tests/testthat/test_compImage.R
In BodenmillerGroup/cytomapper: Visualization of highly multiplexed imaging data in R
test_that("compImage function works", {
data("pancreasImages")
cur_path <- tempdir()
on.exit(unlink(cur_path))
# Mock up realistic spillover matrix
sm_real <- matrix(c(1, 0.033, 0.01, 0.007, 0,
0.016, 1, 0.051, 0.01, 0,
0.004, 0.013, 1, 0.023, 0,
0.005, 0.008, 0.029, 1, 0.006,
0, 0, 0, 0.001, 1), byrow = TRUE,
ncol = 5, nrow = 5,
dimnames = list(c("Dy161Di", "Dy162Di", "Dy163Di", "Dy164Di", "Ho165Di"),
c("Dy161Di", "Dy162Di", "Dy163Di", "Dy164Di", "Ho165Di")))
sm_strong <- matrix(c(1, 0.33, 0.1, 0.07, 0,
0.16, 1, 0.51, 0.1, 0,
0.04, 0.13, 1, 0.23, 0,
0.05, 0.08, 0.29, 1, 0.06,
0, 0, 0, 0.01, 1), byrow = TRUE,
ncol = 5, nrow = 5,
dimnames = list(c("Dy161Di", "Dy162Di", "Dy163Di", "Dy164Di", "Ho165Di"),
c("Dy161Di", "Dy162Di", "Dy163Di", "Dy164Di", "Ho165Di")))
# Generate spillover images - strong spillover
sm_images <- pancreasImages
out <- endoapply(sm_images, function(x){
cur_out <- apply(x, c(1,2), function(y) return(y %*% sm_strong))
cur_out <- aperm(cur_out, c(2,3,1))
return(Image(cur_out))
})
channelNames(out) <- channelNames(pancreasImages)
sm_images <- out
# Visual inspection
plotPixels(pancreasImages, colour_by = "H3")
plotPixels(sm_images, colour_by = "H3")
plotPixels(pancreasImages, colour_by = "CD99")
plotPixels(sm_images, colour_by = "CD99")
plotPixels(pancreasImages, colour_by = "PIN")
plotPixels(sm_images, colour_by = "PIN")
plotPixels(pancreasImages, colour_by = "CD8a")
plotPixels(sm_images, colour_by = "CD8a")
plotPixels(pancreasImages, colour_by = "CDH")
plotPixels(sm_images, colour_by = "CDH")
channelNames(sm_images) <- rownames(sm_strong)
expect_silent(cur_out <- compImage(sm_images, sm_strong))
plotPixels(pancreasImages, colour_by = "H3")
plotPixels(cur_out, colour_by = "Dy161Di")
plotPixels(pancreasImages, colour_by = "CD99")
plotPixels(cur_out, colour_by = "Dy162Di")
plotPixels(pancreasImages, colour_by = "PIN")
plotPixels(cur_out, colour_by = "Dy163Di")
plotPixels(pancreasImages, colour_by = "CD8a")
plotPixels(cur_out, colour_by = "Dy164Di")
plotPixels(pancreasImages, colour_by = "CDH")
plotPixels(cur_out, colour_by = "Ho165Di")
expect_s4_class(cur_out, "CytoImageList")
expect_equal(cur_out@int_metadata, sm_images@int_metadata)
expect_equal(cur_out@elementType, sm_images@elementType)
expect_equal(cur_out@elementMetadata, sm_images@elementMetadata)
expect_equal(cur_out@metadata, sm_images@metadata)
expect_equal(as.vector(as.array(cur_out$E34_imc)[1:5, 1:5, 1:5]),
c(2.23578691482544, 2.88552832603454, 3.40094327926636, 3.22383165359497,
0.998766601085662, 0.253727495670314, 1.99001955986022, 0.9950097799301,
3.17507600784301, 1.99001955986023, 1.26963245868682, 2.26464223861694,
0.995009779930112, 1.12834095954895, 0, 0.999198198318478, 0,
