tests/testthat/test_CytoImageList-manipulation_ondisk.R
In BodenmillerGroup/SingleCellMapper: Visualization of highly multiplexed imaging data in R
test_that("On disk: CytoImageList can be scaled.", {
data("pancreasImages")
cur_path <- tempdir()
on.exit(unlink(cur_path))
cur_Images <- CytoImageList(pancreasImages, on_disk = TRUE, h5FilesPath = cur_path)
cur_size <- file.info(paste0(cur_path, "/E34_imc.h5"))[,"size"]
# Works - single numeric
expect_silent(cur_Images_2 <- scaleImages(cur_Images, 1))
expect_s4_class(cur_Images_2[[1]], "DelayedArray")
expect_s4_class(cur_Images_2[[2]], "DelayedArray")
expect_s4_class(cur_Images_2[[3]], "DelayedArray")
expect_identical(as.array(cur_Images_2[[1]]), as.array(cur_Images[[1]]))
expect_silent(cur_Images_2 <- scaleImages(cur_Images, 2))
expect_identical(as.array(cur_Images_2[[1]]), as.array(cur_Images[[1]]) * 2)
image.list <- list.files(system.file("extdata", package = "cytomapper"),
pattern = "mask.tiff", full.names = TRUE)
cur_Images_2 <- loadImages(image.list, on_disk = TRUE, h5FilesPath = cur_path)
expect_equal(cur_Images_2[[1]][11, 1:2],
c(0.01257343, 0.01257343))
expect_error(plotCells(cur_Images_2),
regexp = "Segmentation masks must only contain integer values.",
fixed = TRUE)
expect_silent(cur_Images_2 <- scaleImages(cur_Images_2, (2^16)-1))
expect_identical(cur_Images_2[[1]][11, 1:2],
c(824, 824))
# Works - numeric vector
expect_silent(cur_images <- scaleImages(cur_Images, c(1, 1, 1)))
expect_identical(as.array(imageData(cur_images[[1]])), as.array(imageData(cur_Images[[1]])))
expect_identical(as.array(imageData(cur_images[[2]])), as.array(imageData(cur_Images[[2]])))
expect_identical(as.array(imageData(cur_images[[3]])), as.array(imageData(cur_Images[[3]])))
expect_silent(cur_images <- scaleImages(cur_Images, c(2, 3, 4)))
expect_identical(as.array(imageData(cur_images[[1]])),
as.array(imageData(cur_Images[[1]])) * 2)
expect_identical(as.array(imageData(cur_images[[2]])),
as.array(imageData(cur_Images[[2]])) * 3)
expect_identical(as.array(imageData(cur_images[[3]])),
as.array(imageData(cur_Images[[3]])) * 4)
expect_silent(plotPixels(cur_images))
# Error
expect_error(scaleImages(cur_Images, c(1,2)),
regexp = "'value' must either be a single numeric or of the same length as 'object'.",
fixed = TRUE)
expect_error(scaleImages(cur_Images, "test"),
regexp = "'value' must be numeric.",
fixed = TRUE)
expect_error(scaleImages(cur_Images, c(1, "test")),
regexp = "'value' must be numeric.",
fixed = TRUE)
expect_error(scaleImages(cur_Images, c(1, 2, "test")),
regexp = "'value' must be numeric.",
fixed = TRUE)
expect_error(scaleImages(cur_Images, c(1, 2, 3, 4)),
regexp = "'value' must either be a single numeric or of the same length as 'object'.",
fixed = TRUE)
expect_equal(cur_size, file.info(paste0(cur_path, "/E34_imc.h5"))[,"size"])
})
test_that("On disk: CytoImageList can be normalized", {
data("pancreasImages")
cur_path <- tempdir()
on.exit(unlink(cur_path))
cur_Images <- CytoImageList(pancreasImages, on_disk = TRUE, h5FilesPath = cur_path)
cur_size <- file.info(paste0(cur_path, "/E34_imc.h5"))[,"size"]
# Works
expect_silent(cur_images <- normalize(cur_Images))
expect_silent(plotPixels(cur_images))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99")))
expect_s4_class(cur_images$E34_imc, "DelayedArray")
expect_s4_class(cur_images$G01_imc, "DelayedArray")
expect_s4_class(cur_images$J02_imc, "DelayedArray")
cur_size_2 <- file.info(paste0(cur_path, "/E34_imc.h5"))[,"size"]
expect_gt(cur_size_2, cur_size)
expect_equal(object.size(cur_images), object.size(cur_Images), tolerance = 0.01)
# Check if second layer was created
expect_true("E34_imc_norm" %in% h5ls(path(cur_images$E34_imc@seed))$name)
expect_true(".E34_imc_norm_dimnames" %in% h5ls(path(cur_images$E34_imc@seed))$name)
# Test if it can be renormalized again
expect_silent(cur_images <- normalize(cur_Images))
expect_s4_class(cur_images$E34_imc, "DelayedArray")
expect_s4_class(cur_images$G01_imc, "DelayedArray")
expect_s4_class(cur_images$J02_imc, "DelayedArray")
expect_equal(object.size(cur_images), object.size(cur_Images), tolerance = 0.01)
# Separate images
# Separate channels
expect_silent(cur_images <- normalize(cur_Images, separateImages = TRUE,
separateChannels = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_equal(as.array(cur_images[[1]])[1, 1:10,1],
imageData(pancreasImages[[1]])[1, 1:10,1]/max(imageData(pancreasImages[[1]])[,,1]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,2],
imageData(pancreasImages[[1]])[1, 1:10,2]/max(imageData(pancreasImages[[1]])[,,2]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,3],
imageData(pancreasImages[[1]])[1, 1:10,3]/max(imageData(pancreasImages[[1]])[,,3]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,4],
imageData(pancreasImages[[1]])[1, 1:10,4]/max(imageData(pancreasImages[[1]])[,,4]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,5],
imageData(pancreasImages[[1]])[1, 1:10,5]/max(imageData(pancreasImages[[1]])[,,5]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,1],
imageData(pancreasImages[[2]])[1, 1:10,1]/max(imageData(pancreasImages[[2]])[,,1]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,2],
imageData(pancreasImages[[2]])[1, 1:10,2]/max(imageData(pancreasImages[[2]])[,,2]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,3],
imageData(pancreasImages[[2]])[1, 1:10,3]/max(imageData(pancreasImages[[2]])[,,3]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,4],
imageData(pancreasImages[[2]])[1, 1:10,4]/max(imageData(pancreasImages[[2]])[,,4]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,5],
imageData(pancreasImages[[2]])[1, 1:10,5]/max(imageData(pancreasImages[[2]])[,,5]),
tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,1]),
1, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,2]),
1, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,3]),
1, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,4]),
1, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,5]),
1, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,1]),
1, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,2]),
1, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,3]),
1, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,4]),
1, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,5]),
1, tolerance = 1e-06)
# Separate images
# Not Separate channels
expect_silent(cur_images <- normalize(cur_Images, separateImages = TRUE,
separateChannels = FALSE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_equal(as.array(cur_images[[1]])[1, 1:10,1],
imageData(pancreasImages[[1]])[1, 1:10,1]/max(imageData(pancreasImages[[1]])),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,2],
imageData(pancreasImages[[1]])[1, 1:10,2]/max(imageData(pancreasImages[[1]])),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,3],
imageData(pancreasImages[[1]])[1, 1:10,3]/max(imageData(pancreasImages[[1]])),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,4],
