tests/testthat/test_CytoImageList-manipulation.R
In BodenmillerGroup/SingleCellMapper: Visualization of highly multiplexed imaging data in R
test_that("CytoImageList can be scaled.", {
data("pancreasImages")
# Works - single numeric
expect_silent(cur_images <- scaleImages(pancreasImages, 1))
expect_identical(imageData(cur_images[[1]]), imageData(pancreasImages[[1]]))
expect_equal(imageData(cur_images[[1]]),
imageData(pancreasImages[[1]]))
expect_silent(cur_images <- scaleImages(pancreasImages, 2))
expect_equal(imageData(cur_images[[1]]),
imageData(pancreasImages[[1]]) * 2)
expect_silent(plotPixels(cur_images))
image.list <- list.files(system.file("extdata", package = "cytomapper"),
pattern = "mask.tiff", full.names = TRUE)
cur_images <- loadImages(image.list)
expect_equal(imageData(cur_images[[1]])[11, 1:2],
c(0.01257343, 0.01257343))
expect_error(plotCells(cur_images),
regexp = "Segmentation masks must only contain integer values.",
fixed = TRUE)
expect_silent(cur_images <- scaleImages(cur_images, (2^16)-1))
expect_equal(imageData(cur_images[[1]])[11, 1:2],
c(824, 824))
expect_silent(plotCells(cur_images))
# Works - numeric vector
expect_silent(cur_images <- scaleImages(pancreasImages, c(1, 1, 1)))
expect_identical(imageData(cur_images[[1]]), imageData(pancreasImages[[1]]))
expect_identical(imageData(cur_images[[2]]), imageData(pancreasImages[[2]]))
expect_identical(imageData(cur_images[[3]]), imageData(pancreasImages[[3]]))
expect_silent(cur_images <- scaleImages(cur_images, c(2, 3, 4)))
expect_identical(imageData(cur_images[[1]]),
imageData(pancreasImages[[1]]) * 2)
expect_identical(imageData(cur_images[[2]]),
imageData(pancreasImages[[2]]) * 3)
expect_identical(imageData(cur_images[[3]]),
imageData(pancreasImages[[3]]) * 4)
expect_silent(plotPixels(cur_images))
# Error
expect_error(scaleImages(pancreasImages, c(1,2)),
regexp = "'value' must either be a single numeric or of the same length as 'object'.",
fixed = TRUE)
expect_error(scaleImages(pancreasImages, "test"),
regexp = "'value' must be numeric.",
fixed = TRUE)
expect_error(scaleImages(pancreasImages, c(1, "test")),
regexp = "'value' must be numeric.",
fixed = TRUE)
expect_error(scaleImages(pancreasImages, c(1, 2, "test")),
regexp = "'value' must be numeric.",
fixed = TRUE)
expect_error(scaleImages(pancreasImages, c(1, 2, 3, 4)),
regexp = "'value' must either be a single numeric or of the same length as 'object'.",
fixed = TRUE)
})
test_that("CytoImageList can be normalized", {
data("pancreasImages")
# Works
expect_silent(cur_images <- normalize(pancreasImages))
expect_silent(plotPixels(cur_images))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99")))
# Separate images
# Separate channels
expect_silent(cur_images <- normalize(pancreasImages, separateImages = TRUE,
separateChannels = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99")))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_equal(imageData(cur_images[[1]])[1, 1:10,1],
imageData(pancreasImages[[1]])[1, 1:10,1]/max(imageData(pancreasImages[[1]])[,,1]),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,2],
imageData(pancreasImages[[1]])[1, 1:10,2]/max(imageData(pancreasImages[[1]])[,,2]),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,3],
imageData(pancreasImages[[1]])[1, 1:10,3]/max(imageData(pancreasImages[[1]])[,,3]),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,4],
imageData(pancreasImages[[1]])[1, 1:10,4]/max(imageData(pancreasImages[[1]])[,,4]),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,5],
imageData(pancreasImages[[1]])[1, 1:10,5]/max(imageData(pancreasImages[[1]])[,,5]),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,1],
imageData(pancreasImages[[2]])[1, 1:10,1]/max(imageData(pancreasImages[[2]])[,,1]),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,2],
imageData(pancreasImages[[2]])[1, 1:10,2]/max(imageData(pancreasImages[[2]])[,,2]),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,3],
imageData(pancreasImages[[2]])[1, 1:10,3]/max(imageData(pancreasImages[[2]])[,,3]),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,4],
imageData(pancreasImages[[2]])[1, 1:10,4]/max(imageData(pancreasImages[[2]])[,,4]),
tolerance = 1e-06)
expect_equal(imageData(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(imageData(cur_images[[1]])[,,1]),
1, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,2]),
1, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,3]),
1, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,4]),
1, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,5]),
1, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,1]),
1, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,2]),
1, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,3]),
1, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,4]),
1, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,5]),
1, tolerance = 1e-06)
# Separate images
# Not Separate channels
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_images,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_equal(imageData(cur_images[[1]])[1, 1:10,1],
imageData(pancreasImages[[1]])[1, 1:10,1]/max(imageData(pancreasImages[[1]])),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,2],
imageData(pancreasImages[[1]])[1, 1:10,2]/max(imageData(pancreasImages[[1]])),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,3],