2.18006634712219, 4.4866042137146, 0, 1.99001955986023, 1.41092383861542,
0, 0, 0, 2.27523970603943, 0.982835233211518, 1.00974380970001,
4.22755718231201, 2.20721272071921e-16, 2.12543272972107, 0.98138463497162,
0.982144415378575, 4.6439061164856, 4.60726071214195e-16, 2.65206265449524,
4.44857520294786e-16, 7.58915550986695e-16, 1.98678028583527,
0.998160183429719, 7.67151408905227e-16, 0, 0, 1.13381838798523,
0, 0, 0, 0, 0.99363511800766, 0, 0, 0, 2.4701816282689e-16, 0,
0, 0, 2.16337061759597e-16, 0, 0, 0, 2.12880592476332e-07, 0,
0, 0, 0, 0, 0, 1.00299690807935e-16, 0, 0, 0, 0, 0, 6.32687268151533e-16,
0, 6.10191405892687e-17, 1.74999046325684, 0, 1.64071826474853e-16,
3.28572770608724e-17, 1.94439564737972e-16, 2.1319274800509e-16,
3.85965625312858e-16, 2.56614558311194e-16, 7.53835593442659e-17,
4.57368151775708e-16, 9.7717796656081e-17, 1.15749526429746e-16,
6.67803442275272e-17, 6.75710029006214e-18, 1.50170020269713e-16,
0.994114995002746, 3.25868869566352e-17, 0, 0, 0, 0, 2.23528671264648,
0.999972105026245, 1.35901546478271, 0.940284013748169, 8.39323902130127,
0.99849933385849, 10.0154972076416, 4.637526512146, 19.6513938903809,
29.3538608551025, 64.6947784423828, 27.3468551635742, 14.9151773452759,
43.2846260070801, 22.2493591308594, 13.0902843475342, 18.3537216186523,
24.8451747894287, 11.4956560134888, 9.78407001495361, 5.88088750839234,
0.986984312534332, 1.02242743968964, 0, 0, 2.58079719543457,
8.27865314483643, 3.21291995048523))
expect_equal(as.vector(as.array(cur_out$G01_imc)[1:5, 1:5, 1:5]),
c(42.6907119750976, 25.232837677002, 25.239236831665, 24.0195350646973,
14.9995908737183, 28.857421875, 11.1768398284912, 35.8239707946777,
13.4233913421631, 17.8999481201172, 31.6688098907471, 7.51331901550293,
34.4753074645996, 25.2723693847656, 4.44271850585937, 19.173656463623,
28.0249900817871, 15.9826612472534, 10.1179981231689, 9.8008460998535,
23.0579395294189, 12.4933423995972, 23.3956642150879, 8.8474531173706,
6.01483392715453, 0, 0, 3.00805682867537e-15, 1.14488792419434,
0, 0.998183071613309, 0, 2.83587074279785, 6.13080783286931e-06,
0, 1.52557253837585, 0.998160183429719, 3.98741555213928, 0.986646056175229,
7.75992011180919e-16, 0, 0, 0.981460273265839, 0.988971889019013,
1.80674989038669e-15, 1.98973262310028, 0, 2.99159264564514,
0.97893738746643, 4.8189868927002, 0, 6.03217214339641e-17, 0,
0, 0, 0, 0, 0, 0, 2.94078779220581, 1.20622252562452e-15, 9.94543109111735e-17,
0.999847590923308, 0.999899148941042, 0, 5.3520040343311e-18,
0, 0, 6.00943535474613e-16, 0, 7.71311780097397e-16, 0, 0.999503374099735,
1.59382124839064e-15, 0.997151911258697, 0, 0, 0, 0, 0, 0, 0,
0, 0, 2.44793419664594e-16, 0, 0, 0, 0, 0.998086810112, 1.2388428052818e-16,
0, 4.89905647259838e-17, 2.91804912988823e-16, 6.46897126899015e-16,
0, 0, 1.54041314125061, 0.994276285171509, 2.24675965309143,
