context("plotting")
# create data
# ... binning
set.seed(1)
x <- rnorm(1000)
b1 <- bin(x, binmode = "equalN", nElements = 100)
b2 <- bin(x, binmode = "equalN", nElements = 50, minAbsX = 0.6)
se <- readRDS(system.file("extdata", "results.binned_motif_enrichment_LMRs.rds",
package = "monaLisa"))[1:10, 1:8]
seqs <- Biostrings::DNAStringSet(
vapply(seq_along(x),
function(i) paste(sample(c("A", "C", "G", "T"), 10,
replace = TRUE), collapse = ""), "")
)
# ... stability selection
Y <- rnorm(n = 100, mean = 2, sd = 1)
X <- matrix(data = runif(n = 20 * 100, min = 0, max = 3), nrow = length(Y), ncol = 20)
for (i in sample(x = seq_len(ncol(X)), size = 10, replace = FALSE))
X[ ,i] <- X[ ,i] + Y * c(1, -1)[(i %% 2) + 1]
ss <- monaLisa::randLassoStabSel(x = X, y = Y)
test_that("getColsByBin() works properly", {
c1 <- getColsByBin(as.numeric(b1))
c2 <- getColsByBin(b2)
expect_length(c1, 1000L)
expect_identical(as.vector(c1), as.vector(getColsByBin(c1)))
expect_equal(sort(unname(table(c2))), sort(unname(table(b2))))
})
test_that("plotBinHist() runs", {
tf <- tempfile(fileext = ".pdf")
pdf(file = tf)
expect_is(plotBinHist(x = x, b = b1), "histogram")
dev.off()
unlink(tf)
})
test_that("plotBinDensity() runs", {
tf <- tempfile(fileext = ".pdf")
pdf(file = tf)
expect_is(plotBinDensity(x = x, b = b1), "density")
dev.off()
unlink(tf)
})
test_that("plotBinDiagnostics() runs", {
tf <- tempfile(fileext = ".pdf")
pdf(file = tf)
expect_is(plotBinDiagnostics(seqs = seqs, bins = b1, aspect = "length"),
"list")
expect_is(plotBinDiagnostics(seqs = seqs, bins = b1, aspect = "GCfrac"),
"list")
expect_is(plotBinDiagnostics(seqs = seqs, bins = b1, aspect = "dinucfreq"),
"Heatmap")
dev.off()
unlink(tf)
expect_error(plotBinDiagnostics(seqs = x, bins = b1))
expect_error(plotBinDiagnostics(seqs = seqs, bins = as.numeric(b1)))
expect_error(plotBinDiagnostics(seqs = seqs, bins = as.character(b1)))
expect_error(plotBinDiagnostics(seqs = seqs, bins = b1, aspect = "missing"))
})
test_that("plotBinScatter() runs", {
tf <- tempfile(fileext = ".pdf")
pdf(file = tf)
expect_true(plotBinScatter(x = x, y = x, b = b1))
expect_error(plotBinScatter(x = x, y = x, b = b1, cols = "gray"))
expect_true(plotBinScatter(x = x, y = x, b = b1, cols = "gray", legend = FALSE))
dev.off()
unlink(tf)
})
test_that("plotMotifHeatmaps() runs", {
expect_error(plotMotifHeatmaps(x = se, cluster = "error"))
tf <- tempfile(fileext = ".pdf")
pdf(file = tf)
expect_is(plotMotifHeatmaps(x = se, which.plots = "pearsonResid", cluster = FALSE, show_motif_GC = TRUE), "list")
expect_is(plotMotifHeatmaps(x = se, which.plots = "negLog10Padj", cluster = TRUE, show_seqlogo = TRUE), "list")
cl <- hclust(dist(SummarizedExperiment::assay(se, "log2enr")))
expect_is(plotMotifHeatmaps(x = se, which.plots = "log2enr", cluster = cl, show_dendrogram = TRUE), "list")
se2 <- se
tmp <- SummarizedExperiment::assay(se2, "pearsonResid")
tmp[1:2, ] <- NA
SummarizedExperiment::assay(se2, "pearsonResid") <- tmp
expect_warning(res <- plotMotifHeatmaps(x = se2, which.plots = "log2enr", cluster = TRUE))
expect_is(res, "list")
dev.off()
unlink(tf)
})
test_that("plotStabilityPaths() runs", {
tf <- tempfile(fileext = ".pdf")
pdf(file = tf)
expect_error(plotStabilityPaths("error"))
expect_true(plotStabilityPaths(ss))
dev.off()
unlink(tf)
})
test_that("plotSelectionProb() runs", {
tf <- tempfile(fileext = ".pdf")
pdf(file = tf)
expect_error(plotSelectionProb(se = "error", selProbMin = 0.5))
expect_error(plotSelectionProb(se = ss, directional = "error"), "logical")
expect_error(plotSelectionProb(se = ss, directional = TRUE, selProbMin = 2.0), "within")
expect_error(plotSelectionProb(se = ss, selProbMinPlot = "error"), "numeric")
expect_error(plotSelectionProb(se = ss, selProbMin = 0.5, selProbMinPlot = 0.6))
expect_error(plotSelectionProb(se = ss, showSelProbMin = "error"))
expect_error(plotSelectionProb(se = ss, col = "error"), "length 3")
expect_error(plotSelectionProb(se = ss, method = "error"), "should be one of")
expect_error(plotSelectionProb(se = ss, legend = "error"))
expect_null(plotSelectionProb(ss, selProbMin = 1.0, selProbMinPlot = 0.99))
expect_is(plotSelectionProb(ss), "matrix")
expect_is(plotSelectionProb(ss, FALSE), "matrix")
dev.off()
unlink(tf)
})
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