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# Test functions for QC calculation.
# library(scuttle); library(testthat); source("setup.R"); source("test-qc-calc.R")
original <- sce
test_that("we can compute standard per-cell QC metrics", {
df <- perCellQCMetrics(original, flatten=FALSE)
expect_identical(rownames(df), colnames(original))
# Testing total metrics for cells.
expect_equal(df$sum, unname(colSums(counts(original))))
expect_equal(df$detected, unname(colSums(counts(original) > 0)))
# Testing percentage metrics for cells.
N <- c(50, 100, 200, 500)
df <- perCellQCMetrics(original, percent.top=N, flatten=FALSE)
for (i in seq_len(ncol(original))) {
cur_counts <- counts(original)[,i]
o <- order(cur_counts, decreasing=TRUE)
lib_size <- sum(cur_counts)
for (x in N) {
chosen <- o[seq_len(x)]
expect_equivalent(df$percent.top[i,as.character(x)], sum(cur_counts[chosen])/lib_size * 100)
}
}
# Flattening works as expected.
flat <- perCellQCMetrics(original, percent.top=N)
for (x in N) {
expect_identical(flat[,paste0("percent.top_", x)], df$percent.top[,as.character(x)])
}
expect_identical(rownames(flat), colnames(original))
})
test_that("we can compute standard QC metrics with subsets", {
ref <- perCellQCMetrics(original, flatten=FALSE)
df <- perCellQCMetrics(original, subsets = list(set1 = 1:20), flatten=FALSE)
expect_identical(df[,1:3], ref[,1:3])
expect_equivalent(df$subsets$set1$sum, colSums(counts(original[1:20,])))
expect_equivalent(df$subsets$set1$detected, colSums(counts(original[1:20,])> 0))
expect_equivalent(df$subsets$set1$percent, df$subsets$set1$sum/df$sum * 100)
# Testing behaviour with multiple feature controls.
multi_controls <- list(controls1 = 1:20, controls2 = rownames(original)[500:1000])
df2 <- perCellQCMetrics(original, subsets = multi_controls, flatten=FALSE)
expect_equivalent(df$subsets$set1, df2$subsets$controls1)
expect_equivalent(df2$subsets$controls2$sum, colSums(counts(original[500:1000,])))
expect_equivalent(df2$subsets$controls2$detected, colSums(counts(original[500:1000,])> 0))
expect_equivalent(df2$subsets$controls2$percent, df2$subsets$controls2$sum/df2$sum * 100)
# Flattening works as expected.
flat <- perCellQCMetrics(original, subsets = list(set1=1:20))
expect_identical(flat$subsets_set1_sum, df$subsets$set1$sum)
})
test_that("perCellQCMetrics works with alternative experiments", {
sce <- original
altExp(sce, "alpha") <- original[1:10,]
altExp(sce, "bravo") <- original[10:20,]
ref <- perCellQCMetrics(original, flatten=FALSE)
df <- perCellQCMetrics(sce, flatten=FALSE)
expect_identical(df[,1:3], ref[,1:3])
for (x in altExpNames(sce)) {
current <- perCellQCMetrics(altExp(sce, x), flatten=FALSE)
expect_identical(df$altexps[[x]]$sum, current$sum)
expect_identical(df$altexps[[x]]$detected, current$detected)
expect_equal(df$altexps[[x]]$percent, current$sum/df$total*100)
}
expect_identical(df$total, df$sum + df$altexps$alpha$sum + df$altexps$bravo$sum)
# Ignores experiments that don't have the requested assay.
copy <- sce
assayNames(altExp(copy, "alpha")) <- "FOO"
ignored <- perCellQCMetrics(copy, use.altexps=NULL)
expect_true(is.null(ignored$altexps_alpha_sum))
expect_false(is.null(ignored$altexps_bravo_sum))
# Flattening works as expected.
flat <- perCellQCMetrics(sce)
expect_identical(flat$altexps_alpha_sum, df$altexps$alpha$sum)
expect_identical(flat$altexps_bravo_detected, df$altexps$bravo$detected)
flat <- perCellQCMetrics(sce, use.altexps="alpha")
expect_identical(flat$altexps_alpha_sum, df$altexps$alpha$sum)
expect_null(flat$altexps_beta_sum)
})
test_that("perCellQCMetrics handles silly inputs", {
expect_error(perCellQCMetrics(original, subsets = list(1:20), flatten=FALSE), "must be named")
# Doesn't choke with no entries.
thing <- perCellQCMetrics(original[0,], flatten=FALSE)
expect_identical(rownames(thing), colnames(original))
expect_true(all(thing$sum==0L))
thing2 <- perCellQCMetrics(original[,0], flatten=FALSE)
expect_identical(nrow(thing2), 0L)
expect_identical(colnames(thing), colnames(thing2))
