tests/testthat/test_tess3main.R
In cayek/TESS3_encho_sen: Inference of Spatial Population Genetic Structure
context("TESS3 main")
test_that("TESS3 copy", {
set.seed(357467)
n <- 100
K <- 3
ploidy <- 1
L <- 3001
data.list <- SampleGenoFromGenerativeModelTESS3(G = SampleUnifDirichletG(L, ploidy, K),
Q = SampleUnifQ(n, K),
coord = SampleNormalClusterCoord(n.by.pop = n, K = 1),
ploidy = ploidy)
data.list$XBin <- matrix(0.0, n, L * (ploidy + 1))
X2XBin(data.list$X, data.list$ploidy, data.list$XBin)
expect_error(tess3.res <- tess3Main(X = data.list$X,
XProba = NULL,
coord = data.list$coord,
K,
ploidy,
lambda = 1.0,
W = NULL,
method = "projected.ls",
max.iteration = 200,
tolerance = 1e-5,
openMP.core.num = 4,
Q.init = NULL,
mask = 0.0,
copy = FALSE),
"To force the function not doing copy of the data, you must set XProba")
expect_error(tess3.res <- tess3Main(X = NULL,
XProba = data.list$X,
coord = data.list$coord,
K,
ploidy,
lambda = 1.0,
W = NULL,
method = "projected.ls",
max.iteration = 200,
tolerance = 1e-5,
openMP.core.num = 4,
Q.init = NULL,
mask = 0.0,
copy = FALSE),
"Number of columns of XProba must be a multiple of ploidy \\+ 1")
tess3.res <- tess3Main(X = NULL,
XProba = data.list$XBin,
coord = data.list$coord,
K,
ploidy,
lambda = 1.0,
W = NULL,
method = "projected.ls",
max.iteration = 200,
tolerance = 1e-5,
openMP.core.num = 4,
Q.init = NULL,
mask = 0.0,
copy = FALSE)
})
test_that("TESS3 to dataframe", {
set.seed(57467)
n <- 100
K <- 6
ploidy <- 4
L <- 3001
data.list <- SampleGenoFromGenerativeModelTESS3(G = SampleUnifDirichletG(L, ploidy, K),
Q = SampleUnifQ(n, K),
coord = SampleNormalClusterCoord(n.by.pop = n, K = 1),
ploidy = ploidy)
data.list$XBin <- matrix(0.0, n, L * (ploidy + 1))
X2XBin(data.list$X, data.list$ploidy, data.list$XBin)
tess3.res <- tess3Main(X = NULL,
XProba = data.list$XBin,
coord = data.list$coord,
K,
ploidy,
lambda = 1.0,
W = NULL,
method = "projected.ls",
max.iteration = 200,
tolerance = 1e-5,
openMP.core.num = 4)
df <- data.frame(log.pvalue = tess3.res$log.pvalue,
fst = tess3.res$Fst,
Fscore = tess3.res$Fscore,
pvalue = tess3.res$pvalue,
index = seq_along(tess3.res$log.pvalue))
df <- data.frame(tess3.res$Q)
df <- data.frame(tess3.res$G)
})
test_that("TESS3 main, algo.copy and XBin", {
set.seed(698)
n <- 100
K <- 3
ploidy <- 2
L <- 3000
data.list <- SampleGenoFromGenerativeModelTESS3(G = SampleUnifDirichletG(L, ploidy, K),
Q = SampleUnifQ(n, K),
coord = SampleNormalClusterCoord(n.by.pop = n, K = 1),
ploidy = ploidy)
data.list$XBin <- matrix(0.0, n, L * (ploidy + 1))
X2XBin(data.list$X, data.list$ploidy, data.list$XBin)
set.seed(687)
tess3.res.nocopy <- tess3Main(X = NULL,
XProba = data.list$XBin,
coord = data.list$coord,
K,
ploidy,
lambda = 1.0,
W = NULL,
method = "projected.ls",
max.iteration = 200,
tolerance = 1e-5,
openMP.core.num = 4,
Q.init = NULL,
mask = 0.0,
algo.copy = FALSE)
set.seed(687)