1.18235528469086, 1.99191880226135, 27.6025657653809, 10.1698713302612,
1.99136519432068, 0.980086922645569, 7.58448457717896, 17.7307186126709,
3.36774706840515, 11.305700302124, 13.0433864593506, 9.24070262908936,
18.3178234100342, 7.87726354598999, 13.3933162689209, 23.2355976104736,
12.103588104248, 15.78102684021, 45.1690864562988, 51.3538932800293,
12.5505819320679, 3.39380836486816, 34.6967391967774, 44.0180168151856,
35.1083717346192))
expect_equal(as.vector(as.array(cur_out$J02_imc)[1:5, 1:5, 1:5]),
c(2.56513524055481, 3.98799896240234, 0.995009779930114, 1.99001955986023,
0, 2.98502922058105, 0.995009779930114, 0.998989760875702, 0,
2.22285175323486, 1.66348826885223, 0, 0.995009779930113, 4.10242557525635,
3.81057667732239, 4.97902917861938, 0.995009779930107, 0.999840378761293,
0, 0, 1.62286090850829, 2.02285480499268, 9.98057272913661e-16,
1.99001955986022, 3.99958419799804, 1.60733664035797, 1.27663969993591,
1.14893049049833e-16, 1.13901674747467, 0, 6.40914067195336e-16,
1.12921868149168e-16, 0.981931209564209, 1.10347361417425e-16,
4.38027567992899e-16, 1.97406804561615, 0, 1.99390268325806,
0.981185019016266, 0.994735479354859, 8.63601561217332e-16, 0.407142639160159,
2.255375623703, 0.978140294551849, 0, 1.53651146412609e-15, 5.91469812393188,
0.969482600688934, 0.924965262413028, 1.90180563926697, 8.00042565079957e-16,
2.38141574816903e-16, 2.43681164872445e-18, 1.30631671547082e-16,
0, 3.41020437597507e-17, 0, 1.33738276886231e-16, 0, 0, 0, 0,
0, 0, 0, 0, 0, 5.13395063957856e-16, 7.64635330341934e-17, 0,
0, 1.25358166550498e-15, 2.17904946155624e-16, 1.44892840762785e-16,
5.27977934518383e-16, 0, 0, 2.59678652173295e-17, 1.63205515154427e-17,
0, 0.998832404613495, 4.04310704311885e-17, 2.62118005054629e-18,
1.06167702530658e-17, 3.37113115874083e-17, 9.57085654436642e-17,
0, 0, 0, 0, 1.21760103020968e-16, 2.49650223556376e-16, 0, 3.06703334435003e-17,
0, 1.49992833090266e-16, 0, 0, 0.994928717613221, 0, 5.78729391098022,
2.12839007377625, 4.09265995025635, 0, 1.99699997901917, 10.0292625427246,
3.22248792648315, 0, 1.99693834781647, 13.6309814453125, 5.7886872291565,
2.06483316421509, 11.0637636184692, 0.987166047096252, 1.99694669246674,
12.1330223083496, 26.7878303527832, 1.76835513114929, 8.9838399887085,
22.0488262176514, 23.7198314666748, 7.28843069076538, 1.08120548725128,
42.1432800292969, 22.6564445495605))
channelNames(cur_out) <- channelNames(pancreasImages)
expect_false(isTRUE(all.equal(imageData(pancreasImages$E34_imc)[,,1], imageData(sm_images$E34_imc)[,,1])))
expect_equal(pancreasImages$E34_imc, cur_out$E34_imc)
expect_equal(pancreasImages$G01_imc, cur_out$G01_imc)
expect_equal(pancreasImages$J02_imc, cur_out$J02_imc)
# weak spillover
sm_images <- pancreasImages
out <- endoapply(sm_images, function(x){
cur_out <- apply(x, c(1,2), function(y) return(y %*% sm_real))
