tests/testthat/testConditions.R
In statOmics/tradeSeq: trajectory-based differential expression analysis for sequencing data
context("tradeSeq works as expected with a condition vector")
# Create data ----
data("sds", package = "tradeSeq")
set.seed(3)
n <- nrow(reducedDim(sds))
G <- 100
pseudotime <- slingPseudotime(sds, na = FALSE)
cellWeights <- slingCurveWeights(sds)
means <- matrix(rep(rlnorm(n = G, meanlog = 4, sdlog = 1), n),
nrow = G, ncol = n, byrow = FALSE
)
dispersions <- matrix(rep(runif(n = G, min = 0.8, max = 3), n),
nrow = G, ncol = n, byrow = FALSE
)
# add pseudotime effects for a few
id <- sample(1:100, 20)
means[id, ] <- sweep(means[id, ], 2, FUN = "*", STATS = (pseudotime[, 1] / 50))
# simulate NB counts
counts <- matrix(rnbinom(n = G * n, mu = means, size = 1 / dispersions),
nrow = G, ncol = n)
sce <- SingleCellExperiment(assays = list(counts = counts))
colData(sce)$slingshot <- slingshot::as.PseudotimeOrdering(sds)
sce$conditions <- conditions <- as.factor(sample(1:5, ncol(sce), replace = TRUE))
rm(id, counts, G, n, pseudotime, dispersions, means, cellWeights)
# Do the tests ----
test_that("fitGAM works when receiving conditions as input", {
colInput <- tradeSeq::fitGAM(sce, nknots = 3, verbose = FALSE,
conditions = "conditions")
expect_s4_class(colInput, "SingleCellExperiment")
vecInput <- tradeSeq::fitGAM(sce, nknots = 3, verbose = FALSE,
conditions = conditions)
expect_s4_class(vecInput, "SingleCellExperiment")
})
test_that("Different conditions format give the same output", {
set.seed(1031)
colInput <- tradeSeq::fitGAM(sce, nknots = 3, verbose = FALSE,
conditions = "conditions")
set.seed(1031)
vecInput <- tradeSeq::fitGAM(sce, nknots = 3, verbose = FALSE,
conditions = conditions)
# Beta coefficients are the same
betaColInput <- as.matrix(rowData(colInput)$tradeSeq$beta)
betaVecInput <- as.matrix(rowData(vecInput)$tradeSeq$beta)
dimnames(betaColInput) <- dimnames(betaVecInput)
expect_equal(betaVecInput, betaColInput)
# Sigma coefficients are the same
SigmaColInput <- rowData(colInput)$tradeSeq$Sigma
SigmaVecInput <- rowData(vecInput)$tradeSeq$Sigma
names(SigmaColInput) <- names(SigmaVecInput)
expect_equal(SigmaColInput, SigmaVecInput)
})
test_that("Different encoding of the same condition give the same output", {
set.seed(1031)
condInput <- tradeSeq::fitGAM(sce, nknots = 3, verbose = FALSE,
conditions = "conditions")
set.seed(1031)
conditions2 <- as.factor(as.numeric(conditions) + 1)
cond2Input <- tradeSeq::fitGAM(sce, nknots = 3, verbose = FALSE,
conditions = conditions2)
# Beta coefficients are the same
betaInput <- as.matrix(rowData(condInput)$tradeSeq$beta)
beta2Input <- as.matrix(rowData(cond2Input)$tradeSeq$beta)
dimnames(betaInput) <- dimnames(beta2Input)
expect_equal(betaInput, beta2Input)
# Sigma coefficients are the same
SigmaInput <- rowData(condInput)$tradeSeq$Sigma
Sigma2Input <- rowData(cond2Input)$tradeSeq$Sigma
names(SigmaInput) <- names(Sigma2Input)
expect_equal(SigmaInput, Sigma2Input)
})
test_that("One condition give the same result as no conditions", {
set.seed(1031)
condInput <- tradeSeq::fitGAM(sce, nknots = 3, verbose = FALSE)
set.seed(1031)
cond2Input <- tradeSeq::fitGAM(sce, nknots = 3, verbose = FALSE,
conditions = as.factor(rep(1, ncol(sce))))
# Beta coefficients are the same
betaInput <- as.matrix(rowData(condInput)$tradeSeq$beta)