imageData(pancreasImages[[1]])[1, 1:10,4]/max(imageData(pancreasImages[[1]])),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,5],
imageData(pancreasImages[[1]])[1, 1:10,5]/max(imageData(pancreasImages[[1]])),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,1],
imageData(pancreasImages[[2]])[1, 1:10,1]/max(imageData(pancreasImages[[2]])),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,2],
imageData(pancreasImages[[2]])[1, 1:10,2]/max(imageData(pancreasImages[[2]])),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,3],
imageData(pancreasImages[[2]])[1, 1:10,3]/max(imageData(pancreasImages[[2]])),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,4],
imageData(pancreasImages[[2]])[1, 1:10,4]/max(imageData(pancreasImages[[2]])),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,5],
imageData(pancreasImages[[2]])[1, 1:10,5]/max(imageData(pancreasImages[[2]])),
tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,1]),
max(imageData(pancreasImages[[1]])[,,1])/max(imageData(pancreasImages[[1]])), tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,2]),
max(imageData(pancreasImages[[1]])[,,2])/max(imageData(pancreasImages[[1]])), tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,3]),
max(imageData(pancreasImages[[1]])[,,3])/max(imageData(pancreasImages[[1]])), tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,4]),
max(imageData(pancreasImages[[1]])[,,4])/max(imageData(pancreasImages[[1]])), tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,5]),
max(imageData(pancreasImages[[1]])[,,5])/max(imageData(pancreasImages[[1]])), tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,1]),
max(imageData(pancreasImages[[2]])[,,1])/max(imageData(pancreasImages[[2]])), tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,2]),
max(imageData(pancreasImages[[2]])[,,2])/max(imageData(pancreasImages[[2]])), tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,3]),
max(imageData(pancreasImages[[2]])[,,3])/max(imageData(pancreasImages[[2]])), tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,4]),
max(imageData(pancreasImages[[2]])[,,4])/max(imageData(pancreasImages[[2]])), tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,5]),
max(imageData(pancreasImages[[2]])[,,5])/max(imageData(pancreasImages[[2]])), tolerance = 1e-06)
# Not Separate images
# Separate channels
expect_silent(cur_images <- normalize(cur_Images, separateImages = FALSE,
separateChannels = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = FALSE))
cur_max <- sapply(pancreasImages, function(x){apply(x, 3, max)})
cur_max <- as.numeric(apply(cur_max, 1, max))
expect_equal(as.array(cur_images[[1]])[1, 1:10,1],
imageData(pancreasImages[[1]])[1, 1:10,1]/cur_max[1],
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,2],
imageData(pancreasImages[[1]])[1, 1:10,2]/cur_max[2],
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,3],
imageData(pancreasImages[[1]])[1, 1:10,3]/cur_max[3],
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,4],
imageData(pancreasImages[[1]])[1, 1:10,4]/cur_max[4],
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,5],
imageData(pancreasImages[[1]])[1, 1:10,5]/cur_max[5],
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,1],
imageData(pancreasImages[[2]])[1, 1:10,1]/cur_max[1],
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,2],
imageData(pancreasImages[[2]])[1, 1:10,2]/cur_max[2],
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,3],
imageData(pancreasImages[[2]])[1, 1:10,3]/cur_max[3],
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,4],
imageData(pancreasImages[[2]])[1, 1:10,4]/cur_max[4],
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,5],
imageData(pancreasImages[[2]])[1, 1:10,5]/cur_max[5],
tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,1]),
max(imageData(pancreasImages[[1]])[,,1])/cur_max[1], tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,2]),
max(imageData(pancreasImages[[1]])[,,2])/cur_max[2], tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,3]),
max(imageData(pancreasImages[[1]])[,,3])/cur_max[3], tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,4]),
max(imageData(pancreasImages[[1]])[,,4])/cur_max[4], tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,5]),
max(imageData(pancreasImages[[1]])[,,5])/cur_max[5], tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,1]),
max(imageData(pancreasImages[[2]])[,,1])/cur_max[1], tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,2]),
max(imageData(pancreasImages[[2]])[,,2])/cur_max[2], tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,3]),
max(imageData(pancreasImages[[2]])[,,3])/cur_max[3], tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,4]),
max(imageData(pancreasImages[[2]])[,,4])/cur_max[4], tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,5]),
max(imageData(pancreasImages[[2]])[,,5])/cur_max[5], tolerance = 1e-06)
# Single frame
cur_img1 <- cur_Images[[1]][,,1]
cur_img2 <- cur_Images[[2]][,,1]
cur_img <- CytoImageList(img1 = cur_img1, img2 = cur_img2, on_disk = TRUE,
h5FilesPath = cur_path)
channelNames(cur_img) <- "H3"
expect_silent(cur_images <- normalize(cur_img, separateImages = FALSE,
separateChannels = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = "H3"))
cur_max <- max(max(cur_img1), max(cur_img2))
expect_equal(max(as.array(cur_images[[1]])[,,1]),
max(imageData(pancreasImages[[1]])[,,1])/cur_max, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,1]),
max(imageData(pancreasImages[[2]])[,,1])/cur_max, tolerance = 1e-06)
# Not Separate images
# Not Separate channels
expect_silent(cur_images <- normalize(cur_Images, separateImages = FALSE,
separateChannels = FALSE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = FALSE))
cur_max <- sapply(pancreasImages, max)
cur_max <- as.numeric(max(cur_max))
expect_equal(as.array(cur_images[[1]])[1, 1:10,1],
imageData(pancreasImages[[1]])[1, 1:10,1]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,2],
imageData(pancreasImages[[1]])[1, 1:10,2]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,3],
imageData(pancreasImages[[1]])[1, 1:10,3]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,4],
imageData(pancreasImages[[1]])[1, 1:10,4]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,5],
imageData(pancreasImages[[1]])[1, 1:10,5]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,1],
imageData(pancreasImages[[2]])[1, 1:10,1]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,2],
imageData(pancreasImages[[2]])[1, 1:10,2]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,3],
imageData(pancreasImages[[2]])[1, 1:10,3]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,4],
imageData(pancreasImages[[2]])[1, 1:10,4]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,5],
imageData(pancreasImages[[2]])[1, 1:10,5]/cur_max,
tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,1]),
max(imageData(pancreasImages[[1]])[,,1])/cur_max, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,2]),