imageData(pancreasImages[[1]])[1, 1:10,3]/max(imageData(pancreasImages[[1]])),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,4],
imageData(pancreasImages[[1]])[1, 1:10,4]/max(imageData(pancreasImages[[1]])),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,5],
imageData(pancreasImages[[1]])[1, 1:10,5]/max(imageData(pancreasImages[[1]])),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,1],
imageData(pancreasImages[[2]])[1, 1:10,1]/max(imageData(pancreasImages[[2]])),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,2],
imageData(pancreasImages[[2]])[1, 1:10,2]/max(imageData(pancreasImages[[2]])),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,3],
imageData(pancreasImages[[2]])[1, 1:10,3]/max(imageData(pancreasImages[[2]])),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,4],
imageData(pancreasImages[[2]])[1, 1:10,4]/max(imageData(pancreasImages[[2]])),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,5],
imageData(pancreasImages[[2]])[1, 1:10,5]/max(imageData(pancreasImages[[2]])),
tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,1]),
max(imageData(pancreasImages[[1]])[,,1])/max(imageData(pancreasImages[[1]])), tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,2]),
max(imageData(pancreasImages[[1]])[,,2])/max(imageData(pancreasImages[[1]])), tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,3]),
max(imageData(pancreasImages[[1]])[,,3])/max(imageData(pancreasImages[[1]])), tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,4]),
max(imageData(pancreasImages[[1]])[,,4])/max(imageData(pancreasImages[[1]])), tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,5]),
max(imageData(pancreasImages[[1]])[,,5])/max(imageData(pancreasImages[[1]])), tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,1]),
max(imageData(pancreasImages[[2]])[,,1])/max(imageData(pancreasImages[[2]])), tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,2]),
max(imageData(pancreasImages[[2]])[,,2])/max(imageData(pancreasImages[[2]])), tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,3]),
max(imageData(pancreasImages[[2]])[,,3])/max(imageData(pancreasImages[[2]])), tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,4]),
max(imageData(pancreasImages[[2]])[,,4])/max(imageData(pancreasImages[[2]])), tolerance = 1e-06)
expect_equal(max(imageData(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(pancreasImages, 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(imageData(cur_images[[1]])[1, 1:10,1],
imageData(pancreasImages[[1]])[1, 1:10,1]/cur_max[1],
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,2],
imageData(pancreasImages[[1]])[1, 1:10,2]/cur_max[2],
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,3],
imageData(pancreasImages[[1]])[1, 1:10,3]/cur_max[3],
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,4],
imageData(pancreasImages[[1]])[1, 1:10,4]/cur_max[4],
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,5],
imageData(pancreasImages[[1]])[1, 1:10,5]/cur_max[5],
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,1],
imageData(pancreasImages[[2]])[1, 1:10,1]/cur_max[1],
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,2],
imageData(pancreasImages[[2]])[1, 1:10,2]/cur_max[2],
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,3],
imageData(pancreasImages[[2]])[1, 1:10,3]/cur_max[3],
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,4],
imageData(pancreasImages[[2]])[1, 1:10,4]/cur_max[4],
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,5],
imageData(pancreasImages[[2]])[1, 1:10,5]/cur_max[5],
tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,1]),
max(imageData(pancreasImages[[1]])[,,1])/cur_max[1], tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,2]),
max(imageData(pancreasImages[[1]])[,,2])/cur_max[2], tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,3]),
max(imageData(pancreasImages[[1]])[,,3])/cur_max[3], tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,4]),
max(imageData(pancreasImages[[1]])[,,4])/cur_max[4], tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,5]),
max(imageData(pancreasImages[[1]])[,,5])/cur_max[5], tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,1]),
max(imageData(pancreasImages[[2]])[,,1])/cur_max[1], tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,2]),
max(imageData(pancreasImages[[2]])[,,2])/cur_max[2], tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,3]),
max(imageData(pancreasImages[[2]])[,,3])/cur_max[3], tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,4]),
max(imageData(pancreasImages[[2]])[,,4])/cur_max[4], tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,5]),
max(imageData(pancreasImages[[2]])[,,5])/cur_max[5], tolerance = 1e-06)
# Single frame
cur_img1 <- pancreasImages[[1]][,,1]
cur_img2 <- pancreasImages[[2]][,,1]
cur_img <- CytoImageList(cur_img1, cur_img2)
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(imageData(cur_images[[1]])[,,1]),
max(imageData(pancreasImages[[1]])[,,1])/cur_max, tolerance = 1e-06)
expect_equal(max(imageData(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(pancreasImages, 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(imageData(cur_images[[1]])[1, 1:10,1],
imageData(pancreasImages[[1]])[1, 1:10,1]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,2],
