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# This tests the functionality of modelGeneCV2.
# library(testthat); library(scran); source("test-model-cv2.R")
set.seed(20001)
ncells <- 200
ngenes <- 1000
means <- 2^runif(ngenes, -1, 5)
dummy <- matrix(rnbinom(ngenes*ncells, mu=means, size=5), ncol=ncells, nrow=ngenes)
rownames(dummy) <- paste0("X", seq_len(ngenes))
library(scuttle)
dummy2 <- normalizeCounts(dummy, log=FALSE)
test_that("modelGeneCV2 works correctly without blocking", {
out <- modelGeneCV2(dummy)
expect_equal(out$mean, unname(rowMeans(dummy2)))
expect_equal(out$total, DelayedMatrixStats::rowVars(dummy2)/out$mean^2)
expect_equal(out$trend, metadata(out)$trend(out$mean))
expect_equal(out$ratio, out$total/out$trend)
expect_equal(order(out$p.value), order(out$ratio, decreasing=TRUE))
})
test_that("modelGeneCV2 responds to size factors", {
sf <- runif(ncells)
out <- modelGeneCV2(dummy, size.factors=sf)
ref <- modelGeneCV2(t(t(dummy)/sf*mean(sf)), size.factors=rep(1, ncells))
expect_equal(out, ref)
})
test_that("modelGeneCV2 works correctly with blocking, no weighting", {
block <- sample(LETTERS[1:5], ncells, replace=TRUE)
out <- modelGeneCV2(dummy, block=block)
accumulated.mean <- accumulated.total <- accumulated.trend <- 0
for (i in unique(block)) {
current <- i==block
ref <- modelGeneCV2(dummy2[,current], size.factors=rep(1, sum(current)))
subout <- out$per.block[[i]]
expect_identical(ref$mean, subout$mean)
expect_identical(ref$total, subout$total)
expect_identical(ref$trend, subout$trend)
expect_identical(ref$ratio, subout$ratio)
expect_identical(ref$p.value, subout$p.value)
accumulated.mean <- accumulated.mean + log(ref$mean)
accumulated.total <- accumulated.total + log(ref$total)
accumulated.trend <- accumulated.trend + log(ref$trend)
}
# Check combining statistics works correctly.
n <- length(unique(block))
expect_equal(out$mean, exp(accumulated.mean/n))
expect_equal(out$total, exp(accumulated.total/n))
expect_equal(out$trend, exp(accumulated.trend/n))
expect_equal(out$ratio, out$total/out$trend)
all.p <- lapply(out$per.block, "[[", i="p.value")
expect_equal(out$p.value, metapod::parallelFisher(all.p)$p.value)
# Responds to choice of method.
out2 <- modelGeneCV2(dummy, block=block, method="stouffer")
all.p <- lapply(out2$per.block, "[[", i="p.value")
expect_equal(out2$p.value, metapod::parallelStouffer(all.p)$p.value)
})
test_that("modelGeneCV2 works correctly with blocking and weighting", {
block <- sample(LETTERS[1:5], ncells, replace=TRUE)
out <- modelGeneCV2(dummy, block=block, equiweight=FALSE)
accumulated.mean <- accumulated.total <- accumulated.trend <- 0
for (i in unique(block)) {
current <- i==block
ref <- modelGeneCV2(dummy2[,current], size.factors=rep(1, sum(current)))
subout <- out$per.block[[i]]
expect_identical(ref$mean, subout$mean)
expect_identical(ref$total, subout$total)
expect_identical(ref$trend, subout$trend)
expect_identical(ref$ratio, subout$ratio)
expect_identical(ref$p.value, subout$p.value)
n <- sum(i==block)
accumulated.mean <- accumulated.mean + log(ref$mean) * n
accumulated.total <- accumulated.total + log(ref$total) * n
accumulated.trend <- accumulated.trend + log(ref$trend) * n
}
# Check combining statistics works correctly.
n <- length(block)
expect_equal(out$mean, exp(accumulated.mean/n))
expect_equal(out$total, exp(accumulated.total/n))
expect_equal(out$trend, exp(accumulated.trend/n))
expect_equal(out$ratio, out$total/out$trend)
all.p <- lapply(out$per.block, "[[", i="p.value")
expect_equal(out$p.value, metapod::parallelFisher(all.p)$p.value)
# Responds to choice of method with weighting.
out2 <- modelGeneCV2(dummy, block=block, method="stouffer", equiweight=FALSE)
all.p <- lapply(out2$per.block, "[[", i="p.value")
w <- countMatches(names(all.p), block)
expect_equal(out2$p.value, metapod::parallelStouffer(all.p, weights=w)$p.value)
})
test_that("modelGeneCV2 handles blocks with no residual d.f.", {
out <- modelGeneCV2(dummy2, size.factors=rep(1, ncells), block=rep(1:2, c(1, ncells-1)))
ref <- modelGeneCV2(dummy2[,-1], size.factors=rep(1, ncells))
expect_equal(out$mean, ref$mean)
expect_equal(out$total, ref$total)
out <- modelGeneCV2(dummy, size.factors=rep(1, ncells), block=rep(1:3, c(1, 1, ncells-2)))
ref <- modelGeneCV2(dummy[,-c(1,2)], size.factors=rep(1, ncells))
expect_equal(out$mean, ref$mean)
expect_equal(out$total, ref$total)
expect_error(modelGeneCV2(dummy[,1,drop=FALSE]), "no residual d.f. in any level")
})
test_that("modelGeneCV2 works with subsetting options", {
chosen <- sample(ngenes, ngenes/2)
out <- modelGeneCV2(dummy, subset.row=chosen)
ref <- modelGeneCV2(dummy[chosen,])
expect_equal(out, ref)
