inittime <- Sys.time()
cat(paste("\n Starting test.Z-mutator at", date(), "\n"))
test_that("Mutator genes missing from fitness", {
RNGkind("Mersenne-Twister")
p4 <- data.frame(parent = c(rep("Root", 4), "A", "B", "D", "E", "C", "F"),
child = c("A", "B", "D", "E", "C", "C", "F", "F", "G", "G"),
s = c(0.01, 0.02, 0.03, 0.04, 0.1, 0.1, 0.2, 0.2, 0.3, 0.3),
sh = c(rep(0, 4), c(-.9, -.9), c(-.95, -.95), c(-.99, -.99)),
typeDep = c(rep("--", 4),
"XMPN", "XMPN", "MN", "MN", "SM", "SM"))
oe <- c("C > F" = -0.1, "H > I" = 0.12)
sm <- c("I:J" = -1)
sv <- c("-K:M" = -.5, "K:-M" = -.5)
epist <- c(sm, sv)
modules <- c("Root" = "Root", "A" = "a1",
"B" = "b1, b2", "C" = "c1",
"D" = "d1, d2", "E" = "e1",
"F" = "f1, f2", "G" = "g1",
"H" = "h1, h2", "I" = "i1",
"J" = "j1, j2", "K" = "k1, k2", "M" = "m1")
set.seed(1) ## for repeatability
## These are seeds in R; no problems with different compilers, etc.
noint <- rexp(5, 10)
names(noint) <- paste0("n", 1:5)
fea <- allFitnessEffects(rT = p4, epistasis = epist, orderEffects = oe,
noIntGenes = noint, geneToModule = modules)
epistM <- c(A = 10, K = 5, "A:K" = 22, "U:K" = 130)
noIntM <- c(k1 = 7, j1 = 3)
modulesM <- c(K = "a1, h2, i1", A = "b2, c1, n3", U = "k2, k3")
fm <- allMutatorEffects(epistasis = epistM,
noIntGenes = noIntM,
geneToModule = modulesM)
expect_error(oncoSimulIndiv(fea, muEF = fm),
"Genes in mutatorEffects not present in fitnessEffects",
fixed = TRUE)
fm3 <- allMutatorEffects(epistasis = epist,
noIntGenes = c(noint, "cucurucu" = 23),
geneToModule = c("Root" = "Root",
"I" = "i1",
"J" = "j1, j2", "K" = "k1, k2", "M" = "m1")
)
expect_error(oncoSimulIndiv(fea, muEF = fm3),
"Genes in mutatorEffects not present in fitnessEffects",
fixed = TRUE)
})
test_that("all genes must be in geneToModule even if present in fitness", {
p4 <- data.frame(parent = c(rep("Root", 4), "A", "B", "D", "E", "C", "F"),
child = c("A", "B", "D", "E", "C", "C", "F", "F", "G", "G"),
s = c(0.01, 0.02, 0.03, 0.04, 0.1, 0.1, 0.2, 0.2, 0.3, 0.3),
sh = c(rep(0, 4), c(-.9, -.9), c(-.95, -.95), c(-.99, -.99)),
typeDep = c(rep("--", 4),
"XMPN", "XMPN", "MN", "MN", "SM", "SM"))
oe <- c("C > F" = -0.1, "H > I" = 0.12)
sm <- c("I:J" = -1)
sv <- c("-K:M" = -.5, "K:-M" = -.5)
epist <- c(sm, sv)
modules <- c("Root" = "Root", "A" = "a1",
"B" = "b1, b2", "C" = "c1",
"D" = "d1, d2", "E" = "e1",
"F" = "f1, f2", "G" = "g1",
"H" = "h1, h2", "I" = "i1",
"J" = "j1, j2", "K" = "k1, k2", "M" = "m1")