imageData(pancreasImages[[1]])[1, 1:10,2]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,3],
imageData(pancreasImages[[1]])[1, 1:10,3]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,4],
imageData(pancreasImages[[1]])[1, 1:10,4]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,5],
imageData(pancreasImages[[1]])[1, 1:10,5]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,1],
imageData(pancreasImages[[2]])[1, 1:10,1]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,2],
imageData(pancreasImages[[2]])[1, 1:10,2]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,3],
imageData(pancreasImages[[2]])[1, 1:10,3]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,4],
imageData(pancreasImages[[2]])[1, 1:10,4]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,5],
imageData(pancreasImages[[2]])[1, 1:10,5]/cur_max,
tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,1]),
max(imageData(pancreasImages[[1]])[,,1])/cur_max, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,2]),
max(imageData(pancreasImages[[1]])[,,2])/cur_max, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,3]),
max(imageData(pancreasImages[[1]])[,,3])/cur_max, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,4]),
max(imageData(pancreasImages[[1]])[,,4])/cur_max, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,5]),
max(imageData(pancreasImages[[1]])[,,5])/cur_max, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,1]),
max(imageData(pancreasImages[[2]])[,,1])/cur_max, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,2]),
max(imageData(pancreasImages[[2]])[,,2])/cur_max, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,3]),
max(imageData(pancreasImages[[2]])[,,3])/cur_max, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,4]),
max(imageData(pancreasImages[[2]])[,,4])/cur_max, tolerance = 1e-06)
expect_equal(max(imageData(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(pancreasImages, separateImages = TRUE,
separateChannels = TRUE))
expect_silent(cur_images2 <- normalize(cur_images, separateImages = TRUE,
separateChannels = TRUE, inputRange = c(0, 0.9)))
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(imageData(cur_images2[[1]])[1, 1:10,1],
imageData(cur_images[[1]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,2],
imageData(cur_images[[1]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,3],
imageData(cur_images[[1]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,4],
imageData(cur_images[[1]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,5],
imageData(cur_images[[1]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,1],
imageData(cur_images[[2]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,2],
imageData(cur_images[[2]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,3],
imageData(cur_images[[2]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,4],
imageData(cur_images[[2]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(imageData(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(pancreasImages, separateImages = TRUE,
separateChannels = FALSE))
expect_silent(cur_images2 <- normalize(cur_images, separateImages = TRUE,
separateChannels = FALSE, inputRange = c(0, 0.9)))
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(imageData(cur_images2[[1]])[1, 1:10,1],
imageData(cur_images[[1]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,2],
imageData(cur_images[[1]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,3],
imageData(cur_images[[1]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,4],
imageData(cur_images[[1]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,5],
imageData(cur_images[[1]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,1],
imageData(cur_images[[2]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,2],
imageData(cur_images[[2]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,3],
imageData(cur_images[[2]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,4],
imageData(cur_images[[2]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(imageData(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(pancreasImages, separateImages = FALSE,
separateChannels = TRUE))
expect_silent(cur_images2 <- normalize(cur_images, separateImages = FALSE,
separateChannels = TRUE, inputRange = c(0, 0.9)))
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(imageData(cur_images2[[1]])[1, 1:10,1],
imageData(cur_images[[1]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,2],
imageData(cur_images[[1]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,3],
imageData(cur_images[[1]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,4],
imageData(cur_images[[1]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,5],
imageData(cur_images[[1]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,1],
imageData(cur_images[[2]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,2],
imageData(cur_images[[2]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,3],
imageData(cur_images[[2]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,4],
imageData(cur_images[[2]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(imageData(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(pancreasImages, separateImages = TRUE,
separateChannels = FALSE))
expect_silent(cur_images2 <- normalize(cur_images, separateImages = TRUE,
separateChannels = FALSE, inputRange = c(0, 0.9)))