beta2Input <- as.matrix(rowData(cond2Input)$tradeSeq$beta)
dimnames(betaInput) <- dimnames(beta2Input)
expect_equal(betaInput, beta2Input)
# Sigma coefficients are the same
SigmaInput <- rowData(condInput)$tradeSeq$Sigma
Sigma2Input <- rowData(cond2Input)$tradeSeq$Sigma
names(SigmaInput) <- names(Sigma2Input)
expect_equal(SigmaInput, Sigma2Input)
expect_error(conditionTest(condInput))
expect_error(conditionTest(cond2Input))
})
test_that("All tests work", {
cellWeights <- slingCurveWeights(sce)
counts <- SingleCellExperiment::counts(sce)
pseudotime <- slingPseudotime(sds, na = FALSE)
expect_s4_class(Fit <- tradeSeq::fitGAM(counts = counts,
pseudotime = pseudotime, cellWeights = cellWeights,
nknots = 3, verbose = FALSE, conditions = conditions),
"SingleCellExperiment")
expect_is(associationTest(Fit), "data.frame")
expect_is(startVsEndTest(Fit), "data.frame")
expect_is(diffEndTest(Fit), "data.frame")
expect_is(conditionTest(Fit), "data.frame")
expect_is(patternTest(Fit), "data.frame")
expect_is(earlyDETest(Fit, knots = 1:3), "data.frame")
})
test_that("Clustering work", {
cellWeights <- slingCurveWeights(sce)
counts <- SingleCellExperiment::counts(sce)
pseudotime <- slingPseudotime(sds, na = FALSE)
conditions <- as.factor(sample(1:2, ncol(sce), replace = TRUE))
Fit <- tradeSeq::fitGAM(counts = counts, pseudotime = pseudotime,
cellWeights = cellWeights, nknots = 3,
verbose = FALSE, conditions = conditions)
#
PatSce <- tradeSeq::clusterExpressionPatterns(Fit, nPoints = 50, genes = 1:50,
k0s = 4:5, alphas = 0.1)
expect_is(PatSce, "list")
})
test_that("Condition works with one lineage", {
cellWeights <- slingCurveWeights(sce)
keep <- cellWeights[, 1] > 0
counts <- SingleCellExperiment::counts(sce)[, keep]
pseudotime <- slingPseudotime(sce)[keep, 1]
cellWeights <- cellWeights[keep, 1]
expect_s4_class(Fit <- tradeSeq::fitGAM(counts = counts, pseudotime = pseudotime,
cellWeights = cellWeights, nknots = 3, verbose = FALSE,
conditions = conditions[keep]),
"SingleCellExperiment")
expect_is(startVsEndTest(Fit), "data.frame")
expect_is(associationTest(Fit, lineages = TRUE), "data.frame")
expect_is(conditionTest(Fit), "data.frame")
expect_error(patternTest(Fit))
expect_error(earlyDETest(Fit, knots = 1:2))
expect_error(diffEndTest(Fit, knots = 1:2))
})
test_that("Condition work correctly with predictSmooth", {
cellWeights <- slingCurveWeights(sce)
counts <- SingleCellExperiment::counts(sce)
pseudotime <- slingPseudotime(sds, na = FALSE)
Fit <- tradeSeq::fitGAM(counts = counts,
pseudotime = pseudotime, cellWeights = cellWeights,
nknots = 3, verbose = FALSE, conditions = conditions)
expect_is(
df <- tradeSeq::predictSmooth(Fit, gene = seq_len(nrow(Fit)), nPoints = 40),
"data.frame")
expect_equal(dim(df), c(40 * nrow(Fit) * 5 * 2, 5))
expect_equal(dim(predictCells(Fit, gene = seq_len(nrow(Fit)))), dim(counts))
})
test_that("Condition work correctly with plotSmoothers", {
cellWeights <- slingCurveWeights(sce)
counts <- SingleCellExperiment::counts(sce)
pseudotime <- slingPseudotime(sds, na = FALSE)
Fit <- tradeSeq::fitGAM(counts = counts,
pseudotime = pseudotime, cellWeights = cellWeights,
nknots = 3, verbose = FALSE, conditions = conditions)
expect_is(plotSmoothers(Fit, counts = counts, gene = 1), "gg")