set.seed(1) ## for repeatability
## These are seeds in R; no problems with different compilers, etc.
noint <- rexp(5, 10)
names(noint) <- paste0("n", 1:5)
fea <- allFitnessEffects(rT = p4, epistasis = epist, orderEffects = oe,
noIntGenes = noint, geneToModule = modules)
expect_error(fm2 <- allMutatorEffects(epistasis = epist,
noIntGenes = c(noint, "cucurucu" = 23),
geneToModule = modules),
"Some values in geneToModule not present in any of",
fixed = TRUE)
})
test_that("fit. mut. eff. values, long ex", {
## Based on "long example OK" in "test.all-fitness.R"
## Fitness and mutator effects are evaluated OK when modules, epist, etc,
## are made differently in mutator and fitness and modules even share names
RNGkind("Mersenne-Twister")
## in 3.6.0 change in sample: see https://bugs.r-project.org/bugzilla/show_bug.cgi?id=17494
suppressWarnings(RNGversion("3.5.1"))
p4 <- data.frame(parent = c(rep("Root", 4), "A", "B", "D", "E", "C", "F"),
child = c("A", "B", "D", "E", "C", "C", "F", "F", "G", "G"),
s = c(0.01, 0.02, 0.03, 0.04, 0.1, 0.1, 0.2, 0.2, 0.3, 0.3),
sh = c(rep(0, 4), c(-.9, -.9), c(-.95, -.95), c(-.99, -.99)),
typeDep = c(rep("--", 4),
"XMPN", "XMPN", "MN", "MN", "SM", "SM"))
oe <- c("C > F" = -0.1, "H > I" = 0.12)
sm <- c("I:J" = -1)
sv <- c("-K:M" = -.5, "K:-M" = -.5)
epist <- c(sm, sv)
modules <- c("Root" = "Root", "A" = "a1",
"B" = "b1, b2", "C" = "c1",
"D" = "d1, d2", "E" = "e1",
"F" = "f1, f2", "G" = "g1",
"H" = "h1, h2", "I" = "i1",
"J" = "j1, j2", "K" = "k1, k2", "M" = "m1")
set.seed(1) ## for repeatability
## These are seeds in R; no problems with different compilers, etc.
noint <- rexp(5, 10)
names(noint) <- paste0("n", 1:5)
fea <- allFitnessEffects(rT = p4, epistasis = epist, orderEffects = oe,
noIntGenes = noint, geneToModule = modules)
epistM <- c(A = 10, K = 5, "A:K" = 22, "U:K" = 130)
noIntM <- c(k1 = 7, j1 = 3)
modulesM <- c(K = "a1, h2, i1", A = "b2, c1, n3", U = "k2")
fm <- allMutatorEffects(epistasis = epistM,
noIntGenes = noIntM,
geneToModule = modulesM)
## Fitness comparisons as in "long example OK" in "test.all-fitness.R"
expect_true(all.equal(evalGenotype("k1 > i1 > h2", fea), 0.5)) ## 0.5
expect_true(all.equal(evalGenotype("k1 > h1 > i1", fea), 0.5 * 1.12)) ## 0.5 * 1.12
expect_true(all.equal(evalGenotype("k2 > m1 > h1 > i1", fea), 1.12)) ## 1.12
nnn <- noint[3]; names(nnn) <- NULL
expect_true(all.equal(evalGenotype("k2 > m1 > h1 > i1 > c1 > n3 > f2", fea), (1 + nnn) * 0.1 * 0.05 * 0.9 * 1.12)) ## 1.12 * 0.1 * (1 + noint[3]) * 0.05 * 0.9
randomGenotype <- function(fe, ns = NULL) {
gn <- setdiff(c(fe$geneModule$Gene,
fe$long.geneNoInt$Gene), "Root")
if(is.null(ns)) ns <- sample(length(gn), 1)
return(paste(sample(gn, ns), collapse = " > "))