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(imageData(cur_images2[[1]])[1, 1:10,1],
imageData(cur_images[[1]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,2],
imageData(cur_images[[1]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,3],
imageData(cur_images[[1]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,4],
imageData(cur_images[[1]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,5],
imageData(cur_images[[1]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,1],
imageData(cur_images[[2]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,2],
imageData(cur_images[[2]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,3],
imageData(cur_images[[2]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,4],
imageData(cur_images[[2]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(imageData(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(pancreasImages, separateImages = FALSE,
separateChannels = FALSE))
expect_silent(cur_images2 <- normalize(cur_images, separateImages = FALSE,
separateChannels = FALSE, inputRange = c(0, 0.9)))
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(imageData(cur_images2[[1]])[1, 1:10,1],
imageData(cur_images[[1]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,2],
imageData(cur_images[[1]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,3],
imageData(cur_images[[1]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,4],
imageData(cur_images[[1]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,5],
imageData(cur_images[[1]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,1],
imageData(cur_images[[2]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,2],
imageData(cur_images[[2]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,3],
imageData(cur_images[[2]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,4],
imageData(cur_images[[2]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(imageData(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(pancreasImages, 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(pancreasImages, 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(pancreasImages, 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(pancreasImages, 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(pancreasImages, separateImages = TRUE,
inputRange = list(H3 = c(0,50), CD99 = c(0,70))))
expect_silent(plotPixels(pancreasImages,
colour_by = c("H3", "CD99", "PIN")))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99", "PIN")))
expect_equal(imageData(cur_images[[1]])[1, 1:10,"H3"],
imageData(pancreasImages[[1]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,"CD99"],
imageData(pancreasImages[[1]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,"PIN"],
imageData(pancreasImages[[1]])[1, 1:10,"PIN"]/max(imageData(pancreasImages[[1]])[,,"PIN"]),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,"H3"],
imageData(pancreasImages[[2]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,"CD99"],
imageData(pancreasImages[[2]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,"PIN"],
imageData(pancreasImages[[2]])[1, 1:10,"PIN"]/max(imageData(pancreasImages[[1]])[,,"PIN"]),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[3]])[1, 1:10,"H3"],
imageData(pancreasImages[[3]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[3]])[1, 1:10,"CD99"],
imageData(pancreasImages[[3]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
expect_equal(imageData(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(pancreasImages, separateImages = FALSE,
inputRange = list(H3 = c(0,50), CD99 = c(0,70))))
expect_silent(plotPixels(pancreasImages,
colour_by = c("H3", "CD99", "PIN")))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99", "PIN")))
expect_equal(imageData(cur_images[[1]])[1, 1:10,"H3"],
imageData(pancreasImages[[1]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(imageData(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(imageData(cur_images[[1]])[1, 1:10,"PIN"],
imageData(pancreasImages[[1]])[1, 1:10,"PIN"]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,"H3"],
imageData(pancreasImages[[2]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,"CD99"],
imageData(pancreasImages[[2]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,"PIN"],
imageData(pancreasImages[[2]])[1, 1:10,"PIN"]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[3]])[1, 1:10,"H3"],
imageData(pancreasImages[[3]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[3]])[1, 1:10,"CD99"],
imageData(pancreasImages[[3]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[3]])[1, 1:10,"PIN"],
imageData(pancreasImages[[3]])[1, 1:10,"PIN"]/cur_max,
tolerance = 1e-06)
# Error
expect_error(normalize(pancreasImages, separateChannels = "test"),
regexp = "'separateChannels' only takes TRUE or FALSE.")
expect_error(normalize(pancreasImages, separateImages = "test"),
regexp = "'separateImages' only takes TRUE or FALSE.")
expect_error(normalize(pancreasImages, inputRange = "test"),
regexp = "'inputRange' takes a vector of length 2, a list or NULL.")
expect_error(normalize(pancreasImages, inputRange = 2),
regexp = "'inputRange' takes a vector of length 2, a list or NULL.")
cur_images <- pancreasImages
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.")
})
BodenmillerGroup/SingleCellMapper documentation built on July 2, 2024, 4:31 p.m.