max(imageData(pancreasImages[[1]])[,,2])/cur_max, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,3]),
max(imageData(pancreasImages[[1]])[,,3])/cur_max, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,4]),
max(imageData(pancreasImages[[1]])[,,4])/cur_max, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,5]),
max(imageData(pancreasImages[[1]])[,,5])/cur_max, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,1]),
max(imageData(pancreasImages[[2]])[,,1])/cur_max, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,2]),
max(imageData(pancreasImages[[2]])[,,2])/cur_max, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,3]),
max(imageData(pancreasImages[[2]])[,,3])/cur_max, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,4]),
max(imageData(pancreasImages[[2]])[,,4])/cur_max, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,5]),
max(imageData(pancreasImages[[2]])[,,5])/cur_max, tolerance = 1e-06)
# Setting the inputRange
# Separate images
# Separate channels
expect_silent(cur_images <- normalize(cur_Images, separateImages = TRUE,
separateChannels = TRUE))
expect_silent(cur_images2 <- normalize(cur_images, separateImages = TRUE,
separateChannels = TRUE, inputRange = c(0, 0.9)))
expect_silent(cur_images <- normalize(pancreasImages, separateImages = TRUE,
separateChannels = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images2,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images2,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_equal(as.array(cur_images2[[1]])[1, 1:10,1],
imageData(cur_images[[1]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,2],
imageData(cur_images[[1]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,3],
imageData(cur_images[[1]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,4],
imageData(cur_images[[1]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,5],
imageData(cur_images[[1]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,1],
imageData(cur_images[[2]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,2],
imageData(cur_images[[2]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,3],
imageData(cur_images[[2]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,4],
imageData(cur_images[[2]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,5],
imageData(cur_images[[2]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
# Separate images
# Not Separate channels
expect_silent(cur_images <- normalize(cur_Images, separateImages = TRUE,
separateChannels = FALSE))
expect_silent(cur_images2 <- normalize(cur_images, separateImages = TRUE,
separateChannels = FALSE, inputRange = c(0, 0.9)))
expect_silent(cur_images <- normalize(pancreasImages, separateImages = TRUE,
separateChannels = FALSE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images2,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images2,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_equal(as.array(cur_images2[[1]])[1, 1:10,1],
imageData(cur_images[[1]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,2],
imageData(cur_images[[1]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,3],
imageData(cur_images[[1]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,4],
imageData(cur_images[[1]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,5],
imageData(cur_images[[1]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,1],
imageData(cur_images[[2]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,2],
imageData(cur_images[[2]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,3],
imageData(cur_images[[2]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,4],
imageData(cur_images[[2]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,5],
imageData(cur_images[[2]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
# Not Separate images
# Separate channels
expect_silent(cur_images <- normalize(cur_Images, separateImages = FALSE,
separateChannels = TRUE))
expect_silent(cur_images2 <- normalize(cur_images, separateImages = FALSE,
separateChannels = TRUE, inputRange = c(0, 0.9)))
expect_silent(cur_images <- normalize(pancreasImages, separateImages = FALSE,
separateChannels = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images2,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images2,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_equal(as.array(cur_images2[[1]])[1, 1:10,1],
imageData(cur_images[[1]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,2],
imageData(cur_images[[1]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,3],
imageData(cur_images[[1]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,4],
imageData(cur_images[[1]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,5],
imageData(cur_images[[1]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,1],
imageData(cur_images[[2]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,2],
imageData(cur_images[[2]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,3],
imageData(cur_images[[2]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,4],
imageData(cur_images[[2]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,5],
imageData(cur_images[[2]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
# Separate images
# Not Separate channels
expect_silent(cur_images <- normalize(cur_Images, separateImages = TRUE,
separateChannels = FALSE))
expect_silent(cur_images2 <- normalize(cur_images, separateImages = TRUE,
separateChannels = FALSE, inputRange = c(0, 0.9)))
expect_silent(cur_images <- normalize(pancreasImages, separateImages = TRUE,
separateChannels = FALSE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images2,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images2,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_equal(as.array(cur_images2[[1]])[1, 1:10,1],
imageData(cur_images[[1]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,2],
imageData(cur_images[[1]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,3],
imageData(cur_images[[1]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,4],
imageData(cur_images[[1]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,5],
imageData(cur_images[[1]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,1],
imageData(cur_images[[2]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,2],
imageData(cur_images[[2]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,3],
imageData(cur_images[[2]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,4],
imageData(cur_images[[2]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,5],
imageData(cur_images[[2]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
# Not Separate images
# Not Separate channels
expect_silent(cur_images <- normalize(cur_Images, separateImages = FALSE,
separateChannels = FALSE))
expect_silent(cur_images2 <- normalize(cur_images, separateImages = FALSE,
separateChannels = FALSE, inputRange = c(0, 0.9)))