}
## the following, all checked by hand. I wonder if seed will move around?
set.seed(2) ## for reproducibility
expect_true(all.equal(evalGenotype(randomGenotype(fea), fea),
0.1 * (1 + noint[1]), check.names = FALSE))
## Genotype: k2 > i1 > c1 > n1 > m1
## Individual s terms are : 0.0755182 -0.9
## Fitness: 0.107552
expect_true(all.equal(evalGenotype(randomGenotype(fea), fea),
(1 + noint[2]), check.names = FALSE))
## Genotype: n2 > h1 > h2
## Individual s terms are : 0.118164
## Fitness: 1.11816
expect_true(all.equal(evalGenotype(randomGenotype(fea), fea),
(1 + noint[3]) * (1 + noint[4]) * (1 + noint[5]) *
1.02 * 1.1 * 1.03 * .05 * 1.3 * .9,
check.names = FALSE))
## Genotype: d2 > k2 > c1 > f2 > n4 > m1 > n3 > f1 > b1 > g1 > n5 > h1 > j2
## Individual s terms are : 0.0145707 0.0139795 0.0436069 0.02 0.1 0.03 -0.95 0.3 -0.1
## Fitness: 0.0725829
expect_true(all.equal(evalGenotype(randomGenotype(fea), fea),
(1 + noint[1]) * (1 + noint[2]) *
1.01 * 1.02 * .1 * .05 * .9 * 1.12,
check.names = FALSE))
## Genotype: h2 > c1 > f1 > n2 > b2 > a1 > n1 > i1
## Individual s terms are : 0.0755182 0.118164 0.01 0.02 -0.9 -0.95 -0.1 0.12
## Fitness: 0.00624418
expect_true(all.equal(evalGenotype(randomGenotype(fea), fea), 0,
check.names = FALSE))
## Genotype: h2 > j1 > m1 > d2 > i1 > b2 > k2 > d1 > b1 > n3 > n1 > g1 > h1 > c1 > k1 > e1 > a1 > f1 > n5 > f2
## Individual s terms are : 0.0755182 0.0145707 0.0436069 0.01 0.02 -0.9 0.03 0.04 0.2 0.3 -1 -0.1 0.12
## Fitness: 0
expect_true(all.equal(evalGenotype(randomGenotype(fea), fea), 0,
check.names = FALSE))
## Genotype: n1 > m1 > n3 > i1 > j1 > n5 > k1
## Individual s terms are : 0.0755182 0.0145707 0.0436069 -1
## Fitness: 0
expect_true(all.equal(evalGenotype(randomGenotype(fea), fea),
(1 + noint[1]) *
(1 + noint[2]) *
(1 + noint[4]) * 1.01 * 1.02 * .1 * 1.03 * .05 * 1.3 * 0.5,
check.names = FALSE))
## Genotype: d2 > n1 > g1 > f1 > f2 > c1 > b1 > d1 > k1 > a1 > b2 > i1 > n4 > h2 > n2
## Individual s terms are : 0.0755182 0.118164 0.0139795 0.01 0.02 -0.9 0.03 -0.95 0.3 -0.5
## Fitness: 0.00420528
expect_true(all.equal(evalGenotype(randomGenotype(fea), fea),
(1 + noint[3]) * (1 + noint[4]) * 1.01 *
1.1 * 1.03 * .05 * .5,
check.names = FALSE))
## Genotype: j1 > f1 > j2 > a1 > n4 > c1 > n3 > k1 > d1 > h1
## Individual s terms are : 0.0145707 0.0139795 0.01 0.1 0.03 -0.95 -0.5
## Fitness: 0.0294308
expect_true(all.equal(evalGenotype(randomGenotype(fea), fea),
(1 + noint[3]) * (1 + noint[4]) * (1 + noint[5]) *
1.02 * 1.1 * 0.05, check.names = FALSE))
## Genotype: n5 > f2 > f1 > h2 > n4 > c1 > n3 > b1
## Individual s terms are : 0.0145707 0.0139795 0.0436069 0.02 0.1 -0.95
## Fitness: 0.0602298
expect_true(all.equal(evalGenotype(randomGenotype(fea), fea),
1.03 * 0.05, check.names = FALSE))
## Genotype: h1 > d1 > f2
## Individual s terms are : 0.03 -0.95
## Fitness: 0.0515
## Fitness comparisons as in "long example OK" in "test.all-fitness.R",
## but adding the mutation
expect_true(all.equal(evalGenotypeFitAndMut("k1 > i1 > h2", fea, fm),
c(0.5, 35.0)))
expect_true(all.equal(evalGenotypeFitAndMut("k1 > h1 > i1", fea, fm),
c(0.5 * 1.12, 35.00)))
expect_true(all.equal(evalGenotypeFitAndMut("k2 > m1 > h1 > i1", fea, fm),
c(1.12, 650.0)))
nnn <- noint[3]; names(nnn) <- NULL
expect_true(all.equal(evalGenotypeFitAndMut("k2 > m1 > h1 > i1 > c1 > n3 > f2", fea, fm),
c((1 + nnn) * 0.1 * 0.05 * 0.9 * 1.12, 130 * 5 * 10 * 22)))