tess3.res.copy <- tess3Main(X = NULL,
XProba = data.list$XBin,
coord = data.list$coord,
K,
ploidy,
lambda = 1.0,
W = NULL,
method = "projected.ls",
max.iteration = 200,
tolerance = 1e-5,
openMP.core.num = 4,
Q.init = NULL,
mask = 0.0,
algo.copy = TRUE)
set.seed(687)
tess3.res.copy.X <- tess3Main(X = data.list$X,
XProba = NULL,
coord = data.list$coord,
K,
ploidy,
lambda = 1.0,
W = NULL,
method = "projected.ls",
max.iteration = 200,
tolerance = 1e-5,
openMP.core.num = 4,
Q.init = NULL,
mask = 0.0,
algo.copy = TRUE)
set.seed(687)
tess3.res.nocopy.X <- tess3Main(X = data.list$X,
XProba = NULL,
coord = data.list$coord,
K,
ploidy,
lambda = 1.0,
W = NULL,
method = "projected.ls",
max.iteration = 200,
tolerance = 1e-5,
openMP.core.num = 4,
Q.init = NULL,
mask = 0.0,
algo.copy = FALSE)
expect_lt(ComputeRmseWithBestPermutation(tess3.res.copy$Q, tess3.res.nocopy$Q), 1e-15)
expect_lt(ComputeRmseWithBestPermutation(tess3.res.copy$G, tess3.res.nocopy$G), 1e-15)
expect_lt(ComputeRmseWithBestPermutation(tess3.res.copy.X$G, tess3.res.nocopy$G), 1e-15)
expect_lt(ComputeRmseWithBestPermutation(tess3.res.copy.X$Q, tess3.res.nocopy$Q), 1e-15)
expect_lt(ComputeRmseWithBestPermutation(tess3.res.nocopy.X$G, tess3.res.nocopy$G), 1e-200)
expect_lt(ComputeRmseWithBestPermutation(tess3.res.nocopy.X$Q, tess3.res.nocopy$Q), 1e-200)
})
test_that("TESS3 main with method MCPA", {
data("data.for.test", package = "tess3r")
set.seed(0)
tess3.res <- tess3Main(X = data.for.test$X,
coord = data.for.test$coord,
K = 3,
ploidy = 1,
lambda = 1.0,
method = "projected.ls")
# consistent error ?
expect_lt(ComputeRmseWithBestPermutation(data.for.test$Q, tess3.res$Q), 0.03)
expect_lt(ComputeRmseWithBestPermutation(data.for.test$G, tess3.res$G), 0.06)
# Consistent Fst ?
Fst <- ComputeFst(data.for.test$Q, data.for.test$G, data.for.test$d + 1)
expect_lt(sqrt(mean((Fst - tess3.res$Fst) ^ 2)), 0.08)
# with a Q.init
set.seed(0)
K = 3
Q.init <- matrix(runif(nrow(data.for.test$X) * K), nrow(data.for.test$X), K)
Q.init <- ProjectQ(Q.init)
tess3.res <- tess3Main(X = data.for.test$X,
coord = data.for.test$coord,
K = K,
ploidy = 1,
lambda = 1.0,
method = "projected.ls",
Q.init = Q.init)
# consistent error ?
expect_lt(ComputeRmseWithBestPermutation(data.for.test$Q, tess3.res$Q), 0.03)
expect_lt(ComputeRmseWithBestPermutation(data.for.test$G, tess3.res$G), 0.06)
# rmse and cross entropy
expect_lte(tess3.res$rmse, 0.3712458)
expect_lte(tess3.res$crossentropy, 0.2111163)
})
test_that("TESS3 main with method OQA", {
data("data.for.test", package = "tess3r")
set.seed(0)
tess3.res <- tess3Main(X = data.for.test$X,
coord = data.for.test$coord,
K = 3,
ploidy = 1,
lambda = 1.0,
method = "qp",
tolerance = 0.00001)
# consistent error ?
expect_lt(ComputeRmseWithBestPermutation(data.for.test$Q, tess3.res$Q), 0.03)
expect_lt(ComputeRmseWithBestPermutation(data.for.test$G, tess3.res$G), 0.06)