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test_that("CytoImageList can be scaled.", {
data("pancreasImages")
# Works - single numeric
expect_silent(cur_images <- scaleImages(pancreasImages, 1))
expect_identical(imageData(cur_images[[1]]), imageData(pancreasImages[[1]]))
expect_equal(imageData(cur_images[[1]]),
imageData(pancreasImages[[1]]))
expect_silent(cur_images <- scaleImages(pancreasImages, 2))
expect_equal(imageData(cur_images[[1]]),
imageData(pancreasImages[[1]]) * 2)
expect_silent(plotPixels(cur_images))
image.list <- list.files(system.file("extdata", package = "cytomapper"),
pattern = "mask.tiff", full.names = TRUE)
cur_images <- loadImages(image.list)
expect_equal(imageData(cur_images[[1]])[11, 1:2],
c(0.01257343, 0.01257343))
expect_error(plotCells(cur_images),
regexp = "Segmentation masks must only contain integer values.",
fixed = TRUE)
expect_silent(cur_images <- scaleImages(cur_images, (2^16)-1))
expect_equal(imageData(cur_images[[1]])[11, 1:2],
c(824, 824))
expect_silent(plotCells(cur_images))
# Works - numeric vector
expect_silent(cur_images <- scaleImages(pancreasImages, c(1, 1, 1)))
expect_identical(imageData(cur_images[[1]]), imageData(pancreasImages[[1]]))
expect_identical(imageData(cur_images[[2]]), imageData(pancreasImages[[2]]))
expect_identical(imageData(cur_images[[3]]), imageData(pancreasImages[[3]]))
expect_silent(cur_images <- scaleImages(cur_images, c(2, 3, 4)))
expect_identical(imageData(cur_images[[1]]),
imageData(pancreasImages[[1]]) * 2)
expect_identical(imageData(cur_images[[2]]),
imageData(pancreasImages[[2]]) * 3)
expect_identical(imageData(cur_images[[3]]),
imageData(pancreasImages[[3]]) * 4)
expect_silent(plotPixels(cur_images))
# Error
expect_error(scaleImages(pancreasImages, c(1,2)),
regexp = "'value' must either be a single numeric or of the same length as 'object'.",
fixed = TRUE)
expect_error(scaleImages(pancreasImages, "test"),
regexp = "'value' must be numeric.",
fixed = TRUE)
expect_error(scaleImages(pancreasImages, c(1, "test")),
regexp = "'value' must be numeric.",
fixed = TRUE)
expect_error(scaleImages(pancreasImages, c(1, 2, "test")),
regexp = "'value' must be numeric.",
fixed = TRUE)
expect_error(scaleImages(pancreasImages, c(1, 2, 3, 4)),
regexp = "'value' must either be a single numeric or of the same length as 'object'.",
fixed = TRUE)
})
test_that("CytoImageList can be normalized", {
data("pancreasImages")
# Works
expect_silent(cur_images <- normalize(pancreasImages))
expect_silent(plotPixels(cur_images))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99")))
# Separate images
# Separate channels
expect_silent(cur_images <- normalize(pancreasImages, separateImages = TRUE,
separateChannels = TRUE))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99")))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_equal(imageData(cur_images[[1]])[1, 1:10,1],
imageData(pancreasImages[[1]])[1, 1:10,1]/max(imageData(pancreasImages[[1]])[,,1]),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,2],
imageData(pancreasImages[[1]])[1, 1:10,2]/max(imageData(pancreasImages[[1]])[,,2]),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,3],
imageData(pancreasImages[[1]])[1, 1:10,3]/max(imageData(pancreasImages[[1]])[,,3]),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,4],
imageData(pancreasImages[[1]])[1, 1:10,4]/max(imageData(pancreasImages[[1]])[,,4]),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,5],
imageData(pancreasImages[[1]])[1, 1:10,5]/max(imageData(pancreasImages[[1]])[,,5]),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,1],
imageData(pancreasImages[[2]])[1, 1:10,1]/max(imageData(pancreasImages[[2]])[,,1]),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,2],
imageData(pancreasImages[[2]])[1, 1:10,2]/max(imageData(pancreasImages[[2]])[,,2]),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,3],
imageData(pancreasImages[[2]])[1, 1:10,3]/max(imageData(pancreasImages[[2]])[,,3]),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,4],
imageData(pancreasImages[[2]])[1, 1:10,4]/max(imageData(pancreasImages[[2]])[,,4]),
tolerance = 1e-06)
expect_equal(imageData(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(imageData(cur_images[[1]])[,,1]),
1, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,2]),
1, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,3]),
1, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,4]),
1, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,5]),
1, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,1]),
1, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,2]),
1, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,3]),
1, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,4]),
1, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,5]),
1, tolerance = 1e-06)
# Separate images
# Not Separate channels
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_images,
colour_by = c("H3", "CD99"), scale = FALSE))
expect_equal(imageData(cur_images[[1]])[1, 1:10,1],
imageData(pancreasImages[[1]])[1, 1:10,1]/max(imageData(pancreasImages[[1]])),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,2],
imageData(pancreasImages[[1]])[1, 1:10,2]/max(imageData(pancreasImages[[1]])),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,3],
imageData(pancreasImages[[1]])[1, 1:10,3]/max(imageData(pancreasImages[[1]])),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,4],
