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# tests for feature pre-processing functions.
# library(scuttle); library(testthat); source("setup.R"); source("test-sum-across-feat.R")
library(Matrix)
library(DelayedArray)
##########################################################
set.seed(10001)
test_that("we can summarise counts at feature set level", {
ids <- sample(nrow(sce)/2, nrow(sce), replace=TRUE)
out <- sumCountsAcrossFeatures(sce, ids)
expect_identical(out, rowsum(counts(sce), ids))
expect_identical(rownames(out), as.character(sort(unique(ids))))
out2 <- sumCountsAcrossFeatures(counts(sce), ids)
expect_identical(out, out2)
# Handles averaging correctly.
out2 <- sumCountsAcrossFeatures(sce, ids, average=TRUE)
expect_identical(out2, rowsum(counts(sce), ids)/as.integer(table(ids)))
# assay.type= works correctly.
alt <- sce
assayNames(alt) <- "whee"
out2 <- sumCountsAcrossFeatures(alt, ids, assay.type="whee")
expect_identical(out, out2)
# Respects levels properly.
fids <- factor(ids, levels=rev(sort(unique(ids))))
fout <- sumCountsAcrossFeatures(sce, fids)
expect_identical(out, fout[nrow(fout):1,])
})
set.seed(10001)
test_that("count summarization at feature set level respects NAs", {
ids2 <- sample(LETTERS, nrow(sce), replace=TRUE)
out2 <- sumCountsAcrossFeatures(sce, ids2)
ids3 <- ids2
ids3[ids3=="A"] <- NA
out3 <- sumCountsAcrossFeatures(sce, ids3)
expect_identical(out2[setdiff(rownames(out2), "A"),], out3)
ids4 <- ids2
ids4[1:10] <- NA
out4a <- sumCountsAcrossFeatures(sce, ids4)
out4b <- sumCountsAcrossFeatures(sce[-(1:10),], ids4[-(1:10)])
expect_identical(out4a, out4b)
})
set.seed(10002)
test_that("by-feature count summarization behaves with lists", {
idl <- list(10:1, sample(nrow(sce), 100), nrow(sce) - 1:10)
outl <- sumCountsAcrossFeatures(sce, idl)
manual <- list()
for (i in seq_along(idl)) {
manual[[i]] <- colSums(counts(sce)[idl[[i]],])
}
expect_identical(outl, do.call(rbind, manual))
expect_identical(outl, sumCountsAcrossFeatures(sce, lapply(idl, function(i) rownames(sce)[i])))
expect_identical(outl, sumCountsAcrossFeatures(sce, lapply(idl, function(i) seq_len(nrow(sce)) %in% i)))
expect_identical(outl/lengths(idl), sumCountsAcrossFeatures(sce, idl, average=TRUE))
})
set.seed(100021)
test_that("by-feature count summarization responds to subsetting", {
ids <- sample(LETTERS, nrow(sce), replace=TRUE)
keep <- rbinom(nrow(sce), 1, 0.5)>0
ref <- sumCountsAcrossFeatures(sce[keep,], ids[keep])
out <- sumCountsAcrossFeatures(sce, ids, subset.row=keep)
expect_identical(out, ref)
keep2 <- rbinom(ncol(sce), 1, 0.5)>0
ref <- sumCountsAcrossFeatures(sce[,keep2], ids)
out <- sumCountsAcrossFeatures(sce, ids, subset.col=keep2)
expect_identical(out, ref)
})
##########################################################
set.seed(10003)
test_that("by-feature count summarization behaves with different classes", {
ids <- sample(nrow(sce)/2, nrow(sce), replace=TRUE)
ref <- sumCountsAcrossFeatures(sce, ids)
# Handles sparse matrices properly.
library(Matrix)
sparsified <- sce
counts(sparsified) <- as(counts(sparsified), "dgCMatrix")
spack <- sumCountsAcrossFeatures(sparsified, ids)
expect_equal(ref, as.matrix(spack))
unknown <- sce
counts(unknown) <- as(counts(unknown), "dgTMatrix")
spack <- sumCountsAcrossFeatures(unknown, ids)
expect_equivalent(ref, as.matrix(spack))
# Handles DelayedArrays properly.
delayed <- sce
counts(delayed) <- DelayedArray(counts(delayed))
dack <- sumCountsAcrossFeatures(delayed, ids)
expect_equivalent(ref, as.matrix(dack))
})
set.seed(100031)
test_that("by-feature count summarization parallelizes properly", {
ids <- sample(nrow(sce)/2, nrow(sce), replace=TRUE)
ref <- sumCountsAcrossFeatures(sce, ids)