expect_is(plotSmoothers(Fit, counts = counts, gene = 1, border = FALSE), "gg")
expect_is(plotSmoothers(Fit, gene = 1, counts = counts,
pointCol = rep("black", ncol(Fit))), "gg")
Fit$color <- rep("black", ncol(Fit))
expect_is(plotSmoothers(Fit, gene = 1, counts = counts, pointCol = "color"), "gg")
expect_message(plotSmoothers(Fit, gene = 1, counts = counts,
pointCol = rep("black", 3)))
expect_is(plotSmoothers(Fit, gene = 1, counts = counts,
curvesCol = rep("black", 10)), "gg")
expect_message(plotSmoothers(Fit, gene = 1, counts = counts,
curvesCol = rep("black", 2)))
})
test_that("conditionTest work with options", {
cellWeights <- slingCurveWeights(sce)
counts <- SingleCellExperiment::counts(sce)
pseudotime <- slingPseudotime(sds, na = FALSE)
Fit <- tradeSeq::fitGAM(counts = counts,
pseudotime = pseudotime, cellWeights = cellWeights,
nknots = 3, verbose = FALSE, conditions = conditions)
# With pairwise and lineages
expect_is(df <- tradeSeq::conditionTest(Fit), "data.frame")
expect_equal(dim(df), c(nrow(Fit), 3))
expect_is(df <- tradeSeq::conditionTest(Fit, pairwise = TRUE), "data.frame")
expect_equal(dim(df), c(nrow(Fit), 3 + 3 * ncol(combn(5, 2))))
expect_is(df <- tradeSeq::conditionTest(Fit, lineages = TRUE), "data.frame")
expect_equal(dim(df), c(nrow(Fit), 3 + 3 * 2))
expect_is(df <- tradeSeq::conditionTest(Fit, pairwise = TRUE, lineages = TRUE),
"data.frame")
expect_equal(dim(df), c(nrow(Fit), 3 + 3 * 2 * ncol(combn(5, 2))))
expect_error(df <- tradeSeq::conditionTest(Fit, global = FALSE))
expect_is(df <- tradeSeq::conditionTest(Fit, global = FALSE, pairwise = TRUE), "data.frame")
expect_equal(dim(df), c(nrow(Fit), 3 * ncol(combn(5, 2))))
expect_is(df <- tradeSeq::conditionTest(Fit, global = FALSE, lineages = TRUE), "data.frame")
expect_equal(dim(df), c(nrow(Fit), 3 * 2))
expect_is(df <- tradeSeq::conditionTest(Fit, global = FALSE, pairwise = TRUE, lineages = TRUE),
"data.frame")
expect_equal(dim(df), c(nrow(Fit), 3 * 2 * ncol(combn(5, 2))))
# With knots
expect_is(df <- tradeSeq::conditionTest(Fit, knots = c(1:2)), "data.frame")
expect_is(df <- tradeSeq::conditionTest(Fit, knots = c(2:3)), "data.frame")
df1 <- tradeSeq::conditionTest(Fit, knots = c(1,3))
df2 <- tradeSeq::conditionTest(Fit)
expect_equal(df1, df2)
expect_equal(dim(df), c(nrow(Fit), 3))
expect_error(tradeSeq::conditionTest(Fit, knots = c(1:4)))
expect_error(tradeSeq::conditionTest(Fit, knots = 1))
})
test_that("associationTest works with conditions", {
cellWeights <- slingCurveWeights(sce)
counts <- SingleCellExperiment::counts(sce)
pseudotime <- slingPseudotime(sds, na = FALSE)
Fit <- tradeSeq::fitGAM(counts = counts,
pseudotime = pseudotime, cellWeights = cellWeights,
nknots = 3, verbose = FALSE, conditions = conditions)
# With global and lineages for default contrastType start
expect_is(df <- tradeSeq::associationTest(Fit), "data.frame")
expect_is(df <- tradeSeq::associationTest(Fit, lineages = TRUE), "data.frame")
expect_is(df <- tradeSeq::associationTest(Fit, global = FALSE, lineages = TRUE), "data.frame")
# With global and lineages for contrastType end
expect_is(df <- tradeSeq::associationTest(Fit, contrastType = "end"), "data.frame")
expect_is(df <- tradeSeq::associationTest(Fit, contrastType = "end", lineages = TRUE), "data.frame")
expect_is(df <- tradeSeq::associationTest(Fit, contrastType = "end", global = FALSE, lineages = TRUE), "data.frame")