cur_out <- aperm(cur_out, c(2,3,1))
return(Image(cur_out))
})
channelNames(out) <- channelNames(pancreasImages)
sm_images <- out
# Visual inspection
plotPixels(pancreasImages, colour_by = "H3")
plotPixels(sm_images, colour_by = "H3")
plotPixels(pancreasImages, colour_by = "CD99")
plotPixels(sm_images, colour_by = "CD99")
plotPixels(pancreasImages, colour_by = "PIN")
plotPixels(sm_images, colour_by = "PIN")
plotPixels(pancreasImages, colour_by = "CD8a")
plotPixels(sm_images, colour_by = "CD8a")
plotPixels(pancreasImages, colour_by = "CDH")
plotPixels(sm_images, colour_by = "CDH")
channelNames(sm_images) <- rownames(sm_real)
expect_silent(cur_out <- compImage(sm_images, sm_real))
plotPixels(pancreasImages, colour_by = "H3")
plotPixels(cur_out, colour_by = "Dy161Di")
plotPixels(pancreasImages, colour_by = "CD99")
plotPixels(cur_out, colour_by = "Dy162Di")
plotPixels(pancreasImages, colour_by = "PIN")
plotPixels(cur_out, colour_by = "Dy163Di")
plotPixels(pancreasImages, colour_by = "CD8a")
plotPixels(cur_out, colour_by = "Dy164Di")
plotPixels(pancreasImages, colour_by = "CDH")
plotPixels(cur_out, colour_by = "Ho165Di")
expect_equal(as.vector(as.array(cur_out$E34_imc)[1:5, 1:5, 1:5]),
c(2.23578691482544, 2.88552832603455, 3.40094327926636, 3.22383165359497,
0.998766601085664, 0.25372749567032, 1.99001955986024, 0.99500977993013,
3.17507600784302, 1.99001955986023, 1.26963245868683, 2.26464223861695,
0.995009779930118, 1.12834095954895, 4.83596051887921e-15, 0.999198198318485,
2.69332780168834e-15, 2.18006634712219, 4.4866042137146, 2.21770122912683e-16,
1.99001955986023, 1.41092383861542, 4.55654073638636e-16, 3.32619462662008e-15,
3.83754286353452e-16, 2.27523970603943, 0.982835233211518, 1.00974380970001,
4.22755718231201, 0, 2.12543272972107, 0.981384634971619, 0.98214441537857,
4.6439061164856, 6.63651264716718e-16, 2.65206265449524, 4.41551510765918e-16,
2.20556729785805e-16, 1.98678028583527, 0.998160183429718, 1.09655057649601e-16,
1.0327327628663e-17, 8.86478916216937e-16, 1.13381838798523,
0, 0, 0, 0, 0.99363511800766, 0, 0, 1.22499415057453e-15, 1.11238882125062e-16,
0, 0, 1.11200103223052e-16, 0, 1.09352185873552e-16, 0, 0, 2.12880593022593e-07,
0, 0, 2.22308723148135e-16, 0, 0, 0, 0, 2.22459995021217e-16,
0, 0, 0, 0, 0, 0, 0, 1.74999046325684, 4.15025262455971e-19,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.994114995002747, 0,
1.48110139581551e-18, 0, 0, 0, 2.23528671264648, 0.999972105026245,
1.35901546478271, 0.940284013748169, 8.39323902130127, 0.99849933385849,
10.0154972076416, 4.637526512146, 19.6513938903809, 29.3538608551025,
64.6947784423828, 27.3468551635742, 14.9151773452759, 43.2846260070801,
22.2493591308594, 13.0902843475342, 18.3537216186523, 24.8451747894287,
11.4956560134888, 9.78407001495361, 5.88088750839233, 0.986984312534332,
1.02242743968964, 0, 9.22015961887504e-25, 2.58079719543457,