# Subsetting by fit works.
out2 <- modelGeneCV2(dummy, subset.fit=chosen, size.factors=librarySizeFactors(dummy, chosen))
expect_identical(rownames(out2), rownames(dummy))
expect_equal(out2[chosen,1:5], ref[,1:5])
# Zero-length subsetting.
empty <- modelGeneCV2(dummy, subset.row=integer(0), subset.fit=chosen, size.factors=librarySizeFactors(dummy))
expect_identical(nrow(empty), 0L)
expect_error(modelGeneCV2(dummy, subset.fit=integer(0)), "need at least 2 points")
})
test_that("modelGeneCV2 works with SingleCellExperiment objects", {
X <- SingleCellExperiment(list(counts=dummy))
expect_equal(modelGeneCV2(X), modelGeneCV2(dummy))
sizeFactors(X) <- runif(ncol(X))
expect_equal(modelGeneCV2(X), modelGeneCV2(dummy, sizeFactors(X)))
X <- SingleCellExperiment(list(whee=dummy))
expect_equal(modelGeneCV2(X, assay.type="whee"), modelGeneCV2(dummy))
})
test_that("modelGeneCV2 works with sparse inputs", {
X <- dummy2
sf <- librarySizeFactors(X) # need to specify this for testing, as minor differences in colSums propagate.
X_ <- as(X, "dgCMatrix")
expect_equal(modelGeneCV2(X, size.factors=sf), modelGeneCV2(X_, size.factors=sf))
# Safe with ultra-sparse rows.
X[1:20,] <- 0
X[1,2] <- 1
X[2,c(10, 20, 30)] <- 1
X[3,c(10, 20, 30)] <- 3:1
X_ <- as(X, "dgCMatrix")
expect_equal(modelGeneCV2(X, size.factors=sf), modelGeneCV2(X_, size.factors=sf))
# Works with ultra dense rows.
X <- dummy2 + 1
X_ <- as(X, "dgCMatrix")
expect_equal(modelGeneCV2(X, size.factors=sf), modelGeneCV2(X_, size.factors=sf))
})
#######################################
#######################################
#######################################
set.seed(201001)
ncells <- 200
ngenes <- 1000
means <- 2^runif(ngenes, -1, 5)
dummy <- matrix(rnbinom(ngenes*ncells, mu=means, size=5), ncol=ncells, nrow=ngenes)
rownames(dummy) <- paste0("X", seq_len(ngenes))
nspikes <- 100
smeans <- 2^runif(nspikes, -1, 5)
spikes <- matrix(rnbinom(nspikes*ncells, mu=smeans, size=5), ncol=ncells, nrow=nspikes)
rownames(spikes) <- paste0("X", seq_len(nspikes))
normdummy <- scuttle::normalizeCounts(dummy, log=FALSE)
normspikes <- scuttle::normalizeCounts(spikes, log=FALSE)
test_that("modelGeneCV2WithSpikes works correctly in the basic case", {
out <- modelGeneCV2WithSpikes(dummy, spikes)
ref <- modelGeneCV2(normdummy)
expect_equal(out$mean, ref$mean)
expect_equal(out$total, ref$total)
expect_equal(metadata(out)$mean, rowMeans(normspikes))
expect_equal(metadata(out)$cv2, DelayedMatrixStats::rowVars(normspikes)/metadata(out)$mean^2)
fit <- fitTrendCV2(metadata(out)$mean, metadata(out)$cv2, ncells)
expect_identical(fit$std.dev, metadata(out)$std.dev)
expect_equal(out$trend, fit$trend(ref$mean))
expect_equal(out$ratio, out$total/out$trend)
})
test_that("modelGeneCV2WithSpikes works correctly with blocking", {
block <- sample(LETTERS[1:5], ncells, replace=TRUE)
out <- modelGeneCV2WithSpikes(dummy, spikes, block=block)
ref <- modelGeneCV2(normdummy, block=block)
expect_equal(out$mean, ref$mean)
expect_equal(out$total, ref$total)
accumulated.mean <- accumulated.total <- accumulated.trend <- 0
sf1 <- scuttle::librarySizeFactors(dummy)
sf2 <- scuttle::librarySizeFactors(spikes)
