error(oncoSimulIndiv(o3, model = "McFL",
mu = 5e-5, finalTime = 500,
detectionDrivers = 3,
onlyCancer = FALSE,
initSize = 1000,
keepPhylog = TRUE
, initMutant = c("1", "2") ## also "a" and "b" separate
),
"genes not in the fitness table")
})
test_that("initMutant lexicog order",
{
max.tries <- 4
for(tries in 1:max.tries) {
o3 <- allFitnessEffects(orderEffects = c(
"M > D > F" = 0.99,
"D > M > F" = 0.2,
"D > M" = 0.1,
"M > D" = 0.9),
noIntGenes = c(0.01, 0.01),
geneToModule =
c("Root" = "Root",
"M" = "m",
"F" = "f",
"D" = "d") )
tmp <- oncoSimulIndiv(o3, model = "McFL",
mu = 5e-5, finalTime = 500,
detectionDrivers = 3,
sampleEvery = 0.03,
keepEvery = 1,
onlyCancer = FALSE,
initSize = 1000,
keepPhylog = TRUE
, initMutant = c("d > m")
)
cn <- colnames(igraph::get.adjacency(plotClonePhylog(tmp, N = 0,
returnGraph = TRUE),
sparse = FALSE))
expect_true( "d > m _ " %in% cn )
expect_false( "m > d _" %in% cn )
expect_false( "m > d > f _" %in% cn )
## this next one will be true almost always
## if there is pop growth. But very occasionally
## it might not. The above must ALWAYS be true,
## but this one we allow to repeat a couple of times
T1 <- ( "d > m > f _ " %in% cn )
if( T1 ) break;
if( !T1 ) {
cat("\n pop in initMutant lexicog order\n ")
print(tmp)
}
}
})
test_that("initMutant lexicog order with noint",
{
max.tries <- 4
for(tries in 1:max.tries) {
o3 <- allFitnessEffects(orderEffects = c(
"M > D > F" = 0.99,
"D > M > F" = 0.2,
"D > M" = 0.1,
"M > D" = 0.9),
noIntGenes = c("u" = 0.01, "z" = 0.01),
geneToModule =
c("Root" = "Root",
"M" = "m",
"F" = "f",
"D" = "d") )
tmp <- oncoSimulIndiv(o3, model = "McFL",
mu = 5e-5, finalTime = 500,
detectionDrivers = 3,
sampleEvery = 0.03,
keepEvery = 1,
onlyCancer = FALSE,
initSize = 1000,
keepPhylog = TRUE
, initMutant = c("d > m > z")
)
cn <- colnames(igraph::get.adjacency(plotClonePhylog(tmp, N = 0,
returnGraph = TRUE),
sparse = FALSE))
expect_true( "d > m _ z" %in% cn )
expect_false( "m > d _" %in% cn )
expect_false( "m > d > f _" %in% cn )
T1 <- ( "d > m > f _ z" %in% cn )
## expect_true( "d, m_z" %in% cn )
## expect_true( "d, m, f_z" %in% cn )
## expect_false( "m, d_" %in% cn )
## expect_false( "m, d, f_" %in% cn )
if( T1 ) break;
if(! T1 ) {
cat("\n pop in initMutant lexicog order with no int\n ")
print(tmp)
}
}
## cat(paste("\n done tries", tries, "\n"))
expect_true(T1)
})
test_that("initMutant non lexicog order", {
max.tries <- 4
for(tries in 1:max.tries) {
o3 <- allFitnessEffects(orderEffects = c(
"M > D > F" = 0.99,
"D > M > F" = 0.2,
"D > M" = 0.1,
"M > D" = 0.9),
noIntGenes = c("u" = 0.01, "z" = 0.01),
geneToModule =
c("Root" = "Root",
"M" = "m",
"F" = "f",
"D" = "d") )
tmp <- oncoSimulIndiv(o3, model = "McFL",
mu = 5e-5, finalTime = 500,
detectionDrivers = 3,
sampleEvery = 0.03,
keepEvery = 1,
onlyCancer = FALSE,
initSize = 1000,
keepPhylog = TRUE
, initMutant = c("m > d")
)
cn <- colnames(igraph::get.adjacency(plotClonePhylog(tmp, N = 0,
returnGraph = TRUE),
sparse = FALSE))
expect_false( "d > m _" %in% cn )
expect_false( "d > m > f _" %in% cn )
expect_true( "m > d _ " %in% cn )
## this next one will be true almost always
## if there is pop growth. But very occasionally
## it might not. The above must ALWAYS be true,
## but this one we allow to repeat a couple of times
T1 <- ( "m > d > f _ " %in% cn )
if( T1 ) break;