imageData(pancreasImages[[1]])[1, 1:10,4]/max(imageData(pancreasImages[[1]])),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,5],
imageData(pancreasImages[[1]])[1, 1:10,5]/max(imageData(pancreasImages[[1]])),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,1],
imageData(pancreasImages[[2]])[1, 1:10,1]/max(imageData(pancreasImages[[2]])),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,2],
imageData(pancreasImages[[2]])[1, 1:10,2]/max(imageData(pancreasImages[[2]])),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,3],
imageData(pancreasImages[[2]])[1, 1:10,3]/max(imageData(pancreasImages[[2]])),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,4],
imageData(pancreasImages[[2]])[1, 1:10,4]/max(imageData(pancreasImages[[2]])),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,5],
imageData(pancreasImages[[2]])[1, 1:10,5]/max(imageData(pancreasImages[[2]])),
tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,1]),
max(imageData(pancreasImages[[1]])[,,1])/max(imageData(pancreasImages[[1]])), tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,2]),
max(imageData(pancreasImages[[1]])[,,2])/max(imageData(pancreasImages[[1]])), tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,3]),
max(imageData(pancreasImages[[1]])[,,3])/max(imageData(pancreasImages[[1]])), tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,4]),
max(imageData(pancreasImages[[1]])[,,4])/max(imageData(pancreasImages[[1]])), tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,5]),
max(imageData(pancreasImages[[1]])[,,5])/max(imageData(pancreasImages[[1]])), tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,1]),
max(imageData(pancreasImages[[2]])[,,1])/max(imageData(pancreasImages[[2]])), tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,2]),
max(imageData(pancreasImages[[2]])[,,2])/max(imageData(pancreasImages[[2]])), tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,3]),
max(imageData(pancreasImages[[2]])[,,3])/max(imageData(pancreasImages[[2]])), tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,4]),
max(imageData(pancreasImages[[2]])[,,4])/max(imageData(pancreasImages[[2]])), tolerance = 1e-06)
expect_equal(max(imageData(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(pancreasImages, 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(imageData(cur_images[[1]])[1, 1:10,1],
imageData(pancreasImages[[1]])[1, 1:10,1]/cur_max[1],
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,2],
imageData(pancreasImages[[1]])[1, 1:10,2]/cur_max[2],
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,3],
imageData(pancreasImages[[1]])[1, 1:10,3]/cur_max[3],
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,4],
imageData(pancreasImages[[1]])[1, 1:10,4]/cur_max[4],
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,5],
imageData(pancreasImages[[1]])[1, 1:10,5]/cur_max[5],
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,1],
imageData(pancreasImages[[2]])[1, 1:10,1]/cur_max[1],
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,2],
imageData(pancreasImages[[2]])[1, 1:10,2]/cur_max[2],
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,3],
imageData(pancreasImages[[2]])[1, 1:10,3]/cur_max[3],
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,4],
imageData(pancreasImages[[2]])[1, 1:10,4]/cur_max[4],
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,5],
imageData(pancreasImages[[2]])[1, 1:10,5]/cur_max[5],
tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,1]),
max(imageData(pancreasImages[[1]])[,,1])/cur_max[1], tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,2]),
max(imageData(pancreasImages[[1]])[,,2])/cur_max[2], tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,3]),
max(imageData(pancreasImages[[1]])[,,3])/cur_max[3], tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,4]),
max(imageData(pancreasImages[[1]])[,,4])/cur_max[4], tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,5]),
max(imageData(pancreasImages[[1]])[,,5])/cur_max[5], tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,1]),
max(imageData(pancreasImages[[2]])[,,1])/cur_max[1], tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,2]),
max(imageData(pancreasImages[[2]])[,,2])/cur_max[2], tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,3]),
max(imageData(pancreasImages[[2]])[,,3])/cur_max[3], tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,4]),
max(imageData(pancreasImages[[2]])[,,4])/cur_max[4], tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,5]),
max(imageData(pancreasImages[[2]])[,,5])/cur_max[5], tolerance = 1e-06)
# Single frame
cur_img1 <- pancreasImages[[1]][,,1]
cur_img2 <- pancreasImages[[2]][,,1]
cur_img <- CytoImageList(cur_img1, cur_img2)
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(imageData(cur_images[[1]])[,,1]),
max(imageData(pancreasImages[[1]])[,,1])/cur_max, tolerance = 1e-06)
expect_equal(max(imageData(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(pancreasImages, 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(imageData(cur_images[[1]])[1, 1:10,1],
imageData(pancreasImages[[1]])[1, 1:10,1]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,2],
imageData(pancreasImages[[1]])[1, 1:10,2]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,3],
imageData(pancreasImages[[1]])[1, 1:10,3]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,4],