8.27865314483642, 3.21291995048523))
expect_equal(as.vector(as.array(cur_out$G01_imc)[1:5, 1:5, 1:5]),
c(42.6907119750977, 25.232837677002, 25.239236831665, 24.0195350646973,
14.9995908737183, 28.857421875, 11.1768398284912, 35.8239707946777,
13.4233913421631, 17.8999481201172, 31.6688098907471, 7.51331901550293,
34.4753074645996, 25.2723693847656, 4.44271850585938, 19.1736564636231,
28.0249900817871, 15.9826612472534, 10.117998123169, 9.80084609985353,
23.057939529419, 12.4933423995972, 23.3956642150879, 8.84745311737061,
6.01483392715455, 0, 0, 7.09533573928995e-15, 1.14488792419434,
2.20676415100722e-16, 0.998183071613312, 8.8651733037851e-16,
2.83587074279785, 6.13080783323267e-06, 2.16185774069926e-15,
1.52557253837586, 0.998160183429719, 3.98741555213928, 0.986646056175232,
9.14042689628838e-16, 7.09420260716262e-15, 1.77359768936059e-15,
0.981460273265841, 0.988971889019014, 1.77030327197491e-15, 1.98973262310028,
0, 2.99159264564515, 0.978937387466434, 4.8189868927002, 0, 0,
0, 2.21960738102303e-16, 0, 1.11085698156659e-16, 0, 0, 0, 2.94078779220581,
0, 1.10609433251125e-16, 0.99984759092331, 0.99989914894104,
5.04325723449548e-16, 0, 0, 0, 1.09942229769164e-16, 0, 0, 0,
0.999503374099731, 7.79541732183792e-16, 0.997151911258699, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.3343092841449e-16, 0, 0.998086810112,
0, 0, 0, 0, 0, 0, 0, 1.54041314125061, 0.994276285171509, 2.24675965309143,
1.18235528469086, 1.99191880226135, 27.6025657653809, 10.1698713302612,
1.99136519432068, 0.980086922645569, 7.58448457717896, 17.7307186126709,
3.36774706840515, 11.305700302124, 13.0433864593506, 9.24070262908936,
18.3178234100342, 7.87726354598999, 13.3933162689209, 23.2355976104736,
12.103588104248, 15.78102684021, 45.1690864562988, 51.3538932800293,
12.5505819320679, 3.39380836486816, 34.6967391967773, 44.0180168151855,
35.1083717346191))
expect_equal(as.vector(as.array(cur_out$J02_imc)[1:5, 1:5, 1:5]),
c(2.56513524055481, 3.98799896240234, 0.995009779930116, 1.99001955986023,
4.43552575162044e-16, 2.98502922058106, 0.995009779930115, 0.998989760875703,
8.87549248467535e-16, 2.22285175323487, 1.66348826885224, 4.43540245825365e-16,
0.995009779930115, 4.10242557525634, 3.81057667732239, 4.97902917861939,
0.995009779930118, 0.999840378761293, 9.82461080743612e-16, 3.54887573951098e-15,
1.6228609085083, 2.02285480499268, 9.54709748276902e-16, 1.99001955986023,
3.99958419799805, 1.60733664035797, 1.27663969993591, 3.32189581133068e-16,
1.13901674747467, 0, 1.04468528051004e-15, 3.32556811569418e-16,
0.981931209564209, 0, 0, 1.97406804561615, 0, 1.99390268325806,
0.981185019016266, 0.994735479354859, 1.77237813972365e-15, 0.407142639160156,
2.255375623703, 0.978140294551849, 0, 4.42882072657039e-16, 5.91469812393188,
0.969482600688934, 0.924965262413025, 1.90180563926697, 2.22353566301504e-16,