# Consistent Fst ?
Fst <- ComputeFst(data.for.test$Q, data.for.test$G, data.for.test$d + 1)
expect_lt(sqrt(mean((Fst - tess3.res$Fst) ^ 2)), 0.08)
# rmse and cross entropy
expect_lte(tess3.res$rmse, 0.3712458)
expect_lte(round(tess3.res$crossentropy,3), 0.211)
})
test_that("TESS3 main check arg", {
data("data.for.test", package = "tess3r")
expect_error(tess3.res <- tess3Main(X = data.for.test$X,
coord = data.for.test$coord,
K = 3,
ploidy = 1,
lambda = 1.0,
method = "OA",
tolerance = 0.00001),"method must be projected.ls or qp")
W <- matrix(2, nrow = 3)
expect_error(tess3.res <- tess3Main(X = data.for.test$X,
coord = data.for.test$coord,
K = 3,
ploidy = 1,
lambda = 1.0,
W = W,
tolerance = 0.00001),"W must be a squared symmetric matrix")
W = matrix(runif(data.for.test$n ^ 2), data.for.test$n,data.for.test$n)
expect_error(tess3.res <- tess3Main(X = data.for.test$X,
coord = data.for.test$coord,
K = 3,
ploidy = 1,
lambda = 1.0,
W = W,
tolerance = 0.00001),"W must be a squared symmetric matrix")
expect_error(tess3Main(X = data.for.test$X,
coord = data.for.test$coord,
K = 3,
ploidy = 0,
lambda = 1.0,
method = "projected.ls",
tolerance = 0.00001),
"The maximum value of the genotype matrix \\(X\\) cannot be greater than ploidy \\+ 1\\. Missing data must be encoded as NA\\.")
genotype <- data.for.test$X
genotype[1,1] <- -9
expect_error(tess3Main(X = genotype,
coord = data.for.test$coord,
K = 3,
ploidy = 1,
lambda = 1.0,
method = "projected.ls",
tolerance = 0.00001),
"Negative values in the genotype matrix \\(X\\) are not allowed\\. Missing data must be encoded as NA\\.")
expect_error(tess3Main(X = data.for.test$X,
coord = data.for.test$coord[-1,],
K = 3,
ploidy = 1,
lambda = 1.0,
method = "projected.ls",
tolerance = 0.00001),"Number of row of coord and X/XBin must be the same")
})
test_that("TESS3 main with missing value", {
data("data.for.test", package = "tess3r")
# mask data
set.seed(0)
masked.prop <- 0.1
masked.X <- data.for.test$X
masked.X[sample(1:(ncol(masked.X)*nrow(masked.X)), (ncol(masked.X)*nrow(masked.X)) * masked.prop)] <- NA
# run tess3 with MCPA
tess3.res <- tess3Main(X = masked.X,
coord = data.for.test$coord,
K = 3,
ploidy = 1,
lambda = 1.0,
method = "projected.ls")
# consistent error ?
expect_lt(ComputeRmseWithBestPermutation(data.for.test$Q, tess3.res$Q), 0.05)
expect_lt(ComputeRmseWithBestPermutation(data.for.test$G, tess3.res$G), 0.1)