# With global and lineages for contrastType consecutive
expect_is(df <- tradeSeq::associationTest(Fit, contrastType = "consecutive"), "data.frame")
expect_is(df <- tradeSeq::associationTest(Fit, contrastType = "consecutive", lineages = TRUE), "data.frame")
expect_is(df <- tradeSeq::associationTest(Fit, contrastType = "consecutive", global = FALSE, lineages = TRUE), "data.frame")
# different contrastTypes give similar results if no l2fc cut-off for global test
dfStart <- tradeSeq::associationTest(Fit, contrastType = "start")
dfEnd <- tradeSeq::associationTest(Fit, contrastType = "end")
dfConsecutive <- tradeSeq::associationTest(Fit, contrastType = "consecutive")
expect_true(max(abs(dfStart$waldStat - dfEnd$waldStat), na.rm = TRUE) < 1e-3)
expect_true(max(abs(dfStart$waldStat - dfConsecutive$waldStat), na.rm = TRUE) < 1e-3)
expect_true(max(abs(dfConsecutive$waldStat - dfEnd$waldStat), na.rm = TRUE) < 1e-3)
# different contrastTypes give different results with l2fc cut-off
dfStartFC <- tradeSeq::associationTest(Fit, contrastType = "start", l2fc = 1)
dfEndFC <- tradeSeq::associationTest(Fit, contrastType = "end", l2fc = 1)
dfConsecutiveFC <- tradeSeq::associationTest(Fit, contrastType = "consecutive", l2fc = 1)
expect_true(max(abs(dfStartFC$waldStat - dfEndFC$waldStat), na.rm = TRUE) > 1e-3)
expect_true(max(abs(dfStartFC$waldStat - dfConsecutiveFC$waldStat), na.rm = TRUE) > 1e-3)
expect_true(max(abs(dfConsecutiveFC$waldStat - dfEndFC$waldStat), na.rm = TRUE) > 1e-3)
# Wrong contrastType
expect_error(tradeSeq::associationTest(Fit, l2fc = 1, contrastType = "wrong"))
})
statOmics/tradeSeq documentation built on Jan. 19, 2024, 8:26 p.m.
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context("tradeSeq works as expected with a condition vector")
# Create data ----
data("sds", package = "tradeSeq")
set.seed(3)
n <- nrow(reducedDim(sds))
G <- 100
pseudotime <- slingPseudotime(sds, na = FALSE)
cellWeights <- slingCurveWeights(sds)
means <- matrix(rep(rlnorm(n = G, meanlog = 4, sdlog = 1), n),
nrow = G, ncol = n, byrow = FALSE
)
dispersions <- matrix(rep(runif(n = G, min = 0.8, max = 3), n),
nrow = G, ncol = n, byrow = FALSE
)
# add pseudotime effects for a few
id <- sample(1:100, 20)
means[id, ] <- sweep(means[id, ], 2, FUN = "*", STATS = (pseudotime[, 1] / 50))
# simulate NB counts
counts <- matrix(rnbinom(n = G * n, mu = means, size = 1 / dispersions),
nrow = G, ncol = n)
sce <- SingleCellExperiment(assays = list(counts = counts))
colData(sce)$slingshot <- slingshot::as.PseudotimeOrdering(sds)
sce$conditions <- conditions <- as.factor(sample(1:5, ncol(sce), replace = TRUE))
rm(id, counts, G, n, pseudotime, dispersions, means, cellWeights)
# Do the tests ----
test_that("fitGAM works when receiving conditions as input", {
colInput <- tradeSeq::fitGAM(sce, nknots = 3, verbose = FALSE,
conditions = "conditions")
expect_s4_class(colInput, "SingleCellExperiment")
vecInput <- tradeSeq::fitGAM(sce, nknots = 3, verbose = FALSE,
conditions = conditions)
expect_s4_class(vecInput, "SingleCellExperiment")
})
test_that("Different conditions format give the same output", {
set.seed(1031)
colInput <- tradeSeq::fitGAM(sce, nknots = 3, verbose = FALSE,
conditions = "conditions")
set.seed(1031)
vecInput <- tradeSeq::fitGAM(sce, nknots = 3, verbose = FALSE,