expect_silent(cur_images <- normalize(pancreasImages, separateImages = FALSE,
separateChannels = FALSE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images2,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images2,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_equal(as.array(cur_images2[[1]])[1, 1:10,1],
imageData(cur_images[[1]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,2],
imageData(cur_images[[1]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,3],
imageData(cur_images[[1]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,4],
imageData(cur_images[[1]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,5],
imageData(cur_images[[1]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,1],
imageData(cur_images[[2]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,2],
imageData(cur_images[[2]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,3],
imageData(cur_images[[2]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,4],
imageData(cur_images[[2]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,5],
imageData(cur_images[[2]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
# Setting ft
expect_silent(cur_images <- normalize(cur_Images, separateImages = TRUE,
separateChannels = TRUE, ft = c(0, 2)))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_silent(cur_images <- normalize(cur_Images, separateImages = TRUE,
separateChannels = FALSE, ft = c(0, 2)))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_silent(cur_images <- normalize(cur_Images, separateImages = FALSE,
separateChannels = TRUE, ft = c(0, 2)))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_silent(cur_images <- normalize(cur_Images, separateImages = FALSE,
separateChannels = FALSE, ft = c(0, 2)))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = FALSE))
# Channel-wise normalization
# Separate images
expect_silent(cur_images <- normalize(cur_Images, separateImages = TRUE,
inputRange = list(H3 = c(0,50), CD99 = c(0,70))))
expect_silent(plotPixels(cur_Images,
colour_by = c("H3", "CD99", "PIN")))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99", "PIN")))
expect_equal(as.array(cur_images[[1]])[1, 1:10,"H3"],
imageData(pancreasImages[[1]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,"CD99"],
imageData(pancreasImages[[1]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,"PIN"],
imageData(pancreasImages[[1]])[1, 1:10,"PIN"]/max(imageData(pancreasImages[[1]])[,,"PIN"]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,"H3"],
imageData(pancreasImages[[2]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,"CD99"],
imageData(pancreasImages[[2]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,"PIN"],
imageData(pancreasImages[[2]])[1, 1:10,"PIN"]/max(imageData(pancreasImages[[1]])[,,"PIN"]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[3]])[1, 1:10,"H3"],
imageData(pancreasImages[[3]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[3]])[1, 1:10,"CD99"],
imageData(pancreasImages[[3]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[3]])[1, 1:10,"PIN"],
imageData(pancreasImages[[3]])[1, 1:10,"PIN"]/max(imageData(pancreasImages[[1]])[,,"PIN"]),
tolerance = 1e-06)
# Not Separate images
expect_silent(cur_images <- normalize(cur_Images, separateImages = FALSE,
inputRange = list(H3 = c(0,50), CD99 = c(0,70))))
expect_silent(plotPixels(cur_Images,
colour_by = c("H3", "CD99", "PIN")))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99", "PIN")))
expect_equal(as.array(cur_images[[1]])[1, 1:10,"H3"],
imageData(pancreasImages[[1]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,"CD99"],
imageData(pancreasImages[[1]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
cur_max <- max(c(max(imageData(pancreasImages[[1]])[,,"PIN"]),
max(imageData(pancreasImages[[2]])[,,"PIN"]),
max(imageData(pancreasImages[[3]])[,,"PIN"])))
expect_equal(as.array(cur_images[[1]])[1, 1:10,"PIN"],
imageData(pancreasImages[[1]])[1, 1:10,"PIN"]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,"H3"],
imageData(pancreasImages[[2]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,"CD99"],
imageData(pancreasImages[[2]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,"PIN"],
imageData(pancreasImages[[2]])[1, 1:10,"PIN"]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[3]])[1, 1:10,"H3"],
imageData(pancreasImages[[3]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[3]])[1, 1:10,"CD99"],
imageData(pancreasImages[[3]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[3]])[1, 1:10,"PIN"],
imageData(pancreasImages[[3]])[1, 1:10,"PIN"]/cur_max,
tolerance = 1e-06)
# Error
expect_error(normalize(cur_Images, separateChannels = "test"),
regexp = "'separateChannels' only takes TRUE or FALSE.")
expect_error(normalize(cur_Images, separateImages = "test"),
regexp = "'separateImages' only takes TRUE or FALSE.")
expect_error(normalize(cur_Images, inputRange = "test"),
regexp = "'inputRange' takes a vector of length 2, a list or NULL.")
expect_error(normalize(cur_Images, inputRange = 2),
regexp = "'inputRange' takes a vector of length 2, a list or NULL.")
cur_images <- cur_Images
channelNames(cur_images) <- NULL
expect_error(normalize(cur_images, inputRange = list(H3 = c(0, 100), CDH = c(0, 20))),
regexp = "Please set the 'channelNames' of the CytoImageList object.")
expect_error(normalize(pancreasImages, inputRange = list(test = c(0, 100), CDH = c(0, 20))),
regexp = "The names of 'inputRange' should correspond to the'channelNames' of the CytoImageList object.")
# Check if overwriting works
expect_silent(cur_images <- normalize(cur_Images, overwrite = TRUE))
expect_s4_class(cur_images$E34_imc, "DelayedArray")
expect_s4_class(cur_images$G01_imc, "DelayedArray")
expect_s4_class(cur_images$J02_imc, "DelayedArray")
# Check if second layer was created
expect_true("E34_imc_norm" %in% h5ls(path(cur_images$E34_imc@seed))$name)
expect_true(".E34_imc_norm_dimnames" %in% h5ls(path(cur_images$E34_imc@seed))$name)
expect_false("E34_imc" %in% h5ls(path(cur_images$E34_imc@seed))$name)
expect_false(".E34_imc_dimnames" %in% h5ls(path(cur_images$E34_imc@seed))$name)
})
test_that("On disk: HDF5 handling works", {
cur_array <- array(data = rnorm(3000), dim = c(100, 100, 3))
cur_array_2 <- array(data = runif(3000), dim = c(100, 100, 3))
cur_path <- tempdir()
on.exit(unlink(cur_path))
cur_file <- paste0(cur_path, "/test.h5")
cur_hdf5 <- writeHDF5Array(x = cur_array, filepath = cur_file, name = "test")
expect_equal(h5ls(cur_file)$name, "test")
expect_silent(test <- .add_h5(cur_obj = cur_hdf5, new_obj = cur_array_2,
overwrite = FALSE, suffix = "_norm"))
expect_equal(h5ls(cur_file)$name, c("test", "test_norm"))
expect_equal(as.array(test), cur_array_2)
expect_silent(test <- .add_h5(cur_obj = cur_hdf5, new_obj = cur_array_2,
overwrite = TRUE, suffix = "_norm"))
expect_equal(h5ls(cur_file)$name, "test_norm")
expect_equal(as.array(test), cur_array_2)
})
BodenmillerGroup/SingleCellMapper documentation built on March 26, 2024, 1:09 a.m.