# Consistent Fst ?
Fst <- ComputeFst(data.for.test$Q, data.for.test$G, data.for.test$d + 1)
expect_lt(sqrt(mean((Fst - tess3.res$Fst) ^ 2)), 0.09)
# rmse and cross entropy
expect_lte(tess3.res$rmse, 0.3608639)
expect_lte(tess3.res$crossentropy, 0.2309402)
# run tess3 with OQA
set.seed(0)
tess3.res <- tess3Main(X = masked.X,
coord = data.for.test$coord,
K = 3,
ploidy = 1,
lambda = 1.0,
method = "qp")
# consistent error ?
expect_lt(ComputeRmseWithBestPermutation(data.for.test$Q, tess3.res$Q), 0.05)
expect_lt(ComputeRmseWithBestPermutation(data.for.test$G, tess3.res$G), 0.1)
# Consistent Fst ?
expect_lt(sqrt(mean((Fst - tess3.res$Fst) ^ 2)), 0.09)
# rmse and cross entropy
expect_lte(tess3.res$rmse, 0.3608441)
expect_lte(tess3.res$crossentropy, 0.2309072)
})
test_that("TESS3 main Fst", {
data("data.for.test", package = "tess3r")
set.seed(0)
tess3.res <- tess3Main(X = data.for.test$X,
coord = data.for.test$coord,
K = 3,
ploidy = 1,
lambda = 1.0,
method = "projected.ls")
# Consistent Fst ?
Fst <- ComputeFst(data.for.test$Q, data.for.test$G, data.for.test$d + 1)
expect_lt(sqrt(mean((Fst - tess3.res$Fst) ^ 2)), 0.08)
# Debug
# hist(tess3.res$Fst)
# hist(tess3.res$p.value)
# BioCompToolsR::manhattanPlot(tess3.res$p.value)
# tess3.res$gif
})
test_that("TESS3 cross validation", {
options(tess3r.debug = "TRUE")
set.seed(354354)
n <- 100
K <- 3
ploidy <- 2
L <- 1000
data.list <- SampleGenoFromGenerativeModelTESS3(G = SampleUnifDirichletG(L, ploidy, K),
Q = SampleUnifQ(n, K),
coord = SampleNormalClusterCoord(n.by.pop = n, K = 1),
ploidy = ploidy)
expect_message(tess3.res <- tess3Main(X = data.list$X,
coord = data.list$coord,
K = K,
ploidy = data.list$ploidy,
lambda = 1.0,
method = "projected.ls",
mask = 0.05), "Mask 0.05% of X for cross validation|Missing value detected in genotype")
expect_lte(round(ComputeRmseWithBestPermutation(data.list$G, tess3.res$G),3), 0.086)
expect_lte(round(ComputeRmseWithBestPermutation(data.list$Q, tess3.res$Q),3), 0.097)
expect_lte(round(tess3.res$crossvalid.rmse,3), 0.393)
expect_lte(round(tess3.res$rmse,3), 0.379)
expect_lte(round(tess3.res$crossentropy,3), 0.511)
expect_lte(round(tess3.res$crossvalid.crossentropy,3), 0.528)
# With already missing values
set.seed(54354)
n <- 120
K <- 4
ploidy <- 3
L <- 3000
data.list <- SampleGenoFromGenerativeModelTESS3(G = SampleUnifDirichletG(L, ploidy, K),
Q = SampleUnifQ(n, K),
coord = SampleNormalClusterCoord(n.by.pop = n, K = 1),
ploidy = ploidy)
data.list$X[sample(seq_along(data.list$X), 0.5 * length(data.list$X))] <- NA
expect_message(tess3.res <- tess3Main(X = data.list$X,
coord = data.list$coord,
K = K,
ploidy = data.list$ploidy,
lambda = 1.0,
method = "projected.ls",
mask = 0.5), "Mask 0.05% of X for cross validation|Missing value detected in genotype")
expect_lte(round(ComputeRmseWithBestPermutation(data.list$G, tess3.res$G),3), 0.235)
expect_lte(round(ComputeRmseWithBestPermutation(data.list$Q, tess3.res$Q),3), 0.239)
expect_lte(round(tess3.res$crossvalid.rmse,3), 0.396)
expect_lte(round(tess3.res$rmse,3), 0.197) # because rmse is computed on naive impuation of X
expect_lte(round(tess3.res$crossentropy,3), 0.847)
expect_lte(round(tess3.res$crossvalid.crossentropy,3), 0.907)
})
cayek/TESS3_encho_sen documentation built on July 4, 2023, 7:51 p.m.