0, 0, 1.3718357599068e-17, 0, 7.19271442227435e-16, 0, 0, 0,
0, 0, 0, 0, 0, 1.1119013885545e-16, 0, 0, 0, 1.30570954634137e-17,
0, 0, 0, 6.34017592606454e-18, 7.79541732183792e-16, 2.22008617562531e-16,
0, 1.73307244964571e-18, 0, 6.63040732823723e-18, 0, 0.998832404613496,
0, 1.73361070713585e-18, 0, 0, 0, 0, 0, 3.46614489929143e-18,
0, 0, 0, 0, 9.24391799796069e-19, 0, 0, 0, 0, 0.994928717613221,
0, 5.78729391098022, 2.12839007377625, 4.09265995025635, 0, 1.99699997901916,
10.0292625427246, 3.22248792648315, 0, 1.99693834781647, 13.6309814453125,
5.78868722915649, 2.06483316421509, 11.0637636184692, 0.987166047096252,
1.99694669246674, 12.1330223083496, 26.7878303527832, 1.76835513114929,
8.9838399887085, 22.0488262176514, 23.7198314666748, 7.28843069076538,
1.08120548725128, 42.1432800292969, 22.6564445495605))
channelNames(cur_out) <- channelNames(pancreasImages)
expect_false(isTRUE(all.equal(imageData(pancreasImages$E34_imc)[,,1], imageData(sm_images$E34_imc)[,,1])))
expect_equal(imageData(pancreasImages$E34_imc), imageData(cur_out$E34_imc))
expect_equal(imageData(pancreasImages$G01_imc), imageData(cur_out$G01_imc))
expect_equal(imageData(pancreasImages$J02_imc), imageData(cur_out$J02_imc))
# on disk
cur_images_ondisk <- CytoImageList(sm_images, on_disk = TRUE,
h5FilesPath = cur_path)
cur_size <- file.info(paste0(cur_path, "/E34_imc.h5"))[,"size"]
expect_s4_class(cur_images_ondisk$E34_imc, "DelayedArray")
expect_s4_class(cur_images_ondisk$G01_imc, "DelayedArray")
expect_s4_class(cur_images_ondisk$J02_imc, "DelayedArray")
expect_silent(cur_out_2 <- compImage(cur_images_ondisk, sm_real))
expect_s4_class(cur_out_2, "CytoImageList")
expect_equal(cur_out_2@int_metadata, sm_images@int_metadata)
expect_equal(cur_out_2@elementType, sm_images@elementType)
expect_equal(cur_out_2@elementMetadata, sm_images@elementMetadata)
expect_equal(cur_out_2@metadata, sm_images@metadata)
channelNames(cur_out_2) <- channelNames(cur_out)
expect_equal(as.array(cur_out$E34_imc),
as.array(cur_out_2$E34_imc))
expect_equal(as.array(cur_out$G01_imc),
as.array(cur_out_2$G01_imc))
expect_equal(as.array(cur_out$G01_imc),
as.array(cur_out_2$G01_imc))
cur_size_2 <- file.info(paste0(cur_path, "/E34_imc.h5"))[,"size"]
expect_gt(cur_size_2, cur_size)
expect_s4_class(cur_out_2$E34_imc, "DelayedArray")
expect_s4_class(cur_out_2$G01_imc, "DelayedArray")
expect_s4_class(cur_out_2$J02_imc, "DelayedArray")
# Check if second layer was created
library(rhdf5)
expect_true("E34_imc_comp" %in% h5ls(path(cur_out_2$E34_imc@seed))$name)
expect_true("E34_imc_comp" %in% h5ls(path(cur_images_ondisk$E34_imc@seed))$name)
expect_equal(h5ls(path(cur_out_2$E34_imc@seed))$name,
c(".E34_imc_dimnames", "3", "E34_imc", "E34_imc_comp"))
expect_silent(cur_out_3 <- compImage(cur_images_ondisk, sm_real))
channelNames(cur_out_3) <- channelNames(cur_out)
