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# This tests out the scaledColRanks function.
# require(scran); require(testthat); source("setup.R"); source("test-colranks.R")
ncells <- 100
ngenes <- 200
set.seed(430000)
test_that("scaledColRanks correctly computes the ranks", {
dummy <- matrix(rnbinom(ncells*ngenes, mu=10, size=20), ncol=ncells, nrow=ngenes)
emp.ranks <- scaledColRanks(dummy)
ref <- apply(dummy, 2, FUN=function(y) {
r <- rank(y)
r <- r - mean(r)
r/sqrt(sum(r^2))/2
})
expect_equal(emp.ranks, ref)
# Behaves with many ties.
dummy <- matrix(sample(50, ncells*ngenes, replace=TRUE), ncol=ncells, nrow=ngenes)
emp.ranks <- scaledColRanks(dummy)
ref <- apply(dummy, 2, FUN=function(y) {
r <- rank(y)
r <- r - mean(r)
r/sqrt(sum(r^2))/2
})
expect_equal(emp.ranks, ref)
# Behaves with no ties.
dummy <- matrix(rnorm(ncells*ngenes), ncol=ncells, nrow=ngenes)
emp.ranks <- scaledColRanks(dummy)
ref <- apply(dummy, 2, FUN=function(y) {
r <- rank(y)
r <- r - mean(r)
r/sqrt(sum(r^2))/2
})
expect_equal(emp.ranks, ref)
# Works correctly with shuffling.
shuffled <- sample(ncells)
emp.ranks <- scaledColRanks(dummy, subset.row=shuffled)
ref <- apply(dummy, 2, FUN=function(y) {
r <- rank(y[shuffled])
r <- r - mean(r)
r/sqrt(sum(r^2))/2
})
expect_equal(emp.ranks, ref)
# Works correctly on sparse matrices.
sparse <- abs(Matrix::rsparsematrix(ngenes, ncells, density=0.1))
out <- scaledColRanks(sparse, min.mean=0)
ref <- scaledColRanks(as.matrix(sparse), min.mean=0)
expect_identical(unname(out), unname(ref))
})
set.seed(430001)
test_that("scaledColRanks responds to other options", {
mat <- matrix(rnbinom(ncells*ngenes, mu=10, size=20), ncol=ncells, nrow=ngenes)
# Subsetting.
keep <- sample(ngenes, ngenes/2)
rnks <- scaledColRanks(mat, subset.row=keep)
expect_identical(rnks, scaledColRanks(mat[keep,]))
# Minimum mean.
rnks <- scaledColRanks(mat, min.mean=10)
expect_identical(rnks, scaledColRanks(mat, subset.row=scuttle::calculateAverage(mat) >= 10))
# Transposition.
rnks <- scaledColRanks(mat, transposed=TRUE)
expect_identical(rnks, t(scaledColRanks(mat)))
})
set.seed(430002)
test_that("scaledColRanks naming is handled correctly", {
mat <- matrix(rnbinom(ncells*ngenes, mu=10, size=20), ncol=ncells, nrow=ngenes)
rownames(mat) <- seq_len(nrow(mat))
colnames(mat) <- seq_len(ncol(mat))
rnks <- scaledColRanks(mat)
expect_identical(dimnames(rnks), dimnames(mat))
rnks <- scaledColRanks(mat, transposed=TRUE)
expect_identical(dimnames(rnks), rev(dimnames(mat)))
rnks <- scaledColRanks(mat, subset.row=1:10)
expect_identical(rownames(rnks), rownames(mat)[1:10])
rnks <- scaledColRanks(mat, withDimnames=FALSE)
expect_identical(dimnames(rnks), NULL)
})
set.seed(430003)
test_that("scaledColRanks handles sparsity requests", {
mat <- matrix(rnbinom(ncells*ngenes, mu=1, size=20), ncol=ncells, nrow=ngenes)
ref <- scaledColRanks(mat)
library(Matrix)
rnks <- scaledColRanks(mat, as.sparse=TRUE)
expect_s4_class(rnks, "dgCMatrix")
expect_identical(rnks!=0, as(mat, "dgCMatrix")!=0)
centred <- sweep(rnks, 2, Matrix::colMeans(rnks), "-")
centred <- as.matrix(centred)
dimnames(centred) <- NULL
expect_equal(centred, ref)
# With transposition.
rnks <- scaledColRanks(mat, as.sparse=TRUE, transposed=TRUE)
expect_identical(rnks!=0, as(t(mat), "dgCMatrix")!=0)
centred <- rnks - Matrix::rowMeans(rnks)
centred <- as.matrix(centred)
dimnames(centred) <- NULL
expect_equal(centred, t(ref))
})
set.seed(430003)
test_that("scaledColRanks handles DA inputs", {
dummy <- matrix(rnbinom(ncells*ngenes, mu=10, size=20), ncol=ncells, nrow=ngenes)
expect_identical(scaledColRanks(dummy), scaledColRanks(DelayedArray(dummy)))
expect_identical(scaledColRanks(dummy, transposed=TRUE), scaledColRanks(DelayedArray(dummy), transposed=TRUE))
expect_identical(scaledColRanks(dummy, as.sparse=TRUE), scaledColRanks(DelayedArray(dummy), as.sparse=TRUE))
})
set.seed(430004)
test_that("scaledColRanks handles silly inputs", {
mat <- matrix(rnbinom(ncells*ngenes, mu=10, size=20), ncol=ncells, nrow=ngenes)
expect_error(scaledColRanks(mat[0,,drop=FALSE]), "rank variances of zero detected for a cell")
out <- scaledColRanks(mat[,0,drop=FALSE])
expect_identical(dim(out), c(as.integer(ngenes), 0L))
})
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