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context("TESS3 main")
test_that("TESS3 copy", {
set.seed(357467)
n <- 100
K <- 3
ploidy <- 1
L <- 3001
data.list <- SampleGenoFromGenerativeModelTESS3(G = SampleUnifDirichletG(L, ploidy, K),
Q = SampleUnifQ(n, K),
coord = SampleNormalClusterCoord(n.by.pop = n, K = 1),
ploidy = ploidy)
data.list$XBin <- matrix(0.0, n, L * (ploidy + 1))
X2XBin(data.list$X, data.list$ploidy, data.list$XBin)
expect_error(tess3.res <- tess3Main(X = data.list$X,
XProba = NULL,
coord = data.list$coord,
K,
ploidy,
lambda = 1.0,
W = NULL,
method = "projected.ls",
max.iteration = 200,
tolerance = 1e-5,
openMP.core.num = 4,
Q.init = NULL,
mask = 0.0,
copy = FALSE),
"To force the function not doing copy of the data, you must set XProba")
expect_error(tess3.res <- tess3Main(X = NULL,
XProba = data.list$X,
coord = data.list$coord,
K,
ploidy,
lambda = 1.0,
W = NULL,
method = "projected.ls",
max.iteration = 200,
tolerance = 1e-5,
openMP.core.num = 4,
Q.init = NULL,
mask = 0.0,
copy = FALSE),
"Number of columns of XProba must be a multiple of ploidy \\+ 1")
tess3.res <- tess3Main(X = NULL,
XProba = data.list$XBin,
coord = data.list$coord,
K,
ploidy,
lambda = 1.0,
W = NULL,
method = "projected.ls",
max.iteration = 200,
tolerance = 1e-5,
openMP.core.num = 4,
Q.init = NULL,
mask = 0.0,
copy = FALSE)
})
test_that("TESS3 to dataframe", {
set.seed(57467)
n <- 100
K <- 6
ploidy <- 4
L <- 3001
data.list <- SampleGenoFromGenerativeModelTESS3(G = SampleUnifDirichletG(L, ploidy, K),
Q = SampleUnifQ(n, K),
coord = SampleNormalClusterCoord(n.by.pop = n, K = 1),
ploidy = ploidy)
data.list$XBin <- matrix(0.0, n, L * (ploidy + 1))
X2XBin(data.list$X, data.list$ploidy, data.list$XBin)
tess3.res <- tess3Main(X = NULL,
XProba = data.list$XBin,
coord = data.list$coord,
K,
ploidy,
lambda = 1.0,
W = NULL,
method = "projected.ls",
max.iteration = 200,
tolerance = 1e-5,
openMP.core.num = 4)
df <- data.frame(log.pvalue = tess3.res$log.pvalue,
fst = tess3.res$Fst,
Fscore = tess3.res$Fscore,
pvalue = tess3.res$pvalue,
index = seq_along(tess3.res$log.pvalue))
df <- data.frame(tess3.res$Q)
df <- data.frame(tess3.res$G)
})
test_that("TESS3 main, algo.copy and XBin", {
set.seed(698)
n <- 100
K <- 3
ploidy <- 2
L <- 3000
data.list <- SampleGenoFromGenerativeModelTESS3(G = SampleUnifDirichletG(L, ploidy, K),
Q = SampleUnifQ(n, K),
coord = SampleNormalClusterCoord(n.by.pop = n, K = 1),
ploidy = ploidy)
data.list$XBin <- matrix(0.0, n, L * (ploidy + 1))
X2XBin(data.list$X, data.list$ploidy, data.list$XBin)
set.seed(687)
tess3.res.nocopy <- tess3Main(X = NULL,
XProba = data.list$XBin,
coord = data.list$coord,
K,
ploidy,
lambda = 1.0,
W = NULL,
method = "projected.ls",
max.iteration = 200,
tolerance = 1e-5,
openMP.core.num = 4,
Q.init = NULL,
mask = 0.0,
algo.copy = FALSE)
set.seed(687)
tess3.res.copy <- tess3Main(X = NULL,