expect_equal(as.array(cur_out$E34_imc),
as.array(cur_out_3$E34_imc))
expect_equal(as.array(cur_out$G01_imc),
as.array(cur_out_3$G01_imc))
expect_equal(as.array(cur_out$G01_imc),
as.array(cur_out_3$G01_imc))
expect_equal(h5ls(path(cur_out_3$E34_imc@seed))$name,
c(".E34_imc_dimnames", "3", "E34_imc", "E34_imc_comp"))
# Overwrite
cur_images_ondisk <- CytoImageList(sm_images, on_disk = TRUE,
h5FilesPath = cur_path)
expect_silent(cur_out_2 <- compImage(cur_images_ondisk, sm_real, overwrite = TRUE))
expect_equal(h5ls(path(cur_out_2$E34_imc@seed))$name,
c("E34_imc_comp"))
channelNames(cur_out_2) <- channelNames(pancreasImages)
expect_equal(as.array(cur_out$E34_imc),
as.array(cur_out_2$E34_imc))
expect_equal(as.array(cur_out$G01_imc),
as.array(cur_out_2$G01_imc))
expect_equal(as.array(cur_out$J02_imc),
as.array(cur_out_2$J02_imc))
# Fail
expect_error(compImage("test"),
regexp = "'object' not of type 'CytoImageList'",
fixed = TRUE)
expect_error(compImage(pancreasImages),
regexp = "argument \"sm\" is missing, with no default",
fixed = TRUE)
expect_error(compImage(pancreasImages, "test"),
regexp = "'sm' is not of type 'matrix'",
fixed = TRUE)
expect_error(compImage(pancreasImages, matrix("test")),
regexp = "'sm' contains values outside 0-1 range.",
fixed = TRUE)
expect_error(compImage(pancreasImages, sm_real),
regexp = "Not all 'channelNames(object)' are of the format (mt)(mass)Di.",
fixed = TRUE)
sm <- sm_real
colnames(sm) <- c(1,2,3,4,5)
expect_error(compImage(sm_images, sm),
regexp = "'channelNames(object)' and 'colnames(sm)' do not match.",
fixed = TRUE)
channelNames(sm_images) <- c(1,2,3,4,5)
expect_error(compImage(sm_images, sm_real),
regexp = "Not all 'channelNames(object)' are of the format (mt)(mass)Di.",
fixed = TRUE)
})
BodenmillerGroup/cytomapper documentation built on March 24, 2024, 10:21 p.m.
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test_that("compImage function works", {
data("pancreasImages")
cur_path <- tempdir()
on.exit(unlink(cur_path))
# Mock up realistic spillover matrix
sm_real <- matrix(c(1, 0.033, 0.01, 0.007, 0,
0.016, 1, 0.051, 0.01, 0,
0.004, 0.013, 1, 0.023, 0,
0.005, 0.008, 0.029, 1, 0.006,
0, 0, 0, 0.001, 1), byrow = TRUE,
ncol = 5, nrow = 5,
dimnames = list(c("Dy161Di", "Dy162Di", "Dy163Di", "Dy164Di", "Ho165Di"),
c("Dy161Di", "Dy162Di", "Dy163Di", "Dy164Di", "Ho165Di")))
sm_strong <- matrix(c(1, 0.33, 0.1, 0.07, 0,
0.16, 1, 0.51, 0.1, 0,
0.04, 0.13, 1, 0.23, 0,
0.05, 0.08, 0.29, 1, 0.06,
0, 0, 0, 0.01, 1), byrow = TRUE,
ncol = 5, nrow = 5,
dimnames = list(c("Dy161Di", "Dy162Di", "Dy163Di", "Dy164Di", "Ho165Di"),
c("Dy161Di", "Dy162Di", "Dy163Di", "Dy164Di", "Ho165Di")))
# Generate spillover images - strong spillover
sm_images <- pancreasImages
out <- endoapply(sm_images, function(x){
cur_out <- apply(x, c(1,2), function(y) return(y %*% sm_strong))
cur_out <- aperm(cur_out, c(2,3,1))