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test_that("On disk: CytoImageList can be scaled.", {
data("pancreasImages")
cur_path <- tempdir()
on.exit(unlink(cur_path))
cur_Images <- CytoImageList(pancreasImages, on_disk = TRUE, h5FilesPath = cur_path)
cur_size <- file.info(paste0(cur_path, "/E34_imc.h5"))[,"size"]
# Works - single numeric
expect_silent(cur_Images_2 <- scaleImages(cur_Images, 1))
expect_s4_class(cur_Images_2[[1]], "DelayedArray")
expect_s4_class(cur_Images_2[[2]], "DelayedArray")
expect_s4_class(cur_Images_2[[3]], "DelayedArray")
expect_identical(as.array(cur_Images_2[[1]]), as.array(cur_Images[[1]]))
expect_silent(cur_Images_2 <- scaleImages(cur_Images, 2))
expect_identical(as.array(cur_Images_2[[1]]), as.array(cur_Images[[1]]) * 2)
image.list <- list.files(system.file("extdata", package = "cytomapper"),
pattern = "mask.tiff", full.names = TRUE)
cur_Images_2 <- loadImages(image.list, on_disk = TRUE, h5FilesPath = cur_path)
expect_equal(cur_Images_2[[1]][11, 1:2],
c(0.01257343, 0.01257343))
expect_error(plotCells(cur_Images_2),
regexp = "Segmentation masks must only contain integer values.",
fixed = TRUE)
expect_silent(cur_Images_2 <- scaleImages(cur_Images_2, (2^16)-1))
expect_identical(cur_Images_2[[1]][11, 1:2],
c(824, 824))
# Works - numeric vector
expect_silent(cur_images <- scaleImages(cur_Images, c(1, 1, 1)))
expect_identical(as.array(imageData(cur_images[[1]])), as.array(imageData(cur_Images[[1]])))
expect_identical(as.array(imageData(cur_images[[2]])), as.array(imageData(cur_Images[[2]])))
expect_identical(as.array(imageData(cur_images[[3]])), as.array(imageData(cur_Images[[3]])))
expect_silent(cur_images <- scaleImages(cur_Images, c(2, 3, 4)))
expect_identical(as.array(imageData(cur_images[[1]])),
as.array(imageData(cur_Images[[1]])) * 2)
expect_identical(as.array(imageData(cur_images[[2]])),
as.array(imageData(cur_Images[[2]])) * 3)
expect_identical(as.array(imageData(cur_images[[3]])),
as.array(imageData(cur_Images[[3]])) * 4)
expect_silent(plotPixels(cur_images))
# Error
expect_error(scaleImages(cur_Images, c(1,2)),
regexp = "'value' must either be a single numeric or of the same length as 'object'.",
fixed = TRUE)
expect_error(scaleImages(cur_Images, "test"),
regexp = "'value' must be numeric.",
fixed = TRUE)
expect_error(scaleImages(cur_Images, c(1, "test")),
regexp = "'value' must be numeric.",
fixed = TRUE)
expect_error(scaleImages(cur_Images, c(1, 2, "test")),
regexp = "'value' must be numeric.",
fixed = TRUE)
expect_error(scaleImages(cur_Images, c(1, 2, 3, 4)),
regexp = "'value' must either be a single numeric or of the same length as 'object'.",
fixed = TRUE)
expect_equal(cur_size, file.info(paste0(cur_path, "/E34_imc.h5"))[,"size"])
})
test_that("On disk: CytoImageList can be normalized", {
data("pancreasImages")
cur_path <- tempdir()
on.exit(unlink(cur_path))
cur_Images <- CytoImageList(pancreasImages, on_disk = TRUE, h5FilesPath = cur_path)
cur_size <- file.info(paste0(cur_path, "/E34_imc.h5"))[,"size"]
# Works
expect_silent(cur_images <- normalize(cur_Images))
expect_silent(plotPixels(cur_images))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99")))
expect_s4_class(cur_images$E34_imc, "DelayedArray")
expect_s4_class(cur_images$G01_imc, "DelayedArray")
expect_s4_class(cur_images$J02_imc, "DelayedArray")
cur_size_2 <- file.info(paste0(cur_path, "/E34_imc.h5"))[,"size"]
expect_gt(cur_size_2, cur_size)
expect_equal(object.size(cur_images), object.size(cur_Images), tolerance = 0.01)
# Check if second layer was created
expect_true("E34_imc_norm" %in% h5ls(path(cur_images$E34_imc@seed))$name)
expect_true(".E34_imc_norm_dimnames" %in% h5ls(path(cur_images$E34_imc@seed))$name)
# Test if it can be renormalized again
expect_silent(cur_images <- normalize(cur_Images))
expect_s4_class(cur_images$E34_imc, "DelayedArray")
expect_s4_class(cur_images$G01_imc, "DelayedArray")
expect_s4_class(cur_images$J02_imc, "DelayedArray")
expect_equal(object.size(cur_images), object.size(cur_Images), tolerance = 0.01)
# Separate images
# Separate channels
expect_silent(cur_images <- normalize(cur_Images, separateImages = TRUE,
separateChannels = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_equal(as.array(cur_images[[1]])[1, 1:10,1],
imageData(pancreasImages[[1]])[1, 1:10,1]/max(imageData(pancreasImages[[1]])[,,1]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,2],
imageData(pancreasImages[[1]])[1, 1:10,2]/max(imageData(pancreasImages[[1]])[,,2]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,3],
imageData(pancreasImages[[1]])[1, 1:10,3]/max(imageData(pancreasImages[[1]])[,,3]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,4],
imageData(pancreasImages[[1]])[1, 1:10,4]/max(imageData(pancreasImages[[1]])[,,4]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,5],
imageData(pancreasImages[[1]])[1, 1:10,5]/max(imageData(pancreasImages[[1]])[,,5]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,1],
imageData(pancreasImages[[2]])[1, 1:10,1]/max(imageData(pancreasImages[[2]])[,,1]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,2],
imageData(pancreasImages[[2]])[1, 1:10,2]/max(imageData(pancreasImages[[2]])[,,2]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,3],
imageData(pancreasImages[[2]])[1, 1:10,3]/max(imageData(pancreasImages[[2]])[,,3]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,4],
imageData(pancreasImages[[2]])[1, 1:10,4]/max(imageData(pancreasImages[[2]])[,,4]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,5],
imageData(pancreasImages[[2]])[1, 1:10,5]/max(imageData(pancreasImages[[2]])[,,5]),
tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,1]),
1, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,2]),
1, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,3]),
1, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,4]),
1, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,5]),
1, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,1]),
1, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,2]),
1, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,3]),
1, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,4]),
1, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,5]),
1, tolerance = 1e-06)
# Separate images
# Not Separate channels
expect_silent(cur_images <- normalize(cur_Images, separateImages = TRUE,
separateChannels = FALSE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_equal(as.array(cur_images[[1]])[1, 1:10,1],
imageData(pancreasImages[[1]])[1, 1:10,1]/max(imageData(pancreasImages[[1]])),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,2],
imageData(pancreasImages[[1]])[1, 1:10,2]/max(imageData(pancreasImages[[1]])),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,3],
imageData(pancreasImages[[1]])[1, 1:10,3]/max(imageData(pancreasImages[[1]])),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,4],