conditions = conditions)
# Beta coefficients are the same
betaColInput <- as.matrix(rowData(colInput)$tradeSeq$beta)
betaVecInput <- as.matrix(rowData(vecInput)$tradeSeq$beta)
dimnames(betaColInput) <- dimnames(betaVecInput)
expect_equal(betaVecInput, betaColInput)
# Sigma coefficients are the same
SigmaColInput <- rowData(colInput)$tradeSeq$Sigma
SigmaVecInput <- rowData(vecInput)$tradeSeq$Sigma
names(SigmaColInput) <- names(SigmaVecInput)
expect_equal(SigmaColInput, SigmaVecInput)
})
test_that("Different encoding of the same condition give the same output", {
set.seed(1031)
condInput <- tradeSeq::fitGAM(sce, nknots = 3, verbose = FALSE,
conditions = "conditions")
set.seed(1031)
conditions2 <- as.factor(as.numeric(conditions) + 1)
cond2Input <- tradeSeq::fitGAM(sce, nknots = 3, verbose = FALSE,
conditions = conditions2)
# Beta coefficients are the same
betaInput <- as.matrix(rowData(condInput)$tradeSeq$beta)
beta2Input <- as.matrix(rowData(cond2Input)$tradeSeq$beta)
dimnames(betaInput) <- dimnames(beta2Input)
expect_equal(betaInput, beta2Input)
# Sigma coefficients are the same
SigmaInput <- rowData(condInput)$tradeSeq$Sigma
Sigma2Input <- rowData(cond2Input)$tradeSeq$Sigma
names(SigmaInput) <- names(Sigma2Input)
expect_equal(SigmaInput, Sigma2Input)
})
test_that("One condition give the same result as no conditions", {
set.seed(1031)
condInput <- tradeSeq::fitGAM(sce, nknots = 3, verbose = FALSE)
set.seed(1031)
cond2Input <- tradeSeq::fitGAM(sce, nknots = 3, verbose = FALSE,
conditions = as.factor(rep(1, ncol(sce))))
# Beta coefficients are the same
betaInput <- as.matrix(rowData(condInput)$tradeSeq$beta)
beta2Input <- as.matrix(rowData(cond2Input)$tradeSeq$beta)
dimnames(betaInput) <- dimnames(beta2Input)
expect_equal(betaInput, beta2Input)
# Sigma coefficients are the same
SigmaInput <- rowData(condInput)$tradeSeq$Sigma
Sigma2Input <- rowData(cond2Input)$tradeSeq$Sigma
names(SigmaInput) <- names(Sigma2Input)
expect_equal(SigmaInput, Sigma2Input)
expect_error(conditionTest(condInput))
expect_error(conditionTest(cond2Input))
})
test_that("All tests work", {
cellWeights <- slingCurveWeights(sce)
counts <- SingleCellExperiment::counts(sce)
pseudotime <- slingPseudotime(sds, na = FALSE)
expect_s4_class(Fit <- tradeSeq::fitGAM(counts = counts,
pseudotime = pseudotime, cellWeights = cellWeights,
nknots = 3, verbose = FALSE, conditions = conditions),
"SingleCellExperiment")
expect_is(associationTest(Fit), "data.frame")
expect_is(startVsEndTest(Fit), "data.frame")
expect_is(diffEndTest(Fit), "data.frame")
expect_is(conditionTest(Fit), "data.frame")
expect_is(patternTest(Fit), "data.frame")
expect_is(earlyDETest(Fit, knots = 1:3), "data.frame")
})
test_that("Clustering work", {
cellWeights <- slingCurveWeights(sce)
counts <- SingleCellExperiment::counts(sce)
pseudotime <- slingPseudotime(sds, na = FALSE)
conditions <- as.factor(sample(1:2, ncol(sce), replace = TRUE))
Fit <- tradeSeq::fitGAM(counts = counts, pseudotime = pseudotime,
cellWeights = cellWeights, nknots = 3,
verbose = FALSE, conditions = conditions)
#
PatSce <- tradeSeq::clusterExpressionPatterns(Fit, nPoints = 50, genes = 1:50,
k0s = 4:5, alphas = 0.1)
expect_is(PatSce, "list")
})
test_that("Condition works with one lineage", {
cellWeights <- slingCurveWeights(sce)