XProba = data.list$XBin,
coord = data.list$coord,
K,
ploidy,
lambda = 1.0,
W = NULL,
method = "projected.ls",
max.iteration = 200,
tolerance = 1e-5,
openMP.core.num = 4,
Q.init = NULL,
mask = 0.0,
algo.copy = TRUE)
set.seed(687)
tess3.res.copy.X <- tess3Main(X = data.list$X,
XProba = NULL,
coord = data.list$coord,
K,
ploidy,
lambda = 1.0,
W = NULL,
method = "projected.ls",
max.iteration = 200,
tolerance = 1e-5,
openMP.core.num = 4,
Q.init = NULL,
mask = 0.0,
algo.copy = TRUE)
set.seed(687)
tess3.res.nocopy.X <- tess3Main(X = data.list$X,
XProba = NULL,
coord = data.list$coord,
K,
ploidy,
lambda = 1.0,
W = NULL,
method = "projected.ls",
max.iteration = 200,
tolerance = 1e-5,
openMP.core.num = 4,
Q.init = NULL,
mask = 0.0,
algo.copy = FALSE)
expect_lt(ComputeRmseWithBestPermutation(tess3.res.copy$Q, tess3.res.nocopy$Q), 1e-15)
expect_lt(ComputeRmseWithBestPermutation(tess3.res.copy$G, tess3.res.nocopy$G), 1e-15)
expect_lt(ComputeRmseWithBestPermutation(tess3.res.copy.X$G, tess3.res.nocopy$G), 1e-15)
expect_lt(ComputeRmseWithBestPermutation(tess3.res.copy.X$Q, tess3.res.nocopy$Q), 1e-15)
expect_lt(ComputeRmseWithBestPermutation(tess3.res.nocopy.X$G, tess3.res.nocopy$G), 1e-200)
expect_lt(ComputeRmseWithBestPermutation(tess3.res.nocopy.X$Q, tess3.res.nocopy$Q), 1e-200)
})
test_that("TESS3 main with method MCPA", {
data("data.for.test", package = "tess3r")
set.seed(0)
tess3.res <- tess3Main(X = data.for.test$X,
coord = data.for.test$coord,
K = 3,
ploidy = 1,
lambda = 1.0,
method = "projected.ls")
# consistent error ?
expect_lt(ComputeRmseWithBestPermutation(data.for.test$Q, tess3.res$Q), 0.03)
expect_lt(ComputeRmseWithBestPermutation(data.for.test$G, tess3.res$G), 0.06)
# Consistent Fst ?
Fst <- ComputeFst(data.for.test$Q, data.for.test$G, data.for.test$d + 1)
expect_lt(sqrt(mean((Fst - tess3.res$Fst) ^ 2)), 0.08)
# with a Q.init
set.seed(0)
K = 3
Q.init <- matrix(runif(nrow(data.for.test$X) * K), nrow(data.for.test$X), K)
Q.init <- ProjectQ(Q.init)
tess3.res <- tess3Main(X = data.for.test$X,
coord = data.for.test$coord,
K = K,
ploidy = 1,
lambda = 1.0,
method = "projected.ls",
Q.init = Q.init)
# consistent error ?
expect_lt(ComputeRmseWithBestPermutation(data.for.test$Q, tess3.res$Q), 0.03)
expect_lt(ComputeRmseWithBestPermutation(data.for.test$G, tess3.res$G), 0.06)
# rmse and cross entropy
expect_lte(tess3.res$rmse, 0.3712458)
expect_lte(tess3.res$crossentropy, 0.2111163)
})
test_that("TESS3 main with method OQA", {
data("data.for.test", package = "tess3r")
set.seed(0)
tess3.res <- tess3Main(X = data.for.test$X,
coord = data.for.test$coord,
K = 3,
ploidy = 1,
lambda = 1.0,
method = "qp",
tolerance = 0.00001)
# consistent error ?
expect_lt(ComputeRmseWithBestPermutation(data.for.test$Q, tess3.res$Q), 0.03)
expect_lt(ComputeRmseWithBestPermutation(data.for.test$G, tess3.res$G), 0.06)