imageData(pancreasImages[[1]])[1, 1:10,4]/max(imageData(pancreasImages[[1]])),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,5],
imageData(pancreasImages[[1]])[1, 1:10,5]/max(imageData(pancreasImages[[1]])),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,1],
imageData(pancreasImages[[2]])[1, 1:10,1]/max(imageData(pancreasImages[[2]])),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,2],
imageData(pancreasImages[[2]])[1, 1:10,2]/max(imageData(pancreasImages[[2]])),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,3],
imageData(pancreasImages[[2]])[1, 1:10,3]/max(imageData(pancreasImages[[2]])),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,4],
imageData(pancreasImages[[2]])[1, 1:10,4]/max(imageData(pancreasImages[[2]])),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,5],
imageData(pancreasImages[[2]])[1, 1:10,5]/max(imageData(pancreasImages[[2]])),
tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,1]),
max(imageData(pancreasImages[[1]])[,,1])/max(imageData(pancreasImages[[1]])), tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,2]),
max(imageData(pancreasImages[[1]])[,,2])/max(imageData(pancreasImages[[1]])), tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,3]),
max(imageData(pancreasImages[[1]])[,,3])/max(imageData(pancreasImages[[1]])), tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,4]),
max(imageData(pancreasImages[[1]])[,,4])/max(imageData(pancreasImages[[1]])), tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,5]),
max(imageData(pancreasImages[[1]])[,,5])/max(imageData(pancreasImages[[1]])), tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,1]),
max(imageData(pancreasImages[[2]])[,,1])/max(imageData(pancreasImages[[2]])), tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,2]),
max(imageData(pancreasImages[[2]])[,,2])/max(imageData(pancreasImages[[2]])), tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,3]),
max(imageData(pancreasImages[[2]])[,,3])/max(imageData(pancreasImages[[2]])), tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,4]),
max(imageData(pancreasImages[[2]])[,,4])/max(imageData(pancreasImages[[2]])), tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,5]),
max(imageData(pancreasImages[[2]])[,,5])/max(imageData(pancreasImages[[2]])), tolerance = 1e-06)
# Not Separate images
# Separate channels
expect_silent(cur_images <- normalize(cur_Images, separateImages = FALSE,
separateChannels = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = FALSE))
cur_max <- sapply(pancreasImages, function(x){apply(x, 3, max)})
cur_max <- as.numeric(apply(cur_max, 1, max))
expect_equal(as.array(cur_images[[1]])[1, 1:10,1],
imageData(pancreasImages[[1]])[1, 1:10,1]/cur_max[1],
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,2],
imageData(pancreasImages[[1]])[1, 1:10,2]/cur_max[2],
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,3],
imageData(pancreasImages[[1]])[1, 1:10,3]/cur_max[3],
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,4],
imageData(pancreasImages[[1]])[1, 1:10,4]/cur_max[4],
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,5],
imageData(pancreasImages[[1]])[1, 1:10,5]/cur_max[5],
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,1],
imageData(pancreasImages[[2]])[1, 1:10,1]/cur_max[1],
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,2],
imageData(pancreasImages[[2]])[1, 1:10,2]/cur_max[2],
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,3],
imageData(pancreasImages[[2]])[1, 1:10,3]/cur_max[3],
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,4],
imageData(pancreasImages[[2]])[1, 1:10,4]/cur_max[4],
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,5],
imageData(pancreasImages[[2]])[1, 1:10,5]/cur_max[5],
tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,1]),
max(imageData(pancreasImages[[1]])[,,1])/cur_max[1], tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,2]),
max(imageData(pancreasImages[[1]])[,,2])/cur_max[2], tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,3]),
max(imageData(pancreasImages[[1]])[,,3])/cur_max[3], tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,4]),
max(imageData(pancreasImages[[1]])[,,4])/cur_max[4], tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,5]),
max(imageData(pancreasImages[[1]])[,,5])/cur_max[5], tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,1]),
max(imageData(pancreasImages[[2]])[,,1])/cur_max[1], tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,2]),
max(imageData(pancreasImages[[2]])[,,2])/cur_max[2], tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,3]),
max(imageData(pancreasImages[[2]])[,,3])/cur_max[3], tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,4]),
max(imageData(pancreasImages[[2]])[,,4])/cur_max[4], tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,5]),
max(imageData(pancreasImages[[2]])[,,5])/cur_max[5], tolerance = 1e-06)
# Single frame
cur_img1 <- cur_Images[[1]][,,1]
cur_img2 <- cur_Images[[2]][,,1]
cur_img <- CytoImageList(img1 = cur_img1, img2 = cur_img2, on_disk = TRUE,
h5FilesPath = cur_path)
channelNames(cur_img) <- "H3"
expect_silent(cur_images <- normalize(cur_img, separateImages = FALSE,
separateChannels = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = "H3"))
cur_max <- max(max(cur_img1), max(cur_img2))
expect_equal(max(as.array(cur_images[[1]])[,,1]),
max(imageData(pancreasImages[[1]])[,,1])/cur_max, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,1]),
max(imageData(pancreasImages[[2]])[,,1])/cur_max, tolerance = 1e-06)
# Not Separate images
# Not Separate channels
expect_silent(cur_images <- normalize(cur_Images, separateImages = FALSE,
separateChannels = FALSE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = FALSE))
cur_max <- sapply(pancreasImages, max)
cur_max <- as.numeric(max(cur_max))
expect_equal(as.array(cur_images[[1]])[1, 1:10,1],
imageData(pancreasImages[[1]])[1, 1:10,1]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,2],
imageData(pancreasImages[[1]])[1, 1:10,2]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,3],
imageData(pancreasImages[[1]])[1, 1:10,3]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,4],
imageData(pancreasImages[[1]])[1, 1:10,4]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,5],
imageData(pancreasImages[[1]])[1, 1:10,5]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,1],
imageData(pancreasImages[[2]])[1, 1:10,1]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,2],
imageData(pancreasImages[[2]])[1, 1:10,2]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,3],
imageData(pancreasImages[[2]])[1, 1:10,3]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,4],
imageData(pancreasImages[[2]])[1, 1:10,4]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,5],
imageData(pancreasImages[[2]])[1, 1:10,5]/cur_max,
tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,1]),
max(imageData(pancreasImages[[1]])[,,1])/cur_max, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,2]),