if( !T1 ) {
cat("\n pop in initMutant non lexicog order\n ")
print(tmp)
}
}
## cat(paste("\n done tries", tries, "\n"))
expect_true(T1)
})
test_that("initMutant non lexicog order",
{
max.tries <- 4
for(tries in 1:max.tries) {
o3 <- allFitnessEffects(orderEffects = c(
"M > D > F" = 0.99,
"D > M > F" = 0.2,
"D > M" = 0.1,
"M > D" = 0.9),
noIntGenes = c("u" = 0.01, "z" = 0.01),
geneToModule =
c("Root" = "Root",
"M" = "m",
"F" = "f",
"D" = "d") )
tmp <- oncoSimulIndiv(o3, model = "McFL",
mu = 5e-5, finalTime = 500,
detectionDrivers = 3,
sampleEvery = 0.03,
keepEvery = 1,
onlyCancer = FALSE,
initSize = 1000,
keepPhylog = TRUE
, initMutant = c("m > u > d")
)
cn <- colnames(igraph::get.adjacency(plotClonePhylog(tmp, N = 0,
returnGraph = TRUE),
sparse = FALSE))
expect_false( "d > m _" %in% cn )
expect_false( "d > m > f _" %in% cn )
expect_true( "m > d _ u" %in% cn )
## this next one will be true almost always
## if there is pop growth. But very occasionally
## it might not. The above must ALWAYS be true,
## but this one we allow to repeat a couple of times
T1 <- ( "m > d > f _ u" %in% cn )
if( T1 ) break;
if(! T1 ) {
cat("\n pop in initMutant non lexicog order\n ")
print(tmp)
}
}
## cat(paste("\n done tries", tries, "\n"))
expect_true(T1)
})
## FIXME: we could use stronger test: we will never see M > D
test_that("initMutant with oncoSimulSample", {
o3init <- allFitnessEffects(orderEffects = c(
"M > D > F" = 0.99,
"D > M > F" = 0.2,
"D > M" = 0.1,
"M > D" = 0.9),
noIntGenes = c("u" = 0.01,
"v" = 0.01,
"w" = 0.001,
"x" = 0.0001,
"y" = -0.0001,
"z" = 0.001),
geneToModule =
c("Root" = "Root",
"M" = "m",
"F" = "f",
"D" = "d"),
drvNames = c("m", "f", "d"))
ossI <- oncoSimulSample(4,
o3init, model = "Exp",
mu = 5e-5, finalTime = 5000,
detectionDrivers = 2,
onlyCancer = TRUE,
initSize = 500,
sampleEvery = 0.03,
initMutant = c("z > d"),
thresholdWhole = 1 ## check presence of initMutant
)
ssp <- ossI$popSample
expect_equal(ssp[, c("d", "z")],
matrix(1, nrow = 4, ncol = 2,
dimnames = list(NULL, c("d", "z"))))
expect_false(sum(ssp) == prod(dim(ssp))) ## we don't just have all of
## them, which would turn the
## previous into irrelevant
expect_equal(grep("d",
as.character(ossI$popSummary$OccurringDrivers)), 1:4)
})
test_that("initMutant with oncoSimulSample, 2", {
o3init <- allFitnessEffects(orderEffects = c(
"M > D > F" = 0.99,
"D > M > F" = 0.2,
"D > M" = 0.1,
"M > D" = 0.9,
"M > A" = 0.25,
"A > H" = 0.2,
"A > G" = 0.3),
noIntGenes = c("u" = 0.1,
"v" = 0.2,
"w" = 0.001,
"x" = 0.0001,
"y" = -0.0001,
"z" = -0.001),
geneToModule =
c("Root" = "Root",
"A" = "a",
"M" = "m",
"F" = "f",
"D" = "d",
"H" = "h",
"G" = "g"),
drvNames = c("m", "f", "d", "a", "h", "g"))
ossI <- oncoSimulSample(4,
o3init, model = "Exp",
mu = 5e-5, finalTime = 5000,
detectionDrivers = 3,
sampleEvery = 0.03,
onlyCancer = TRUE,
initSize = 500,
initMutant = c("z > a"),
thresholdWhole = 1 ## check presence of initMutant
)
ssp <- ossI$popSample
expect_equal(ssp[, c("a", "z")],
matrix(1, nrow = 4, ncol = 2,
dimnames = list(NULL, c("a", "z"))))
expect_false(sum(ssp) == prod(dim(ssp))) ## we don't just have all of
## them, which would turn the
## previous into irrelevant
expect_equal(grep("a",
as.character(ossI$popSummary$OccurringDrivers)), 1:4)
})
test_that("initMutant with oncoSimulPop", {
o3init <- allFitnessEffects(orderEffects = c(