imageData(pancreasImages[[1]])[1, 1:10,4]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,5],
imageData(pancreasImages[[1]])[1, 1:10,5]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,1],
imageData(pancreasImages[[2]])[1, 1:10,1]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,2],
imageData(pancreasImages[[2]])[1, 1:10,2]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,3],
imageData(pancreasImages[[2]])[1, 1:10,3]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,4],
imageData(pancreasImages[[2]])[1, 1:10,4]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,5],
imageData(pancreasImages[[2]])[1, 1:10,5]/cur_max,
tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,1]),
max(imageData(pancreasImages[[1]])[,,1])/cur_max, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,2]),
max(imageData(pancreasImages[[1]])[,,2])/cur_max, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,3]),
max(imageData(pancreasImages[[1]])[,,3])/cur_max, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,4]),
max(imageData(pancreasImages[[1]])[,,4])/cur_max, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[1]])[,,5]),
max(imageData(pancreasImages[[1]])[,,5])/cur_max, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,1]),
max(imageData(pancreasImages[[2]])[,,1])/cur_max, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,2]),
max(imageData(pancreasImages[[2]])[,,2])/cur_max, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,3]),
max(imageData(pancreasImages[[2]])[,,3])/cur_max, tolerance = 1e-06)
expect_equal(max(imageData(cur_images[[2]])[,,4]),
max(imageData(pancreasImages[[2]])[,,4])/cur_max, tolerance = 1e-06)
expect_equal(max(imageData(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(pancreasImages, separateImages = TRUE,
separateChannels = TRUE))
expect_silent(cur_images2 <- normalize(cur_images, separateImages = TRUE,
separateChannels = TRUE, inputRange = c(0, 0.9)))
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(imageData(cur_images2[[1]])[1, 1:10,1],
imageData(cur_images[[1]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,2],
imageData(cur_images[[1]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,3],
imageData(cur_images[[1]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,4],
imageData(cur_images[[1]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,5],
imageData(cur_images[[1]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,1],
imageData(cur_images[[2]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,2],
imageData(cur_images[[2]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,3],
imageData(cur_images[[2]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,4],
imageData(cur_images[[2]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(imageData(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(pancreasImages, separateImages = TRUE,
separateChannels = FALSE))
expect_silent(cur_images2 <- normalize(cur_images, separateImages = TRUE,
separateChannels = FALSE, inputRange = c(0, 0.9)))
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(imageData(cur_images2[[1]])[1, 1:10,1],
imageData(cur_images[[1]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,2],
imageData(cur_images[[1]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,3],
imageData(cur_images[[1]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,4],
imageData(cur_images[[1]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,5],
imageData(cur_images[[1]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,1],
imageData(cur_images[[2]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,2],
imageData(cur_images[[2]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,3],
imageData(cur_images[[2]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,4],
imageData(cur_images[[2]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(imageData(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(pancreasImages, separateImages = FALSE,
separateChannels = TRUE))
expect_silent(cur_images2 <- normalize(cur_images, separateImages = FALSE,
separateChannels = TRUE, inputRange = c(0, 0.9)))
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(imageData(cur_images2[[1]])[1, 1:10,1],
imageData(cur_images[[1]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,2],
imageData(cur_images[[1]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,3],
imageData(cur_images[[1]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,4],
imageData(cur_images[[1]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,5],
imageData(cur_images[[1]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,1],
imageData(cur_images[[2]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,2],
imageData(cur_images[[2]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,3],
imageData(cur_images[[2]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,4],
imageData(cur_images[[2]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(imageData(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(pancreasImages, separateImages = TRUE,
separateChannels = FALSE))
expect_silent(cur_images2 <- normalize(cur_images, separateImages = TRUE,
separateChannels = FALSE, inputRange = c(0, 0.9)))
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(imageData(cur_images2[[1]])[1, 1:10,1],