# Consistent Fst ?
Fst <- ComputeFst(data.for.test$Q, data.for.test$G, data.for.test$d + 1)
expect_lt(sqrt(mean((Fst - tess3.res$Fst) ^ 2)), 0.08)
# rmse and cross entropy
expect_lte(tess3.res$rmse, 0.3712458)
expect_lte(round(tess3.res$crossentropy,3), 0.211)
})
test_that("TESS3 main check arg", {
data("data.for.test", package = "tess3r")
expect_error(tess3.res <- tess3Main(X = data.for.test$X,
coord = data.for.test$coord,
K = 3,
ploidy = 1,
lambda = 1.0,
method = "OA",
tolerance = 0.00001),"method must be projected.ls or qp")
W <- matrix(2, nrow = 3)
expect_error(tess3.res <- tess3Main(X = data.for.test$X,
coord = data.for.test$coord,
K = 3,
ploidy = 1,
lambda = 1.0,
W = W,
tolerance = 0.00001),"W must be a squared symmetric matrix")
W = matrix(runif(data.for.test$n ^ 2), data.for.test$n,data.for.test$n)
expect_error(tess3.res <- tess3Main(X = data.for.test$X,
coord = data.for.test$coord,
K = 3,
ploidy = 1,
lambda = 1.0,
W = W,
tolerance = 0.00001),"W must be a squared symmetric matrix")
expect_error(tess3Main(X = data.for.test$X,
coord = data.for.test$coord,
K = 3,
ploidy = 0,
lambda = 1.0,
method = "projected.ls",
tolerance = 0.00001),
"The maximum value of the genotype matrix \\(X\\) cannot be greater than ploidy \\+ 1\\. Missing data must be encoded as NA\\.")
genotype <- data.for.test$X
genotype[1,1] <- -9
expect_error(tess3Main(X = genotype,
coord = data.for.test$coord,
K = 3,
ploidy = 1,
lambda = 1.0,
method = "projected.ls",
tolerance = 0.00001),
"Negative values in the genotype matrix \\(X\\) are not allowed\\. Missing data must be encoded as NA\\.")
expect_error(tess3Main(X = data.for.test$X,
coord = data.for.test$coord[-1,],
K = 3,
ploidy = 1,
lambda = 1.0,
method = "projected.ls",
tolerance = 0.00001),"Number of row of coord and X/XBin must be the same")
})
test_that("TESS3 main with missing value", {
data("data.for.test", package = "tess3r")
# mask data
set.seed(0)
masked.prop <- 0.1
masked.X <- data.for.test$X
masked.X[sample(1:(ncol(masked.X)*nrow(masked.X)), (ncol(masked.X)*nrow(masked.X)) * masked.prop)] <- NA
# run tess3 with MCPA
tess3.res <- tess3Main(X = masked.X,
coord = data.for.test$coord,
K = 3,
ploidy = 1,
lambda = 1.0,
method = "projected.ls")
# consistent error ?
expect_lt(ComputeRmseWithBestPermutation(data.for.test$Q, tess3.res$Q), 0.05)
expect_lt(ComputeRmseWithBestPermutation(data.for.test$G, tess3.res$G), 0.1)