"M > D > F" = 0.99,
"D > M > F" = 0.2,
"D > M" = 0.2,
"M > D" = 0.9),
noIntGenes = c("u" = 0.01,
"v" = 0.01,
"w" = 0.001,
"x" = 0.0001,
"y" = -0.0001,
"z" = -0.001),
geneToModule =
c("Root" = "Root",
"M" = "m",
"F" = "f",
"D" = "d"),
drvNames = c("m", "f", "d"))
ospI <- oncoSimulPop(4,
o3init, model = "Exp",
mu = 5e-5, finalTime = 5000,
detectionDrivers = 3,
onlyCancer = TRUE,
keepPhylog = TRUE,
sampleEvery = 0.03,
keepEvery = 1,
initSize = 500,
initMutant = c("d > m > y"),
mc.cores = 2
)
genepos <- match(c("d", "m", "y"), ospI[[1]]$geneNames)
expect_true( all(
unlist(lapply(ospI,
function(x) x$Genotypes[genepos, , drop = FALSE])) == 1))
## make sure not all 1, by error, which would render previous useless
expect_false( all(
unlist(lapply(ospI,
function(x) x$Genotypes)) == 1))
expect_true(all(unlist(lapply(ospI,
function(x) (
lapply(x$GenotypesWDistinctOrderEff,
function(z) genepos %in% z))))))
## Like before, check no silly things
allgenepos <- seq_along(ospI[[1]]$geneNames)
expect_false(all(unlist(lapply(ospI,
function(x) (
lapply(x$GenotypesWDistinctOrderEff,
function(z) allgenepos %in% z))))))
expect_true(all(
lapply(ospI,
function(x)
as.character(x$other$PhylogDF[1, 1])) == "d > m _ y"))
})
test_that("initMutant with oncoSimulPop, 2", {
o3init <- allFitnessEffects(orderEffects = c(
"M > D > F" = 0.99,
"D > M > F" = 0.2,
"D > M" = 0.1,
"M > D" = 0.9),
noIntGenes = c("u" = 0.01,
"v" = 0.01,
"w" = 0.001,
"x" = 0.0001,
"y" = -0.0001,
"z" = -0.001),
geneToModule =
c("Root" = "Root",
"M" = "m",
"F" = "f",
"D" = "d"),
drvNames = c("m", "f", "d"))
ospI <- oncoSimulPop(4,
o3init, model = "Exp",
mu = 5e-5, finalTime = 70,
detectionDrivers = 4, ## yes, reach end
onlyCancer = FALSE,
keepPhylog = TRUE,
sampleEvery = 0.03,
keepEvery = 1,
initSize = 100,
initMutant = c("m > v > d"),
mc.cores = 2
)
genepos <- match(c("d", "m", "v"), ospI[[1]]$geneNames)
expect_true( all(
unlist(lapply(ospI,
function(x) x$Genotypes[genepos, , drop = FALSE])) == 1))
## make sure not all 1, by error, which would render previous useless
expect_false( all(
unlist(lapply(ospI,
function(x) x$Genotypes)) == 1))
expect_true(all(unlist(lapply(ospI,
function(x) (
lapply(x$GenotypesWDistinctOrderEff,
function(z) genepos %in% z))))))
## Like before, check no silly things
allgenepos <- seq_along(ospI[[1]]$geneNames)
expect_false(all(unlist(lapply(ospI,
function(x) (
lapply(x$GenotypesWDistinctOrderEff,
function(z) allgenepos %in% z))))))
expect_true(all(
lapply(ospI,
function(x)
as.character(x$other$PhylogDF[1, 1])) == "m > d _ v"))
## as.character(x$other$PhylogDF[1, 1])) == "m, d_v"))
})
test_that("initMutant with oncoSimulPop, McFL", {
o3init <- allFitnessEffects(orderEffects = c(
"M > D > F" = 0.99,
"D > M > F" = 0.2,
"D > M" = 0.2,
"M > D" = 0.9),
noIntGenes = c("u" = 0.01,
"v" = 0.01,
"w" = 0.001,
"x" = 0.0001,
"y" = -0.0001,
"z" = -0.001),
geneToModule =
c("Root" = "Root",
"M" = "m",
"F" = "f",
"D" = "d"),
drvNames = c("m", "f", "d"))
ospI <- oncoSimulPop(4,
o3init, model = "McFL",
mu = 5e-5, finalTime = 5000,
detectionDrivers = 3,
onlyCancer = TRUE,
keepPhylog = TRUE,
sampleEvery = 0.03,
keepEvery = 1,
detectionProb = NA,
initSize = 500,
initMutant = c("d > m > y"),
mc.cores = 2
)
genepos <- match(c("d", "m", "y"), ospI[[1]]$geneNames)
expect_true( all(
unlist(lapply(ospI,