max(imageData(pancreasImages[[1]])[,,2])/cur_max, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,3]),
max(imageData(pancreasImages[[1]])[,,3])/cur_max, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,4]),
max(imageData(pancreasImages[[1]])[,,4])/cur_max, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[1]])[,,5]),
max(imageData(pancreasImages[[1]])[,,5])/cur_max, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,1]),
max(imageData(pancreasImages[[2]])[,,1])/cur_max, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,2]),
max(imageData(pancreasImages[[2]])[,,2])/cur_max, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,3]),
max(imageData(pancreasImages[[2]])[,,3])/cur_max, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,4]),
max(imageData(pancreasImages[[2]])[,,4])/cur_max, tolerance = 1e-06)
expect_equal(max(as.array(cur_images[[2]])[,,5]),
max(imageData(pancreasImages[[2]])[,,5])/cur_max, tolerance = 1e-06)
# Setting the inputRange
# Separate images
# Separate channels
expect_silent(cur_images <- normalize(cur_Images, separateImages = TRUE,
separateChannels = TRUE))
expect_silent(cur_images2 <- normalize(cur_images, separateImages = TRUE,
separateChannels = TRUE, inputRange = c(0, 0.9)))
expect_silent(cur_images <- normalize(pancreasImages, separateImages = TRUE,
separateChannels = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images2,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images2,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_equal(as.array(cur_images2[[1]])[1, 1:10,1],
imageData(cur_images[[1]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,2],
imageData(cur_images[[1]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,3],
imageData(cur_images[[1]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,4],
imageData(cur_images[[1]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,5],
imageData(cur_images[[1]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,1],
imageData(cur_images[[2]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,2],
imageData(cur_images[[2]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,3],
imageData(cur_images[[2]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,4],
imageData(cur_images[[2]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,5],
imageData(cur_images[[2]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
# Separate images
# Not Separate channels
expect_silent(cur_images <- normalize(cur_Images, separateImages = TRUE,
separateChannels = FALSE))
expect_silent(cur_images2 <- normalize(cur_images, separateImages = TRUE,
separateChannels = FALSE, inputRange = c(0, 0.9)))
expect_silent(cur_images <- normalize(pancreasImages, separateImages = TRUE,
separateChannels = FALSE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images2,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images2,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_equal(as.array(cur_images2[[1]])[1, 1:10,1],
imageData(cur_images[[1]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,2],
imageData(cur_images[[1]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,3],
imageData(cur_images[[1]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,4],
imageData(cur_images[[1]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,5],
imageData(cur_images[[1]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,1],
imageData(cur_images[[2]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,2],
imageData(cur_images[[2]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,3],
imageData(cur_images[[2]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,4],
imageData(cur_images[[2]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,5],
imageData(cur_images[[2]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
# Not Separate images
# Separate channels
expect_silent(cur_images <- normalize(cur_Images, separateImages = FALSE,
separateChannels = TRUE))
expect_silent(cur_images2 <- normalize(cur_images, separateImages = FALSE,
separateChannels = TRUE, inputRange = c(0, 0.9)))
expect_silent(cur_images <- normalize(pancreasImages, separateImages = FALSE,
separateChannels = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images2,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images2,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_equal(as.array(cur_images2[[1]])[1, 1:10,1],
imageData(cur_images[[1]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,2],
imageData(cur_images[[1]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,3],
imageData(cur_images[[1]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,4],
imageData(cur_images[[1]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,5],
imageData(cur_images[[1]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,1],
imageData(cur_images[[2]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,2],
imageData(cur_images[[2]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,3],
imageData(cur_images[[2]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,4],
imageData(cur_images[[2]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,5],
imageData(cur_images[[2]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
# Separate images
# Not Separate channels
expect_silent(cur_images <- normalize(cur_Images, separateImages = TRUE,
separateChannels = FALSE))
expect_silent(cur_images2 <- normalize(cur_images, separateImages = TRUE,
separateChannels = FALSE, inputRange = c(0, 0.9)))
expect_silent(cur_images <- normalize(pancreasImages, separateImages = TRUE,
separateChannels = FALSE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images2,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images2,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_equal(as.array(cur_images2[[1]])[1, 1:10,1],
imageData(cur_images[[1]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,2],
imageData(cur_images[[1]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,3],
imageData(cur_images[[1]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,4],
imageData(cur_images[[1]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,5],
imageData(cur_images[[1]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,1],
imageData(cur_images[[2]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,2],
imageData(cur_images[[2]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,3],
imageData(cur_images[[2]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,4],
imageData(cur_images[[2]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,5],
imageData(cur_images[[2]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
# Not Separate images
# Not Separate channels
expect_silent(cur_images <- normalize(cur_Images, separateImages = FALSE,
separateChannels = FALSE))
expect_silent(cur_images2 <- normalize(cur_images, separateImages = FALSE,
separateChannels = FALSE, inputRange = c(0, 0.9)))
expect_silent(cur_images <- normalize(pancreasImages, separateImages = FALSE,