# Consistent Fst ?
Fst <- ComputeFst(data.for.test$Q, data.for.test$G, data.for.test$d + 1)
expect_lt(sqrt(mean((Fst - tess3.res$Fst) ^ 2)), 0.09)
# rmse and cross entropy
expect_lte(tess3.res$rmse, 0.3608639)
expect_lte(tess3.res$crossentropy, 0.2309402)
# run tess3 with OQA
set.seed(0)
tess3.res <- tess3Main(X = masked.X,
coord = data.for.test$coord,
K = 3,
ploidy = 1,
lambda = 1.0,
method = "qp")
# consistent error ?
expect_lt(ComputeRmseWithBestPermutation(data.for.test$Q, tess3.res$Q), 0.05)
expect_lt(ComputeRmseWithBestPermutation(data.for.test$G, tess3.res$G), 0.1)
# Consistent Fst ?
expect_lt(sqrt(mean((Fst - tess3.res$Fst) ^ 2)), 0.09)
# rmse and cross entropy
expect_lte(tess3.res$rmse, 0.3608441)
expect_lte(tess3.res$crossentropy, 0.2309072)
})
test_that("TESS3 main Fst", {
data("data.for.test", package = "tess3r")
set.seed(0)
tess3.res <- tess3Main(X = data.for.test$X,
coord = data.for.test$coord,
K = 3,
ploidy = 1,
lambda = 1.0,
method = "projected.ls")
# Consistent Fst ?
Fst <- ComputeFst(data.for.test$Q, data.for.test$G, data.for.test$d + 1)
expect_lt(sqrt(mean((Fst - tess3.res$Fst) ^ 2)), 0.08)
# Debug
# hist(tess3.res$Fst)
# hist(tess3.res$p.value)
# BioCompToolsR::manhattanPlot(tess3.res$p.value)
# tess3.res$gif
})
test_that("TESS3 cross validation", {
options(tess3r.debug = "TRUE")
set.seed(354354)
n <- 100
K <- 3
ploidy <- 2
L <- 1000
data.list <- SampleGenoFromGenerativeModelTESS3(G = SampleUnifDirichletG(L, ploidy, K),
Q = SampleUnifQ(n, K),
coord = SampleNormalClusterCoord(n.by.pop = n, K = 1),
ploidy = ploidy)
expect_message(tess3.res <- tess3Main(X = data.list$X,
coord = data.list$coord,
K = K,
ploidy = data.list$ploidy,
lambda = 1.0,
method = "projected.ls",
mask = 0.05), "Mask 0.05% of X for cross validation|Missing value detected in genotype")
expect_lte(round(ComputeRmseWithBestPermutation(data.list$G, tess3.res$G),3), 0.086)
expect_lte(round(ComputeRmseWithBestPermutation(data.list$Q, tess3.res$Q),3), 0.097)
expect_lte(round(tess3.res$crossvalid.rmse,3), 0.393)
expect_lte(round(tess3.res$rmse,3), 0.379)
expect_lte(round(tess3.res$crossentropy,3), 0.511)
expect_lte(round(tess3.res$crossvalid.crossentropy,3), 0.528)
# With already missing values
set.seed(54354)
n <- 120
K <- 4
ploidy <- 3
L <- 3000
data.list <- SampleGenoFromGenerativeModelTESS3(G = SampleUnifDirichletG(L, ploidy, K),
Q = SampleUnifQ(n, K),
coord = SampleNormalClusterCoord(n.by.pop = n, K = 1),
ploidy = ploidy)
data.list$X[sample(seq_along(data.list$X), 0.5 * length(data.list$X))] <- NA
expect_message(tess3.res <- tess3Main(X = data.list$X,
coord = data.list$coord,
K = K,
ploidy = data.list$ploidy,
lambda = 1.0,
method = "projected.ls",
mask = 0.5), "Mask 0.05% of X for cross validation|Missing value detected in genotype")
expect_lte(round(ComputeRmseWithBestPermutation(data.list$G, tess3.res$G),3), 0.235)
expect_lte(round(ComputeRmseWithBestPermutation(data.list$Q, tess3.res$Q),3), 0.239)
expect_lte(round(tess3.res$crossvalid.rmse,3), 0.396)
expect_lte(round(tess3.res$rmse,3), 0.197) # because rmse is computed on naive impuation of X
expect_lte(round(tess3.res$crossentropy,3), 0.847)
expect_lte(round(tess3.res$crossvalid.crossentropy,3), 0.907)
})
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