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# This tests the testLinearModel function.
# library(testthat); library(scran); source("setup.R"); source("test-linear-test.R")
test_that("linear model testing works for contrast vectors in categorical designs", {
y <- matrix(rnorm(10000), ncol=100)
A <- gl(2, 50)
design <- model.matrix(~A)
out <- testLinearModel(y, design, contrast=c(0, 1))
for (i in seq_len(10)) {
Y <- y[i,]
fit <- lm(Y ~ A)
stats <- summary(fit)$coefficients
expect_equal(stats[2,"Pr(>|t|)"], out$p.value[i])
}
# Robust to another formulation of the same comparison:
design2 <- model.matrix(~0 + A)
alt <- testLinearModel(y, design2, contrast=c(-1, 1))
expect_equal(out, alt)
alt <- testLinearModel(y, design)
expect_equal(out, alt)
})
test_that("linear model testing works for contrast vectors with continuous variables", {
y <- matrix(rnorm(10000), ncol=100)
u <- runif(100)
design <- model.matrix(~u)
out <- testLinearModel(y, design, contrast=c(0, 1))
for (i in seq_len(10)) {
Y <- y[i,]
fit <- lm(Y ~ u)
stats <- summary(fit)$coefficients
expect_equal(stats[2,"Pr(>|t|)"], out$p.value[i])
}
alt <- testLinearModel(y, design)
expect_equal(out, alt)
# Handles more complex designs like a champ.
v <- gl(4, 25)
design <- model.matrix(~u + v)
out <- testLinearModel(y, design, contrast=c(0, 1, 0, 0, 0))
for (i in seq_len(10)) {
Y <- y[i,]
fit <- lm(Y ~ u + v)
stats <- summary(fit)$coefficients
expect_equal(stats[2,"Pr(>|t|)"], out$p.value[i])
}
alt <- testLinearModel(y, design, coef=2)
expect_equal(out, alt)
})
test_that("linear model testing works for contrast matrices", {
y <- matrix(rnorm(10000), ncol=100)
A <- gl(2, 50)
u <- runif(100)
design <- model.matrix(~A + u)
out <- testLinearModel(y, design, contrast=cbind(c(0, 1, 0), c(0, 0, 1)))
for (i in seq_len(10)) {
Y <- y[i,]
fit <- lm(Y ~ A + u)
fit0 <- lm(Y ~ 1)
stats <- anova(fit, fit0)
expect_equal(stats[2,"Pr(>F)"], out$p.value[i])
}
alt <- testLinearModel(y, design, coef=2:3)
expect_equal(out[,1], alt[,1])
expect_equal(out[,2], alt[,2])
expect_equal(out$PValue, alt$PValue)
# Another design:
B <- gl(4, 25)
design <- model.matrix(~B)
out <- testLinearModel(y, design, contrast=cbind(c(0,1,0,0), c(0,0,1,-1)))
for (i in seq_len(10)) {
Y <- y[i,]
fit <- lm(Y ~ B)
fit0 <- lm(Y ~ A)
stats <- anova(fit, fit0)
expect_equal(stats[2,"Pr(>F)"], out$p.value[i])
}
})
test_that("linear model testing works with miscellaneous options", {
y <- matrix(rnorm(10000), ncol=100)
A <- gl(2, 50)
u <- runif(100)
design <- model.matrix(~A + u)
ref <- testLinearModel(y, design, contrast=cbind(c(0, 1, 0), c(0, 0, 1)))
out <- testLinearModel(y, design, contrast=cbind(c(0, 1, 0), c(0, 0, 1)), subset.row=1:10)
ref$FDR <- out$FDR <- NULL
expect_identical(ref[1:10,], out)
# Respects row names.
rownames(y) <- paste0("GENE_", seq_len(nrow(y)))
out <- testLinearModel(y, design, contrast=cbind(c(0, 1, 0), c(0, 0, 1)))
expect_identical(rownames(out), rownames(y))
out <- testLinearModel(y, design, contrast=cbind(c(0, 1, 0), c(0, 0, 1)), subset.row=1:10)
expect_identical(rownames(out), rownames(y)[1:10])
})
test_that("linear model testing works with blocking", {
y <- matrix(rnorm(10000), ncol=100)
A <- gl(2, 50)
design <- model.matrix(~A)
b <- rep(1:2, 50)
out <- testLinearModel(y, design, contrast=c(0, 1), block=b)
alt1 <- testLinearModel(y[,b==1], design[b==1,], contrast=c(0, 1))
alt2 <- testLinearModel(y[,b==2], design[b==2,], contrast=c(0, 1))
expect_equivalent(alt1, out$per.block[,1])
expect_equivalent(alt2, out$per.block[,2])
expect_equal(out$p.value, metapod::parallelStouffer(list(alt1$p.value, alt2$p.value))$p.value)
# Robust to parallelization.
parl <- testLinearModel(y, design, contrast=c(0, 1), block=b, BPPARAM=safeBPParam(2))
expect_identical(out, parl)
# Responds to weighting.
b <- rep(1:4, 25)==1
out <- testLinearModel(y, design, contrast=c(0, 1), block=b)
alt1 <- testLinearModel(y[,b], design[b,], contrast=c(0, 1))
alt2 <- testLinearModel(y[,!b], design[!b,], contrast=c(0, 1))
expect_equal(out$p.value, metapod::parallelStouffer(list(alt1$p.value, alt2$p.value), weights=c(1,3))$p.value)
# Fails gracefully without rank.
b <- rep(c(1,2), c(75, 25))
out <- testLinearModel(y, design, contrast=c(0, 1), block=b)
alt1 <- testLinearModel(y[,b==1], design[b==1,], contrast=c(0, 1))
expect_identical(sum(is.na(out$per.block[,2]$p.value)), nrow(out))
out$per.block <- NULL
metadata(alt1) <- list()
expect_equal(out, alt1)
expect_error(testLinearModel(y, design, contrast=c(0, 1), block=A), "no level")
# Respects row names.
rownames(y) <- paste0("GENE_", seq_len(nrow(y)))
out <- testLinearModel(y, design, contrast=c(0, 1), block=b)
expect_identical(rownames(y), rownames(out))
})
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