for (i in unique(block)) {
current <- i==block
# Forcibly avoid auto-centering of size.factors, to use as a reference here.
ssf1 <- sf1[current]
ssf2 <- sf2[current]
ssf2 <- ssf2/mean(ssf2) * mean(ssf1)
ref <- modelGeneCV2WithSpikes(t(t(dummy[,current])/ssf1),
size.factors=rep(1, sum(current)),
spikes=t(t(spikes[,current])/ssf2),
spike.size.factors=rep(1, sum(current)))
subout <- out$per.block[[i]]
expect_equal(ref$mean, subout$mean)
expect_equal(ref$total, subout$total)
expect_equal(ref$trend, subout$trend)
expect_equal(ref$ratio, subout$ratio)
expect_equal(ref$p.value, subout$p.value)
accumulated.mean <- accumulated.mean + log(ref$mean)
accumulated.total <- accumulated.total + log(ref$total)
accumulated.trend <- accumulated.trend + log(ref$trend)
}
# Check combining statistics works correctly.
n <- length(unique(block))
expect_equal(out$mean, exp(accumulated.mean/n))
expect_equal(out$total, exp(accumulated.total/n))
expect_equal(out$trend, exp(accumulated.trend/n))
expect_equal(out$ratio, out$total/out$trend)
all.p <- lapply(out$per.block, "[[", i="p.value")
expect_equal(out$p.value, metapod::parallelFisher(all.p)$p.value)
})
test_that("modelGeneCV2WithSpikes centers size factors correctly", {
# Without blocking.
sf1 <- 2^rnorm(ncells, 0.05)
sf2 <- 2^rnorm(ncells, 0.05)
out <- modelGeneCV2WithSpikes(dummy, size.factors=sf1, spikes=spikes, spike.size.factors=sf2)
msf1 <- sf1/mean(sf1)
msf2 <- sf2/mean(sf2)
ref <- modelGeneCV2WithSpikes(t(t(dummy)/msf1), size.factors=rep(1, ncells),
spikes=t(t(spikes)/msf2), spike.size.factors=rep(1, ncells))
expect_equal(ref$mean, out$mean)
expect_equal(ref$total, out$total)
expect_equal(ref$trend, out$trend)
expect_equal(ref$ratio, out$ratio)
expect_equal(ref$p.value, out$p.value)
# With blocking.
block <- sample(LETTERS[1:5], ncells, replace=TRUE)
out <- modelGeneCV2WithSpikes(dummy, size.factors=sf1, spikes=spikes, spike.size.factors=sf2, block=block)
for (i in unique(block)) {
current <- i==block
ssf1 <- msf1[current]
ssf2 <- msf2[current]
ssf2 <- ssf2/mean(ssf2) * mean(ssf1)
ref <- modelGeneCV2WithSpikes(t(t(dummy[,current])/ssf1),
size.factors=rep(1, sum(current)),
spikes=t(t(spikes[,current])/ssf2),
spike.size.factors=rep(1, sum(current)))
subout <- out$per.block[[i]]
expect_equal(ref$mean, subout$mean)
expect_equal(ref$total, subout$total)
expect_equal(ref$trend, subout$trend)
expect_equal(ref$ratio, subout$ratio)
expect_equal(ref$p.value, subout$p.value)
}
})
test_that("modelGeneCV2 works with SingleCellExperiment objects", {
X <- SingleCellExperiment(list(counts=dummy))
altExp(X, "spikes") <- SingleCellExperiment(list(counts=spikes))
expect_equal(modelGeneCV2WithSpikes(X, spikes="spikes"), modelGeneCV2WithSpikes(dummy, spikes))
X <- SingleCellExperiment(list(whee=dummy))
altExp(X, "spikes") <- SingleCellExperiment(list(whee=spikes))
expect_equal(modelGeneCV2WithSpikes(X, "spikes", assay.type="whee"), modelGeneCV2WithSpikes(dummy, spikes))
X <- SingleCellExperiment(list(whee=dummy))
sizeFactors(X) <- sf1 <- 2^rnorm(ncells, 0.1)
altExp(X, "spikes") <- SingleCellExperiment(list(whee=spikes))
sizeFactors(altExp(X)) <- sf2 <- 2^rnorm(ncells, 0.1)
expect_equal(modelGeneCV2WithSpikes(X, "spikes", assay.type="whee"),
modelGeneCV2WithSpikes(dummy, size.factors=sf1, spikes, spike.size.factors=sf2))
})
test_that("modelGeneCV2WithSpikes works with sparse inputs", {
# Need to specify the size factors to avoid propagating small errors due to colSums differences.
sf1 <- librarySizeFactors(normdummy)
sf2 <- librarySizeFactors(normspikes)
ref <- modelGeneVarWithSpikes(normdummy, normspikes, size.factors=sf1, spike.size.factors=sf2)
d2 <- as(normdummy, "dgCMatrix")
s2 <- as(normspikes, "dgCMatrix")
expect_equal(ref, modelGeneVarWithSpikes(normdummy, s2, size.factors=sf1, spike.size.factors=sf2))
expect_equal(ref, modelGeneVarWithSpikes(d2, normspikes, size.factors=sf1, spike.size.factors=sf2))
expect_equal(ref, modelGeneVarWithSpikes(d2, s2, size.factors=sf1, spike.size.factors=sf2))
})
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