keep <- cellWeights[, 1] > 0
counts <- SingleCellExperiment::counts(sce)[, keep]
pseudotime <- slingPseudotime(sce)[keep, 1]
cellWeights <- cellWeights[keep, 1]
expect_s4_class(Fit <- tradeSeq::fitGAM(counts = counts, pseudotime = pseudotime,
cellWeights = cellWeights, nknots = 3, verbose = FALSE,
conditions = conditions[keep]),
"SingleCellExperiment")
expect_is(startVsEndTest(Fit), "data.frame")
expect_is(associationTest(Fit, lineages = TRUE), "data.frame")
expect_is(conditionTest(Fit), "data.frame")
expect_error(patternTest(Fit))
expect_error(earlyDETest(Fit, knots = 1:2))
expect_error(diffEndTest(Fit, knots = 1:2))
})
test_that("Condition work correctly with predictSmooth", {
cellWeights <- slingCurveWeights(sce)
counts <- SingleCellExperiment::counts(sce)
pseudotime <- slingPseudotime(sds, na = FALSE)
Fit <- tradeSeq::fitGAM(counts = counts,
pseudotime = pseudotime, cellWeights = cellWeights,
nknots = 3, verbose = FALSE, conditions = conditions)
expect_is(
df <- tradeSeq::predictSmooth(Fit, gene = seq_len(nrow(Fit)), nPoints = 40),
"data.frame")
expect_equal(dim(df), c(40 * nrow(Fit) * 5 * 2, 5))
expect_equal(dim(predictCells(Fit, gene = seq_len(nrow(Fit)))), dim(counts))
})
test_that("Condition work correctly with plotSmoothers", {
cellWeights <- slingCurveWeights(sce)
counts <- SingleCellExperiment::counts(sce)
pseudotime <- slingPseudotime(sds, na = FALSE)
Fit <- tradeSeq::fitGAM(counts = counts,
pseudotime = pseudotime, cellWeights = cellWeights,
nknots = 3, verbose = FALSE, conditions = conditions)
expect_is(plotSmoothers(Fit, counts = counts, gene = 1), "gg")
expect_is(plotSmoothers(Fit, counts = counts, gene = 1, border = FALSE), "gg")
expect_is(plotSmoothers(Fit, gene = 1, counts = counts,
pointCol = rep("black", ncol(Fit))), "gg")
Fit$color <- rep("black", ncol(Fit))
expect_is(plotSmoothers(Fit, gene = 1, counts = counts, pointCol = "color"), "gg")
expect_message(plotSmoothers(Fit, gene = 1, counts = counts,
pointCol = rep("black", 3)))
expect_is(plotSmoothers(Fit, gene = 1, counts = counts,
curvesCol = rep("black", 10)), "gg")
expect_message(plotSmoothers(Fit, gene = 1, counts = counts,
curvesCol = rep("black", 2)))
})
test_that("conditionTest work with options", {
cellWeights <- slingCurveWeights(sce)
counts <- SingleCellExperiment::counts(sce)
pseudotime <- slingPseudotime(sds, na = FALSE)
Fit <- tradeSeq::fitGAM(counts = counts,
pseudotime = pseudotime, cellWeights = cellWeights,
nknots = 3, verbose = FALSE, conditions = conditions)
# With pairwise and lineages
expect_is(df <- tradeSeq::conditionTest(Fit), "data.frame")
expect_equal(dim(df), c(nrow(Fit), 3))
expect_is(df <- tradeSeq::conditionTest(Fit, pairwise = TRUE), "data.frame")
expect_equal(dim(df), c(nrow(Fit), 3 + 3 * ncol(combn(5, 2))))
expect_is(df <- tradeSeq::conditionTest(Fit, lineages = TRUE), "data.frame")
expect_equal(dim(df), c(nrow(Fit), 3 + 3 * 2))
expect_is(df <- tradeSeq::conditionTest(Fit, pairwise = TRUE, lineages = TRUE),
"data.frame")
expect_equal(dim(df), c(nrow(Fit), 3 + 3 * 2 * ncol(combn(5, 2))))
expect_error(df <- tradeSeq::conditionTest(Fit, global = FALSE))
expect_is(df <- tradeSeq::conditionTest(Fit, global = FALSE, pairwise = TRUE), "data.frame")
expect_equal(dim(df), c(nrow(Fit), 3 * ncol(combn(5, 2))))
expect_is(df <- tradeSeq::conditionTest(Fit, global = FALSE, lineages = TRUE), "data.frame")