function(x) x$Genotypes[genepos, , drop = FALSE])) == 1))
## make sure not all 1, by error, which would render previous useless
expect_false( all(
unlist(lapply(ospI,
function(x) x$Genotypes)) == 1))
expect_true(all(unlist(lapply(ospI,
function(x) (
lapply(x$GenotypesWDistinctOrderEff,
function(z) genepos %in% z))))))
## Like before, check no silly things
allgenepos <- seq_along(ospI[[1]]$geneNames)
expect_false(all(unlist(lapply(ospI,
function(x) (
lapply(x$GenotypesWDistinctOrderEff,
function(z) allgenepos %in% z))))))
expect_true(all(
lapply(ospI,
function(x)
as.character(x$other$PhylogDF[1, 1])) == "d > m _ y"))
})
test_that("initMutant with oncoSimulPop, Bozic", {
o3init <- allFitnessEffects(orderEffects = c(
"M > D > F" = 0.99,
"D > M > F" = 0.2,
"D > M" = 0.2,
"M > D" = 0.9),
noIntGenes = c("u" = 0.01,
"v" = 0.01,
"w" = 0.001,
"x" = 0.0001,
"y" = -0.0001,
"z" = -0.001),
geneToModule =
c("Root" = "Root",
"M" = "m",
"F" = "f",
"D" = "d"),
drvNames = c("m", "f", "d"))
ospI <- oncoSimulPop(4,
o3init, model = "Bozic",
mu = 5e-5, finalTime = 5000,
detectionDrivers = 3,
onlyCancer = TRUE,
keepPhylog = TRUE,
sampleEvery = 0.03,
keepEvery = 1,
initSize = 500,
initMutant = c("d > m > y"),
mc.cores = 2
)
genepos <- match(c("d", "m", "y"), ospI[[1]]$geneNames)
expect_true( all(
unlist(lapply(ospI,
function(x) x$Genotypes[genepos, , drop = FALSE])) == 1))
## make sure not all 1, by error, which would render previous useless
expect_false( all(
unlist(lapply(ospI,
function(x) x$Genotypes)) == 1))
expect_true(all(unlist(lapply(ospI,
function(x) (
lapply(x$GenotypesWDistinctOrderEff,
function(z) genepos %in% z))))))
## Like before, check no silly things
allgenepos <- seq_along(ospI[[1]]$geneNames)
expect_false(all(unlist(lapply(ospI,
function(x) (
lapply(x$GenotypesWDistinctOrderEff,
function(z) allgenepos %in% z))))))
expect_true(all(
lapply(ospI,
function(x)
as.character(x$other$PhylogDF[1, 1])) == "d > m _ y"))
})
test_that("initMutant with oncoSimulSample, 2, McFL", {
o3init <- allFitnessEffects(orderEffects = c(
"M > D > F" = 0.99,
"D > M > F" = 0.2,
"D > M" = 0.1,
"M > D" = 0.9,
"M > A" = 0.25,
"A > H" = 0.2,
"A > G" = 0.3),
noIntGenes = c("u" = 0.1,
"v" = 0.2,
"w" = 0.001,
"x" = 0.0001,
"y" = -0.0001,
"z" = -0.001),
geneToModule =
c("Root" = "Root",
"A" = "a",
"M" = "m",
"F" = "f",
"D" = "d",
"H" = "h",
"G" = "g"),
drvNames = c("m", "f", "d", "a", "h", "g")
)
ossI <- oncoSimulSample(4,
o3init, model = "McFL",
mu = 5e-5, finalTime = 5000,
detectionDrivers = 3,
sampleEvery = 0.03,
onlyCancer = TRUE,
initSize = 500,
initMutant = c("z > a"),
thresholdWhole = 1 ## check presence of initMutant
)
ssp <- ossI$popSample
expect_equal(ssp[, c("a", "z")],
matrix(1, nrow = 4, ncol = 2,
dimnames = list(NULL, c("a", "z"))))
expect_false(sum(ssp) == prod(dim(ssp))) ## we don't just have all of
## them, which would turn the
## previous into irrelevant
expect_equal(grep("a",
as.character(ossI$popSummary$OccurringDrivers)), 1:4)
})
test_that("initMutant with oncoSimulSample, 2, Bozic", {
o3init <- allFitnessEffects(orderEffects = c(
"M > D > F" = 0.99,
"D > M > F" = 0.2,
"D > M" = 0.1,
"M > D" = 0.9,
"M > A" = 0.25,
"A > H" = 0.2,
"A > G" = 0.3),
noIntGenes = c("u" = 0.1,
"v" = 0.2,
"w" = 0.001,
"x" = 0.0001,
"y" = -0.0001,
"z" = -0.001),
geneToModule =
c("Root" = "Root",
"A" = "a",
"M" = "m",
"F" = "f",