## 1.12 * 0.1 * (1 + noint[3]) * 0.05 * 0.9
## the following, all checked by hand. I wonder if seed will move around?
set.seed(2) ## for reproducibility
expect_true(all.equal(evalGenotypeFitAndMut(randomGenotype(fea), fea, fm),
c(0.1 * (1 + noint[1]), 22 * 130 * 10 * 5),
check.names = FALSE))
## Genotype: k2 > i1 > c1 > n1 > m1
## Individual s terms are : 0.0755182 -0.9
## Fitness: 0.107552
expect_true(all.equal(evalGenotypeFitAndMut(randomGenotype(fea), fea, fm),
c((1 + noint[2]), 5 ), check.names = FALSE))
## Genotype: n2 > h1 > h2
## Individual s terms are : 0.118164
## Fitness: 1.11816
expect_true(all.equal(evalGenotypeFitAndMut(randomGenotype(fea), fea, fm),
c((1 + noint[3]) * (1 + noint[4]) * (1 + noint[5]) *
1.02 * 1.1 * 1.03 * .05 * 1.3 * .9,
10),
check.names = FALSE))
## Genotype: d2 > k2 > c1 > f2 > n4 > m1 > n3 > f1 > b1 > g1 > n5 > h1 > j2
## Individual s terms are : 0.0145707 0.0139795 0.0436069 0.02 0.1 0.03 -0.95 0.3 -0.1
## Fitness: 0.0725829
expect_true(all.equal(evalGenotypeFitAndMut(randomGenotype(fea), fea, fm),
c((1 + noint[1]) * (1 + noint[2]) *
1.01 * 1.02 * .1 * .05 * .9 * 1.12,
22 * 10 * 5),
check.names = FALSE))
## Genotype: h2 > c1 > f1 > n2 > b2 > a1 > n1 > i1
## Individual s terms are : 0.0755182 0.118164 0.01 0.02 -0.9 -0.95 -0.1 0.12
## Fitness: 0.00624418
expect_true(all.equal(evalGenotypeFitAndMut(randomGenotype(fea), fea, fm),
c(0, 3 * 7 * 10 * 5 * 22 * 130),
check.names = FALSE))
## Genotype: h2 > j1 > m1 > d2 > i1 > b2 > k2 > d1 > b1 > n3 > n1 > g1 > h1 > c1 > k1 > e1 > a1 > f1 > n5 > f2
## Individual s terms are : 0.0755182 0.0145707 0.0436069 0.01 0.02 -0.9 0.03 0.04 0.2 0.3 -1 -0.1 0.12
## Fitness: 0
expect_true(all.equal(evalGenotypeFitAndMut(randomGenotype(fea), fea, fm),
c(0, 3 * 7 * 10 * 5 * 22),
check.names = FALSE))
## Genotype: n1 > m1 > n3 > i1 > j1 > n5 > k1
## Individual s terms are : 0.0755182 0.0145707 0.0436069 -1
## Fitness: 0
expect_true(all.equal(evalGenotypeFitAndMut(randomGenotype(fea), fea, fm),
c((1 + noint[1]) * (1 + noint[2]) * (1 + noint[4]) *
1.01 * 1.02 * .1 * 1.03 * .05 * 1.3 * 0.5,
7 * 10 * 5 * 22),
, check.names = FALSE))
## Genotype: d2 > n1 > g1 > f1 > f2 > c1 > b1 > d1 > k1 > a1 > b2 > i1 > n4 > h2 > n2
## Individual s terms are : 0.0755182 0.118164 0.0139795 0.01 0.02 -0.9 0.03 -0.95 0.3 -0.5
## Fitness: 0.00420528
expect_true(all.equal(evalGenotypeFitAndMut(randomGenotype(fea), fea, fm),
c((1 + noint[3]) * (1 + noint[4]) * 1.01 *
1.1 * 1.03 * .05 * .5,
3 * 7 * 10 * 5 * 22),
check.names = FALSE))
## Genotype: j1 > f1 > j2 > a1 > n4 > c1 > n3 > k1 > d1 > h1
## Individual s terms are : 0.0145707 0.0139795 0.01 0.1 0.03 -0.95 -0.5
## Fitness: 0.0294308
expect_true(all.equal(evalGenotypeFitAndMut(randomGenotype(fea), fea, fm),
c((1 + noint[3]) * (1 + noint[4]) * (1 + noint[5]) *
1.02 * 1.1 * 0.05,
10 * 5 * 22),
check.names = FALSE))
## Genotype: n5 > f2 > f1 > h2 > n4 > c1 > n3 > b1
## Individual s terms are : 0.0145707 0.0139795 0.0436069 0.02 0.1 -0.95
## Fitness: 0.0602298
expect_true(all.equal(evalGenotypeFitAndMut(randomGenotype(fea), fea, fm),
c(1.03 * 0.05,
1),
check.names = FALSE))
## Genotype: h1 > d1 > f2
## Individual s terms are : 0.03 -0.95
## Fitness: 0.0515
})
set.seed(NULL)
cat(paste("\n Ending test.Z-mutator at", date(), "\n"))
cat(paste(" Took ", round(difftime(Sys.time(), inittime, units = "secs"), 2), "\n\n"))
rm(inittime)