separateChannels = FALSE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images2,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images2,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_equal(as.array(cur_images2[[1]])[1, 1:10,1],
imageData(cur_images[[1]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,2],
imageData(cur_images[[1]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,3],
imageData(cur_images[[1]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,4],
imageData(cur_images[[1]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[1]])[1, 1:10,5],
imageData(cur_images[[1]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,1],
imageData(cur_images[[2]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,2],
imageData(cur_images[[2]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,3],
imageData(cur_images[[2]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,4],
imageData(cur_images[[2]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(as.array(cur_images2[[2]])[1, 1:10,5],
imageData(cur_images[[2]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
# Setting ft
expect_silent(cur_images <- normalize(cur_Images, separateImages = TRUE,
separateChannels = TRUE, ft = c(0, 2)))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_silent(cur_images <- normalize(cur_Images, separateImages = TRUE,
separateChannels = FALSE, ft = c(0, 2)))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_silent(cur_images <- normalize(cur_Images, separateImages = FALSE,
separateChannels = TRUE, ft = c(0, 2)))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_silent(cur_images <- normalize(cur_Images, separateImages = FALSE,
separateChannels = FALSE, ft = c(0, 2)))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = FALSE))
# Channel-wise normalization
# Separate images
expect_silent(cur_images <- normalize(cur_Images, separateImages = TRUE,
inputRange = list(H3 = c(0,50), CD99 = c(0,70))))
expect_silent(plotPixels(cur_Images,
colour_by = c("H3", "CD99", "PIN")))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99", "PIN")))
expect_equal(as.array(cur_images[[1]])[1, 1:10,"H3"],
imageData(pancreasImages[[1]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,"CD99"],
imageData(pancreasImages[[1]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,"PIN"],
imageData(pancreasImages[[1]])[1, 1:10,"PIN"]/max(imageData(pancreasImages[[1]])[,,"PIN"]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,"H3"],
imageData(pancreasImages[[2]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,"CD99"],
imageData(pancreasImages[[2]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,"PIN"],
imageData(pancreasImages[[2]])[1, 1:10,"PIN"]/max(imageData(pancreasImages[[1]])[,,"PIN"]),
tolerance = 1e-06)
expect_equal(as.array(cur_images[[3]])[1, 1:10,"H3"],
imageData(pancreasImages[[3]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[3]])[1, 1:10,"CD99"],
imageData(pancreasImages[[3]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[3]])[1, 1:10,"PIN"],
imageData(pancreasImages[[3]])[1, 1:10,"PIN"]/max(imageData(pancreasImages[[1]])[,,"PIN"]),
tolerance = 1e-06)
# Not Separate images
expect_silent(cur_images <- normalize(cur_Images, separateImages = FALSE,
inputRange = list(H3 = c(0,50), CD99 = c(0,70))))
expect_silent(plotPixels(cur_Images,
colour_by = c("H3", "CD99", "PIN")))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99", "PIN")))
expect_equal(as.array(cur_images[[1]])[1, 1:10,"H3"],
imageData(pancreasImages[[1]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[1]])[1, 1:10,"CD99"],
imageData(pancreasImages[[1]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
cur_max <- max(c(max(imageData(pancreasImages[[1]])[,,"PIN"]),
max(imageData(pancreasImages[[2]])[,,"PIN"]),
max(imageData(pancreasImages[[3]])[,,"PIN"])))
expect_equal(as.array(cur_images[[1]])[1, 1:10,"PIN"],
imageData(pancreasImages[[1]])[1, 1:10,"PIN"]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,"H3"],
imageData(pancreasImages[[2]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,"CD99"],
imageData(pancreasImages[[2]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[2]])[1, 1:10,"PIN"],
imageData(pancreasImages[[2]])[1, 1:10,"PIN"]/cur_max,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[3]])[1, 1:10,"H3"],
imageData(pancreasImages[[3]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[3]])[1, 1:10,"CD99"],
imageData(pancreasImages[[3]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
expect_equal(as.array(cur_images[[3]])[1, 1:10,"PIN"],
imageData(pancreasImages[[3]])[1, 1:10,"PIN"]/cur_max,
tolerance = 1e-06)
# Error
expect_error(normalize(cur_Images, separateChannels = "test"),
regexp = "'separateChannels' only takes TRUE or FALSE.")
expect_error(normalize(cur_Images, separateImages = "test"),
regexp = "'separateImages' only takes TRUE or FALSE.")
expect_error(normalize(cur_Images, inputRange = "test"),
regexp = "'inputRange' takes a vector of length 2, a list or NULL.")
expect_error(normalize(cur_Images, inputRange = 2),
regexp = "'inputRange' takes a vector of length 2, a list or NULL.")
cur_images <- cur_Images
channelNames(cur_images) <- NULL
expect_error(normalize(cur_images, inputRange = list(H3 = c(0, 100), CDH = c(0, 20))),
regexp = "Please set the 'channelNames' of the CytoImageList object.")
expect_error(normalize(pancreasImages, inputRange = list(test = c(0, 100), CDH = c(0, 20))),
regexp = "The names of 'inputRange' should correspond to the'channelNames' of the CytoImageList object.")
# Check if overwriting works
expect_silent(cur_images <- normalize(cur_Images, overwrite = TRUE))
expect_s4_class(cur_images$E34_imc, "DelayedArray")
expect_s4_class(cur_images$G01_imc, "DelayedArray")
expect_s4_class(cur_images$J02_imc, "DelayedArray")
# Check if second layer was created
expect_true("E34_imc_norm" %in% h5ls(path(cur_images$E34_imc@seed))$name)
expect_true(".E34_imc_norm_dimnames" %in% h5ls(path(cur_images$E34_imc@seed))$name)
expect_false("E34_imc" %in% h5ls(path(cur_images$E34_imc@seed))$name)
expect_false(".E34_imc_dimnames" %in% h5ls(path(cur_images$E34_imc@seed))$name)
})
test_that("On disk: HDF5 handling works", {
cur_array <- array(data = rnorm(3000), dim = c(100, 100, 3))
cur_array_2 <- array(data = runif(3000), dim = c(100, 100, 3))
cur_path <- tempdir()
on.exit(unlink(cur_path))
cur_file <- paste0(cur_path, "/test.h5")
cur_hdf5 <- writeHDF5Array(x = cur_array, filepath = cur_file, name = "test")
expect_equal(h5ls(cur_file)$name, "test")
expect_silent(test <- .add_h5(cur_obj = cur_hdf5, new_obj = cur_array_2,
overwrite = FALSE, suffix = "_norm"))
expect_equal(h5ls(cur_file)$name, c("test", "test_norm"))
expect_equal(as.array(test), cur_array_2)
expect_silent(test <- .add_h5(cur_obj = cur_hdf5, new_obj = cur_array_2,
overwrite = TRUE, suffix = "_norm"))
expect_equal(h5ls(cur_file)$name, "test_norm")
expect_equal(as.array(test), cur_array_2)
})
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