"D" = "d",
"H" = "h",
"G" = "g"),
drvNames = c("m", "f", "d", "a", "h", "g"))
ossI <- oncoSimulSample(4,
o3init, model = "Bozic",
mu = 5e-5, finalTime = 5000,
detectionDrivers = 3,
sampleEvery = 0.03,
onlyCancer = TRUE,
initSize = 500,
initMutant = c("z > a"),
thresholdWhole = 1 ## check presence of initMutant
)
ssp <- ossI$popSample
expect_equal(ssp[, c("a", "z")],
matrix(1, nrow = 4, ncol = 2,
dimnames = list(NULL, c("a", "z"))))
expect_false(sum(ssp) == prod(dim(ssp))) ## we don't just have all of
## them, which would turn the
## previous into irrelevant
expect_equal(grep("a",
as.character(ossI$popSummary$OccurringDrivers)), 1:4)
})
test_that("initMutant crashes if > number of genes", {
o1 <- allFitnessEffects(
noIntGenes = c("a" = .1, "b" = 0.2, "c" = 0.3))
expect_error(oncoSimulIndiv(o1, initMutant = "b, a, c, d"),
"For driver or initMutant you have passed genes not in the fitness table",
fixed = TRUE)
})
test_that("initMutant crashes if not in fitness table even if fewer", {
o1 <- allFitnessEffects(
noIntGenes = c("a" = .1, "b" = 0.2, "c" = 0.3))
expect_error(oncoSimulIndiv(o1, initMutant = "a, d"),
"For driver or initMutant you have passed genes not in the fitness table",
fixed = TRUE)
})
test_that("initMutant works if == number of genes", {
o1 <- allFitnessEffects(
noIntGenes = c("a" = .1, "b" = 0.2, "c" = 0.3))
expect_silent(ooox <- oncoSimulIndiv(o1, initMutant = "b, a, c"))
## "No mutable positions. Mutation set to dummyMutationRate",
## fixed = TRUE)
## This used to crash
set.seed(5)
o2 <- allFitnessEffects(genotFitness = rfitness(2))
oncoSimulIndiv(o2, initMutant = "B, A")
## Test a few
for(i in 1:5){
set.seed(i)
o2i <- allFitnessEffects(genotFitness = rfitness(2))
suppressWarnings(o2io <- oncoSimulIndiv(o2i, initMutant = "B, A",
onlyCancer = FALSE))
if(!is.null(o2io$other$ExceptionMessage)) {
expect_false(
grepl("Trying to obtain a mutation when nonmutated.size is 0",
o2io$other$ExceptionMessage)
)
}
}
})
test_that("initMutant with freq-dep-fitness" , {
r <- data.frame(rfitness(2))
r[, "Fitness"] <- c("f_ - f_1 - f_2 - f_1_2",
"max(100*f_1, 10)",
"max(100*f_2, 10)",
"max((200*(f_1 + f_2) + 50*f_1_2), 1)")
afe <- allFitnessEffects(genotFitness = r,
frequencyDependentFitness = TRUE)
expect_s3_class(
os1 <- oncoSimulIndiv(afe,
model = "McFL",
initMutant = "A",
onlyCancer = FALSE,
finalTime = 50,
verbosity = 0,
mu = 1e-6,
initSize = 500,
keepPhylog = FALSE,
seed = NULL,
errorHitMaxTries = TRUE,
errorHitWallTime = TRUE),
"oncosimul2")
expect_true(is.null(os1$other$ExceptionMessage))
expect_s3_class(os2 <- oncoSimulIndiv(afe,
model = "McFL",
initMutant = "B",
onlyCancer = FALSE,
finalTime = 50,
verbosity = 0,
mu = 1e-6,
initSize = 500,
keepPhylog = FALSE,
seed = NULL,
errorHitMaxTries = TRUE,
errorHitWallTime = TRUE),
"oncosimul2")
expect_s3_class(os3 <- oncoSimulIndiv(afe,
model = "McFL",
initMutant = "A, B",
onlyCancer = FALSE,
finalTime = 50,
verbosity = 0,
mu = 1e-6,
initSize = 500,
keepPhylog = FALSE,
seed = NULL,
errorHitMaxTries = TRUE,
errorHitWallTime = TRUE),
"oncosimul2")
expect_true(is.null(os3$other$ExceptionMessage))
})
test_that("WT initMutant simulation equiv. to no init mutant", {
set.seed(1)
r <- data.frame(rfitness(2))
r[, "Fitness"] <- c("f_ - f_1 - f_2 - f_1_2",
"max(100*f_1, 10)",
"max(100*f_2, 10)",
"max((200*(f_1 + f_2) + 50*f_1_2), 1)")