expect_equal(dim(df), c(nrow(Fit), 3 * 2))
expect_is(df <- tradeSeq::conditionTest(Fit, global = FALSE, pairwise = TRUE, lineages = TRUE),
"data.frame")
expect_equal(dim(df), c(nrow(Fit), 3 * 2 * ncol(combn(5, 2))))
# With knots
expect_is(df <- tradeSeq::conditionTest(Fit, knots = c(1:2)), "data.frame")
expect_is(df <- tradeSeq::conditionTest(Fit, knots = c(2:3)), "data.frame")
df1 <- tradeSeq::conditionTest(Fit, knots = c(1,3))
df2 <- tradeSeq::conditionTest(Fit)
expect_equal(df1, df2)
expect_equal(dim(df), c(nrow(Fit), 3))
expect_error(tradeSeq::conditionTest(Fit, knots = c(1:4)))
expect_error(tradeSeq::conditionTest(Fit, knots = 1))
})
test_that("associationTest works with conditions", {
cellWeights <- slingCurveWeights(sce)
counts <- SingleCellExperiment::counts(sce)
pseudotime <- slingPseudotime(sds, na = FALSE)
Fit <- tradeSeq::fitGAM(counts = counts,
pseudotime = pseudotime, cellWeights = cellWeights,
nknots = 3, verbose = FALSE, conditions = conditions)
# With global and lineages for default contrastType start
expect_is(df <- tradeSeq::associationTest(Fit), "data.frame")
expect_is(df <- tradeSeq::associationTest(Fit, lineages = TRUE), "data.frame")
expect_is(df <- tradeSeq::associationTest(Fit, global = FALSE, lineages = TRUE), "data.frame")
# With global and lineages for contrastType end
expect_is(df <- tradeSeq::associationTest(Fit, contrastType = "end"), "data.frame")
expect_is(df <- tradeSeq::associationTest(Fit, contrastType = "end", lineages = TRUE), "data.frame")
expect_is(df <- tradeSeq::associationTest(Fit, contrastType = "end", global = FALSE, lineages = TRUE), "data.frame")
# With global and lineages for contrastType consecutive
expect_is(df <- tradeSeq::associationTest(Fit, contrastType = "consecutive"), "data.frame")
expect_is(df <- tradeSeq::associationTest(Fit, contrastType = "consecutive", lineages = TRUE), "data.frame")
expect_is(df <- tradeSeq::associationTest(Fit, contrastType = "consecutive", global = FALSE, lineages = TRUE), "data.frame")
# different contrastTypes give similar results if no l2fc cut-off for global test
dfStart <- tradeSeq::associationTest(Fit, contrastType = "start")
dfEnd <- tradeSeq::associationTest(Fit, contrastType = "end")
dfConsecutive <- tradeSeq::associationTest(Fit, contrastType = "consecutive")
expect_true(max(abs(dfStart$waldStat - dfEnd$waldStat), na.rm = TRUE) < 1e-3)
expect_true(max(abs(dfStart$waldStat - dfConsecutive$waldStat), na.rm = TRUE) < 1e-3)
expect_true(max(abs(dfConsecutive$waldStat - dfEnd$waldStat), na.rm = TRUE) < 1e-3)
# different contrastTypes give different results with l2fc cut-off
dfStartFC <- tradeSeq::associationTest(Fit, contrastType = "start", l2fc = 1)
dfEndFC <- tradeSeq::associationTest(Fit, contrastType = "end", l2fc = 1)
dfConsecutiveFC <- tradeSeq::associationTest(Fit, contrastType = "consecutive", l2fc = 1)
expect_true(max(abs(dfStartFC$waldStat - dfEndFC$waldStat), na.rm = TRUE) > 1e-3)
expect_true(max(abs(dfStartFC$waldStat - dfConsecutiveFC$waldStat), na.rm = TRUE) > 1e-3)
expect_true(max(abs(dfConsecutiveFC$waldStat - dfEndFC$waldStat), na.rm = TRUE) > 1e-3)
# Wrong contrastType
expect_error(tradeSeq::associationTest(Fit, l2fc = 1, contrastType = "wrong"))
})
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