# Handles parallelization properly.
alt <- sumCountsAcrossFeatures(sce, ids, BPPARAM=safeBPParam(2))
expect_identical(alt, ref)
alt <- sumCountsAcrossFeatures(sce, ids, BPPARAM=safeBPParam(3))
expect_identical(alt, ref)
})
##########################################################
set.seed(10004)
test_that("Aggregation across features works correctly", {
ids <- paste0("GENE_", sample(nrow(sce)/2, nrow(sce), replace=TRUE))
alt <- aggregateAcrossFeatures(sce, ids)
expect_identical(rownames(alt), sort(unique(ids)))
expect_identical(counts(alt), sumCountsAcrossFeatures(counts(sce), ids))
# Behaves in the presence of multiple assays.
normcounts(sce) <- normalizeCounts(sce, log=FALSE)
alt2 <- aggregateAcrossFeatures(sce, ids)
expect_identical(alt, alt2)
alt3 <- aggregateAcrossFeatures(sce, ids, use.assay.type=c("counts", "normcounts"))
expect_identical(counts(alt), counts(alt3))
expect_identical(normcounts(alt3), sumCountsAcrossFeatures(sce, ids, assay.type="normcounts"))
})
set.seed(10004)
test_that("Aggregation across features works correctly with lists", {
idl <- list(10:1, sample(nrow(sce), 100), nrow(sce) - 1:10)
alt <- aggregateAcrossFeatures(sce, idl)
outl <- sumCountsAcrossFeatures(sce, idl)
expect_identical(counts(alt), outl)
# Handles row names.
idl <- list(X=10:1, Y=sample(nrow(sce), 100), Z=nrow(sce) - 1:10)
alt <- aggregateAcrossFeatures(sce, idl)
expect_identical(rownames(alt), names(idl))
# Strips metadata.
rowRanges(sce) <- GRanges("chrA", IRanges(seq_len(nrow(sce)), width=1))
alt <- aggregateAcrossFeatures(sce, idl)
expect_identical(ncol(rowData(alt)), 0L)
expect_true(all(lengths(rowRanges(alt))==0))
# Stripping works for SE's.
se <- as(sce, "SummarizedExperiment")
rowData(se)$stuff <- 1L
alt <- aggregateAcrossFeatures(se, idl)
expect_identical(ncol(rowData(alt)), 0L)
expect_identical(rownames(alt), names(idl))
})
############################################
test_that("numDetectedAcrossFeatures works as expected", {
ids <- sample(LETTERS[1:5], nrow(sce), replace=TRUE)
expect_equal(numDetectedAcrossFeatures(counts(sce), ids),
rowsum((counts(sce) > 0)+0, ids))
expect_identical(numDetectedAcrossFeatures(counts(sce), ids, average=TRUE),
rowsum((counts(sce) > 0)+0, ids)/as.integer(table(ids)))
# Checking that it works direclty with SCEs.
expect_equal(numDetectedAcrossFeatures(counts(sce), ids),
numDetectedAcrossFeatures(sce, ids))
expect_equal(numDetectedAcrossFeatures(counts(sce), ids, average=TRUE),
numDetectedAcrossFeatures(sce, ids, average=TRUE))
# Checking that subsetting works.
expect_identical(numDetectedAcrossFeatures(counts(sce), ids, subset.col=10:1),
numDetectedAcrossFeatures(counts(sce), ids)[,10:1])
expect_identical(numDetectedAcrossFeatures(counts(sce), ids, subset.row=2:15),
numDetectedAcrossFeatures(counts(sce)[2:15,], ids[2:15]))
ids[c(1,3,5,6)] <- NA
expect_identical(numDetectedAcrossFeatures(counts(sce), ids),
numDetectedAcrossFeatures(counts(sce)[!is.na(ids),], ids[!is.na(ids)]))
# Comparing to sumCountsAcrossFeatures.
expect_equal(numDetectedAcrossFeatures(counts(sce), ids),
sumCountsAcrossFeatures((counts(sce) > 0)+0, ids))
expect_equal(numDetectedAcrossFeatures(counts(sce), ids, average=TRUE),
sumCountsAcrossFeatures((counts(sce) > 0)+0, ids, average=TRUE))
})
test_that("numDetectedAcrossFeatures handles other matrix classes", {
thing <- matrix(rpois(2000, lambda=0.5), ncol=100, nrow=20)
ids <- sample(LETTERS[1:6], nrow(thing), replace=TRUE)
ref <- numDetectedAcrossFeatures(thing, ids)
expect_equal(colSums(ref), colSums(thing > 0))
sparse <- as(thing, 'dgCMatrix')
expect_equal(numDetectedAcrossFeatures(sparse, ids), ref)
delayed <- DelayedArray(thing)
expect_equal(numDetectedAcrossFeatures(delayed, ids), ref)
})
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