afe <- allFitnessEffects(genotFitness = r,
frequencyDependentFitness = TRUE)
null <- capture.output({
set.seed(1)
of1 <- oncoSimulIndiv(afe,
model = "McFL",
initMutant = "WT",
onlyCancer = FALSE,
finalTime = 50,
verbosity = 0,
mu = 1e-6,
initSize = 500,
keepPhylog = FALSE,
seed = NULL,
errorHitMaxTries = TRUE,
errorHitWallTime = TRUE)
set.seed(1)
of2 <- oncoSimulIndiv(afe,
model = "McFL",
onlyCancer = FALSE,
finalTime = 50,
verbosity = 0,
mu = 1e-6,
initSize = 500,
keepPhylog = FALSE,
seed = NULL,
errorHitMaxTries = TRUE,
errorHitWallTime = TRUE)
})
expect_true(of1$InitMutant == "WT")
expect_true(of2$InitMutant == "")
expect_identical(of1[!(names(of1) == "InitMutant")],
of2[!(names(of2) == "InitMutant")])
null <- capture.output({
set.seed(2)
o2 <- allFitnessEffects(genotFitness = rfitness(2))
set.seed(2)
os1 <- oncoSimulIndiv(o2, initMutant = "WT",
model = "McFLD",
onlyCancer = FALSE,
finalTime = 50,
verbosity = 0,
mu = 1e-6,
initSize = 500,
keepPhylog = FALSE,
seed = NULL,
errorHitMaxTries = TRUE,
errorHitWallTime = TRUE)
set.seed(2)
os2 <- oncoSimulIndiv(o2,
model = "McFLD",
onlyCancer = FALSE,
finalTime = 50,
verbosity = 0,
mu = 1e-6,
initSize = 500,
keepPhylog = FALSE,
seed = NULL,
errorHitMaxTries = TRUE,
errorHitWallTime = TRUE)
})
expect_true(os1$InitMutant == "WT")
expect_true(os2$InitMutant == "")
expect_identical(os1[!(names(of1) == "InitMutant")],
os2[!(names(of2) == "InitMutant")])
})
test_that("initMutant does not accept integer vectors", {
## Yes, evalGenotype does accept them
set.seed(2)
o2 <- allFitnessEffects(genotFitness = rfitness(2))
set.seed(2)
expect_error(oncoSimulIndiv(o2, initMutant = 1,
model = "McFLD",
onlyCancer = FALSE,
finalTime = 50,
verbosity = 0,
mu = 1e-6,
initSize = 500,
keepPhylog = FALSE,
seed = NULL,
errorHitMaxTries = TRUE,
errorHitWallTime = TRUE),
"initMutant must be a (list of) character string(s)",
fixed = TRUE)
})
## zz4:
test_that("initMutant: multiple pops, basic", {
o1 <- allFitnessEffects(
noIntGenes = c("a" = .1, "b" = 0.2, "c" = 0.3))
expect_silent(out <- oncoSimulIndiv(o1, initMutant = c("c, b", "b"),
initSize = c(300, 20),
onlyCancer = FALSE,
seed = NULL))
null <- capture.output({
outx <- oncoSimulIndiv(o1, initMutant = c("c, b", "WT"),
initSize = c(300, 20),
onlyCancer = FALSE,
seed = NULL)
outx <- oncoSimulIndiv(o1, initMutant = c("c, b", "WT", "a"),
initSize = c(300, 20, 10),
onlyCancer = FALSE,
seed = NULL)
})
## Pass all genotypes
null <- capture.output(oncoSimulIndiv(o1, initMutant = c("WT", "a", "b", "c",
"a, b", "a, c", "b, c",
"a, b, c"),
initSize = c(300, 20, 10, 6, 9, 5, 4, 6),
onlyCancer = FALSE,
seed = NULL))
set.seed(1)
r2 <- rfitness(2)
r2[4, 3] <- 0
o2 <- allFitnessEffects(genotFitness = r2)
## oncoSimulIndiv(o2, initMutant = c("B, A", "A"),
## initSize = c(300, 20),
## onlyCancer = FALSE)
expect_warning( oncoSimulIndiv(o2, initMutant = c("B, A", "A"),
initSize = c(300, 20),
onlyCancer = FALSE),
"Init Mutant with birth == 0.0", fixed = TRUE)
set.seed(1)
r2 <- rfitness(2)
o2 <- allFitnessEffects(genotFitness = r2)
expect_silent(out <- oncoSimulIndiv(o2, initMutant = c("B, A", "A"),
initSize = c(300, 20),
onlyCancer = FALSE,
seed = NULL))
})
test_that("lengths initSize and initMutant must match", {
set.seed(1)
r2 <- rfitness(2)
o2 <- allFitnessEffects(genotFitness = r2)