imageData(cur_images[[1]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,2],
imageData(cur_images[[1]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,3],
imageData(cur_images[[1]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,4],
imageData(cur_images[[1]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,5],
imageData(cur_images[[1]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,1],
imageData(cur_images[[2]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,2],
imageData(cur_images[[2]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,3],
imageData(cur_images[[2]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,4],
imageData(cur_images[[2]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(imageData(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(pancreasImages, separateImages = FALSE,
separateChannels = FALSE))
expect_silent(cur_images2 <- normalize(cur_images, separateImages = FALSE,
separateChannels = FALSE, inputRange = c(0, 0.9)))
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(imageData(cur_images2[[1]])[1, 1:10,1],
imageData(cur_images[[1]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,2],
imageData(cur_images[[1]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,3],
imageData(cur_images[[1]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,4],
imageData(cur_images[[1]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[1]])[1, 1:10,5],
imageData(cur_images[[1]])[1, 1:10,5]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,1],
imageData(cur_images[[2]])[1, 1:10,1]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,2],
imageData(cur_images[[2]])[1, 1:10,2]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,3],
imageData(cur_images[[2]])[1, 1:10,3]/0.9,
tolerance = 1e-06)
expect_equal(imageData(cur_images2[[2]])[1, 1:10,4],
imageData(cur_images[[2]])[1, 1:10,4]/0.9,
tolerance = 1e-06)
expect_equal(imageData(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(pancreasImages, 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(pancreasImages, 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(pancreasImages, 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(pancreasImages, 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(pancreasImages, separateImages = TRUE,
inputRange = list(H3 = c(0,50), CD99 = c(0,70))))
expect_silent(plotPixels(pancreasImages,
colour_by = c("H3", "CD99", "PIN")))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99", "PIN")))
expect_equal(imageData(cur_images[[1]])[1, 1:10,"H3"],
imageData(pancreasImages[[1]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,"CD99"],
imageData(pancreasImages[[1]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[1]])[1, 1:10,"PIN"],
imageData(pancreasImages[[1]])[1, 1:10,"PIN"]/max(imageData(pancreasImages[[1]])[,,"PIN"]),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,"H3"],
imageData(pancreasImages[[2]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,"CD99"],
imageData(pancreasImages[[2]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,"PIN"],
imageData(pancreasImages[[2]])[1, 1:10,"PIN"]/max(imageData(pancreasImages[[1]])[,,"PIN"]),
tolerance = 1e-06)
expect_equal(imageData(cur_images[[3]])[1, 1:10,"H3"],
imageData(pancreasImages[[3]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[3]])[1, 1:10,"CD99"],
imageData(pancreasImages[[3]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
expect_equal(imageData(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(pancreasImages, separateImages = FALSE,
inputRange = list(H3 = c(0,50), CD99 = c(0,70))))
expect_silent(plotPixels(pancreasImages,
colour_by = c("H3", "CD99", "PIN")))
expect_silent(plotPixels(cur_images,
colour_by = c("H3", "CD99", "PIN")))
expect_equal(imageData(cur_images[[1]])[1, 1:10,"H3"],
imageData(pancreasImages[[1]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(imageData(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(imageData(cur_images[[1]])[1, 1:10,"PIN"],
imageData(pancreasImages[[1]])[1, 1:10,"PIN"]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,"H3"],
imageData(pancreasImages[[2]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,"CD99"],
imageData(pancreasImages[[2]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[2]])[1, 1:10,"PIN"],
imageData(pancreasImages[[2]])[1, 1:10,"PIN"]/cur_max,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[3]])[1, 1:10,"H3"],
imageData(pancreasImages[[3]])[1, 1:10,"H3"]/50,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[3]])[1, 1:10,"CD99"],
imageData(pancreasImages[[3]])[1, 1:10,"CD99"]/70,
tolerance = 1e-06)
expect_equal(imageData(cur_images[[3]])[1, 1:10,"PIN"],
imageData(pancreasImages[[3]])[1, 1:10,"PIN"]/cur_max,
tolerance = 1e-06)
# Error
expect_error(normalize(pancreasImages, separateChannels = "test"),
regexp = "'separateChannels' only takes TRUE or FALSE.")
expect_error(normalize(pancreasImages, separateImages = "test"),
regexp = "'separateImages' only takes TRUE or FALSE.")
expect_error(normalize(pancreasImages, inputRange = "test"),
regexp = "'inputRange' takes a vector of length 2, a list or NULL.")
expect_error(normalize(pancreasImages, inputRange = 2),
regexp = "'inputRange' takes a vector of length 2, a list or NULL.")
cur_images <- pancreasImages
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.")
})
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