# Percentage holds at the limit.
df <- perCellQCMetrics(original[1:10,], percent.top=c(50, 100, 200, 500), flatten=FALSE)
expect_true(all(df$percent.top==100))
df <- perCellQCMetrics(original, percent.top=integer(0), flatten=FALSE)
expect_identical(ncol(df$percent.top), 0L)
# Responds to alternative inputs.
blah <- sce
assayNames(blah) <- "whee"
expect_error(perCellQCMetrics(blah, flatten=FALSE), "counts")
expect_error(perCellQCMetrics(blah, assay.type="whee", flatten=FALSE), NA)
})
#######################################################################
# Works for per-feature metrics.
test_that("perFeatureQCMetrics works correctly", {
out <- perFeatureQCMetrics(original, flatten=FALSE)
expect_identical(rownames(out), rownames(original))
expect_equal(out$mean, unname(rowMeans(counts(original))))
expect_equal(out$detected, unname(rowMeans(counts(original) > 0))*100)
})
test_that("we can compute standard QC metrics with cell controls", {
expect_error(perFeatureQCMetrics(original, subsets = list(1:20), flatten=FALSE), "must be named")
df <- perFeatureQCMetrics(original, subsets = list(set1 = 1:20), flatten=FALSE)
sub_counts <- counts(original)[,1:20]
expect_equal(df$subsets$set1$mean, unname(rowMeans(sub_counts)))
expect_equal(df$subsets$set1$detected, unname(rowMeans(sub_counts > 0) * 100))
expect_equal(df$subsets$set1$ratio, df$subsets$set1$mean/df$mean)
# Testing behaviour with multiple cell controls.
multi_controls <- list(controls1 = 1:5, controls2 = 10:20)
df2 <- perFeatureQCMetrics(original, subsets = multi_controls, flatten=FALSE)
expect_equivalent(df2$subsets$controls2$mean, rowMeans(counts(original[,10:20])))
expect_equivalent(df2$subsets$controls2$detected, rowMeans(counts(original[,10:20])> 0)*100)
expect_equivalent(df2$subsets$controls2$ratio, df2$subsets$controls2$mean/df2$mean)
# Flattening works as expected.
flat <- perFeatureQCMetrics(original, subsets = list(set1 = 1:20))
expect_identical(rownames(flat), rownames(original))
expect_identical(flat$subsets_set1_mean, df$subsets$set1$mean)
expect_identical(flat$subsets_set1_ratio, df$subsets$set1$ratio)
})
test_that("perFeatureQCmetrics handles silly inputs", {
expect_error(perFeatureQCMetrics(original, subsets = list(1:20), flatten=FALSE), "must be named")
# Doesn't choke with no entries.
thing <- perFeatureQCMetrics(original[,0], flatten=FALSE)
expect_identical(rownames(thing), rownames(original))
expect_true(all(thing$sum==0L))
thing2 <- perFeatureQCMetrics(original[0,], flatten=FALSE)
expect_identical(nrow(thing2), 0L)
expect_identical(colnames(thing), colnames(thing2))
# Responds to alternative inputs.
blah <- sce
assayNames(blah) <- "whee"
expect_error(perFeatureQCMetrics(blah, flatten=FALSE), "counts")
expect_error(perFeatureQCMetrics(blah, assay.type="whee", flatten=FALSE), NA)
})
#######################################################################
# Responds to special settings:
test_that("we can compute standard QC metrics on sparse counts matrix", {
alt <- original
library(Matrix)
counts(alt) <- as(counts(alt), "dgCMatrix")
expect_equal(perCellQCMetrics(alt, flatten=FALSE), perCellQCMetrics(original, flatten=FALSE))
expect_equal(perFeatureQCMetrics(alt, flatten=FALSE), perFeatureQCMetrics(original, flatten=FALSE))
expect_equal(perCellQCMetrics(alt, subset=list(set=1:10), flatten=FALSE),
perCellQCMetrics(original, subset=list(set=1:10), flatten=FALSE))
expect_equal(perFeatureQCMetrics(alt, subset=list(set=1:10), flatten=FALSE),
perFeatureQCMetrics(original, subset=list(set=1:10), flatten=FALSE))
})
test_that("we can compute standard QC metrics across multiple cores", {
expect_equal(perCellQCMetrics(original, flatten=FALSE),
perCellQCMetrics(original, BPPARAM=safeBPParam(3), flatten=FALSE))
expect_equal(perFeatureQCMetrics(original, flatten=FALSE),
perFeatureQCMetrics(original, BPPARAM=safeBPParam(3), flatten=FALSE))
expect_equal(perCellQCMetrics(original, subset=list(set=1:10), flatten=FALSE),
perCellQCMetrics(original, subset=list(set=1:10), BPPARAM=safeBPParam(3), flatten=FALSE))
expect_equal(perFeatureQCMetrics(original, subset=list(set=1:10), flatten=FALSE),
perFeatureQCMetrics(original, subset=list(set=1:10), BPPARAM=safeBPParam(3), flatten=FALSE))
})
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