expect_error(oncoSimulIndiv(o2, initMutant = c("B, A", "A"),
initSize = c(20),
onlyCancer = FALSE),
"Lengths of initSize and initMutant differ",
fixed = TRUE)
})
test_that("multiple init mutants: pops of 0 size is error", {
set.seed(1)
r2 <- rfitness(2)
o2 <- allFitnessEffects(genotFitness = r2)
expect_error(oncoSimulIndiv(o2, initMutant = c("B, A", "A"),
initSize = c(0, 20),
onlyCancer = FALSE),
"At least one initSize <= 0",
fixed = TRUE)
## Now caught in R
## expect_true(out$other$ExceptionMessage
## "Unrecoverable exception: ti: popSize <= 0",
## fixed = TRUE)
})
## with fdf and without fdf
test_that("multiple init mutants: different species", {
num_reps <- 10
for(i in 1:num_reps) {
r2 <- rfitness(6)
## Make sure these always viable for interesting stuff
r2[2, 7] <- 1 + runif(1) # A
r2[4, 7] <- 1 + runif(1) # C
r2[8, 7] <- 1 + runif(1) # A, B
## Two species, first with pos 1 and 2
## all with both mutated is 0
r2[ (r2[, 1] == 0) & (r2[, 2] == 1), 7] <- 0
r2[ (r2[, 3] == 0) & apply(r2[, 3:6] == 1, 1, any), 7] <- 0
r2[ (r2[, 1] == 1) & apply(r2[, 3:6] == 1, 1, any), 7] <- 0
r2[ (r2[, 3] == 1) & apply(r2[, 1:2] == 1, 1, any), 7] <- 0
r2 <- r2[r2[, 7] > 0, ]
o2 <- allFitnessEffects(genotFitness = r2)
ag <- evalAllGenotypes(o2)
## Set mutation rate of the "species indicator" close to 1e-10
out1 <- oncoSimulIndiv(o2, initMutant = c("A", "C"),
initSize = c(1e3, 1e4),
finalTime = 300,
mu = c(A = 1e-10, C = 1e-10,
B = 1e-5, D = 1e-5, E = 1e-5, F = 1e-5),
onlyCancer = FALSE)
not_possible <- c("", ag$Genotype[ag$Fitness == 0])
expect_false(any(not_possible %in% out1$GenotypesLabels))
}
num_reps <- 5
for(i in 1:num_reps) {
r2 <- rfitness(6)
## Make sure these always viable for interesting stuff
r2[2, 7] <- 1 + runif(1) # A
r2[4, 7] <- 1 + runif(1) # C
r2[8, 7] <- 1 + runif(1) # A, B
## Two species, first with pos 1 and 2
## all with both mutated is 0
r2[ (r2[, 1] == 0) & (r2[, 2] == 1), 7] <- 0
r2[ (r2[, 3] == 0) & apply(r2[, 3:6] == 1, 1, any), 7] <- 0
r2[ (r2[, 1] == 1) & apply(r2[, 3:6] == 1, 1, any), 7] <- 0
r2[ (r2[, 3] == 1) & apply(r2[, 1:2] == 1, 1, any), 7] <- 0
r2 <- r2[r2[, 7] > 0, ]
o2 <- allFitnessEffects(genotFitness = r2)
ag <- evalAllGenotypes(o2)
## Set mutation rate of the "species indicator" close to 1e-10
## But this ain't really enough: what about A, D? A is mutating
## to A, D with rate given by D, not by C.
out1 <- oncoSimulIndiv(o2, initMutant = c("A", "C"),
model = "McFLD",
initSize = c(1e3, 1e4),
finalTime = 500,
mu = c(A = 1e-10, C = 1e-10,
B = 1e-5, D = 1e-5, E = 1e-5, F = 1e-5),
onlyCancer = FALSE)
not_possible <- c("", ag$Genotype[ag$Fitness == 0])
expect_false(any(not_possible %in% out1$GenotypesLabels))
}
})
test_that("multiple init mutants: cannot have descendants of absent parents", {
num_reps <- 10
for(i in 1:num_reps) {
r2 <- rfitness(6)
## Make sure these always viable
r2[8, 7] <- 1 + runif(1) # A, B
r2[19, 7] <- 1 + runif(1) # C, F
o2 <- allFitnessEffects(genotFitness = r2)
ag <- evalAllGenotypes(o2)
## Set mutation rate of the "species indicator" close to 1e-10
out1 <- oncoSimulIndiv(o2, initMutant = c("A, B", "C, F"),
initSize = c(100, 200),
onlyCancer = FALSE)
not_possible <- unique(c("", LETTERS[1:6],
paste0("A, ", LETTERS[3:6]),
paste0("B, ", LETTERS[3:6]),
paste0("C, ", LETTERS[4:5]),
paste0("D, ", LETTERS[5:6]),
"E, F",
ag$Genotype[ag$Fitness == 0]))
expect_false(any(not_possible %in% out1$GenotypesLabels))
}
})
test_that("multiple init mutants: different species, FDF", {
gffd0 <- data.frame(
Genotype = c(
"A", "A, B",
"C", "C, D", "C, E"),
Fitness = c(
"1.3",
"1.4",
"1.4",
"1.1 + 0.7*((f_1 + f_A_B) > 0.3)",
"1.2 + sqrt(f_A + f_C + f_C_D)"),
stringsAsFactors = FALSE)
afd0 <- allFitnessEffects(genotFitness = gffd0,
frequencyDependentFitness = TRUE)
suppressWarnings(eag0 <- evalAllGenotypes(afd0, spPopSizes = 1:5))
gffd <- data.frame(
Genotype = c("WT",
"A", "A, B",
"C", "C, D", "C, E"),
Fitness = c("0",
"1.3",
"1.4",
"1.4",
"1.1 + 0.7*((f_1 + f_1_2) > 0.3)",
"1.2 + sqrt(f_1 + f_3 + f_3_4)"),
stringsAsFactors = FALSE)
afd <- allFitnessEffects(genotFitness = gffd,
frequencyDependentFitness = TRUE)
suppressWarnings(eag1 <- evalAllGenotypes(afd, spPopSizes = 0:5))
## No wildtype
gffd2 <- data.frame(
Genotype = c("A", "A, B",
"C", "C, D", "C, E"),
Fitness = c("1.3",
"1.4",
"1.4",
"1.1 + 0.7*((f_1 + f_1_2) > 0.3)",
"1.2 + sqrt(f_1 + f_3 + f_3_4)"),
stringsAsFactors = FALSE)
afd2 <- allFitnessEffects(genotFitness = gffd2,
frequencyDependentFitness = TRUE)
suppressWarnings(eag2 <- evalAllGenotypes(afd2, spPopSizes = 1:5))
expect_identical(eag1, eag2)
expect_identical(eag0, eag2)
set.seed(1)
os1 <- oncoSimulIndiv(afd, initMutant = "A", seed = NULL,
finalTime = 20, initSize = 1e6,
onlyCancer = FALSE, model = "McFLD")
set.seed(1)
os2 <- oncoSimulIndiv(afd2, initMutant = "A", seed = NULL,
finalTime = 20, initSize = 1e6,
onlyCancer = FALSE, model = "McFLD")
set.seed(1)
os0 <- oncoSimulIndiv(afd0, initMutant = "A", seed = NULL,
finalTime = 20, initSize = 1e6,
onlyCancer = FALSE, model = "McFLD")
expect_equivalent(os1, os2)
expect_equivalent(os1, os0)
})
test_that("multiple init mutants: different species, FDF, check fitness", {
mspecF <- data.frame(
Genotype = c("A",
"A, a1", "A, a2", "A, a1, a2",
"B",
"B, b1", "B, b2", "B, b3",
"B, b1, b2", "B, b1, b3", "B, b1, b2, b3"),
Fitness = c("1 + f_A_a1",
"1 + f_A_a2",
"1 + f_A_a1_a2",
"1 + f_B",
"1 + f_B_b1",
"1 + f_B_b2",
"1 + f_B_b3",
"1 + f_B_b1_b2",
"1 + f_B_b1_b3",
"1 + f_B_b1_b2_b3",
"1 + f_A")
)
fmspecF <- allFitnessEffects(genotFitness = mspecF,
frequencyDependentFitness = TRUE)
## Remeber, spPopSizes correspond to the genotypes
## shown in
fmspecF$full_FDF_spec
## in exactly that order
suppressWarnings(afmspecF <- evalAllGenotypes(fmspecF, spPopSizes = 1:11))
## Show only viable ones
afmspecF[afmspecF$Fitness >= 1, ]
## Expected values
exv <- 1 + c(3, 5, 4, 8, 6, 7, 9, 2, 10, 11, 1)/sum(1:11)
stopifnot(isTRUE(all.equal(exv, afmspecF[afmspecF$Fitness >= 1, ]$Fitness)))
})
test_that("multiple init mutants: different species, FDF, exprtk crash if not in fitness table", {
## Crash, as f_2 is not present
gffd <- data.frame(
Genotype = c("WT",
"A", "A, B",
"C", "C, D", "C, E"),
Fitness = c("0",
"1.3",
"1.4",
"1.4",
"1.1 + 0.7*((f_1 + f_2) > 0.3)",
"1.2 + sqrt(f_1 + f_3 + f_2)"),
stringsAsFactors = FALSE)
afd <- allFitnessEffects(genotFitness = gffd,
frequencyDependentFitness = TRUE)
suppressWarnings(expect_error(evalAllGenotypes(afd, spPopSizes = rep(10, 6))))
### FIXME: catch this exact string"Undefined symbol: 'f_2'", fixed = TRUE)
})
cat(paste("\n Ending init-mutant tests", date(), "\n"))
cat(paste(" Took ", round(difftime(Sys.time(), inittime, units = "secs"), 2), "\n\n"))
rm(inittime)