return(Image(cur_out))
})
channelNames(out) <- channelNames(pancreasImages)
sm_images <- out
# Visual inspection
plotPixels(pancreasImages, colour_by = "H3")
plotPixels(sm_images, colour_by = "H3")
plotPixels(pancreasImages, colour_by = "CD99")
plotPixels(sm_images, colour_by = "CD99")
plotPixels(pancreasImages, colour_by = "PIN")
plotPixels(sm_images, colour_by = "PIN")
plotPixels(pancreasImages, colour_by = "CD8a")
plotPixels(sm_images, colour_by = "CD8a")
plotPixels(pancreasImages, colour_by = "CDH")
plotPixels(sm_images, colour_by = "CDH")
channelNames(sm_images) <- rownames(sm_strong)
expect_silent(cur_out <- compImage(sm_images, sm_strong))
plotPixels(pancreasImages, colour_by = "H3")
plotPixels(cur_out, colour_by = "Dy161Di")
plotPixels(pancreasImages, colour_by = "CD99")
plotPixels(cur_out, colour_by = "Dy162Di")
plotPixels(pancreasImages, colour_by = "PIN")
plotPixels(cur_out, colour_by = "Dy163Di")
plotPixels(pancreasImages, colour_by = "CD8a")
plotPixels(cur_out, colour_by = "Dy164Di")
plotPixels(pancreasImages, colour_by = "CDH")
plotPixels(cur_out, colour_by = "Ho165Di")
expect_s4_class(cur_out, "CytoImageList")
expect_equal(cur_out@int_metadata, sm_images@int_metadata)
expect_equal(cur_out@elementType, sm_images@elementType)
expect_equal(cur_out@elementMetadata, sm_images@elementMetadata)
expect_equal(cur_out@metadata, sm_images@metadata)
expect_equal(as.vector(as.array(cur_out$E34_imc)[1:5, 1:5, 1:5]),
c(2.23578691482544, 2.88552832603454, 3.40094327926636, 3.22383165359497,
0.998766601085662, 0.253727495670314, 1.99001955986022, 0.9950097799301,
3.17507600784301, 1.99001955986023, 1.26963245868682, 2.26464223861694,
0.995009779930112, 1.12834095954895, 0, 0.999198198318478, 0,
2.18006634712219, 4.4866042137146, 0, 1.99001955986023, 1.41092383861542,
0, 0, 0, 2.27523970603943, 0.982835233211518, 1.00974380970001,
4.22755718231201, 2.20721272071921e-16, 2.12543272972107, 0.98138463497162,
0.982144415378575, 4.6439061164856, 4.60726071214195e-16, 2.65206265449524,
4.44857520294786e-16, 7.58915550986695e-16, 1.98678028583527,
0.998160183429719, 7.67151408905227e-16, 0, 0, 1.13381838798523,
0, 0, 0, 0, 0.99363511800766, 0, 0, 0, 2.4701816282689e-16, 0,
0, 0, 2.16337061759597e-16, 0, 0, 0, 2.12880592476332e-07, 0,
0, 0, 0, 0, 0, 1.00299690807935e-16, 0, 0, 0, 0, 0, 6.32687268151533e-16,
0, 6.10191405892687e-17, 1.74999046325684, 0, 1.64071826474853e-16,
3.28572770608724e-17, 1.94439564737972e-16, 2.1319274800509e-16,
3.85965625312858e-16, 2.56614558311194e-16, 7.53835593442659e-17,
4.57368151775708e-16, 9.7717796656081e-17, 1.15749526429746e-16,
6.67803442275272e-17, 6.75710029006214e-18, 1.50170020269713e-16,
0.994114995002746, 3.25868869566352e-17, 0, 0, 0, 0, 2.23528671264648,
0.999972105026245, 1.35901546478271, 0.940284013748169, 8.39323902130127,
0.99849933385849, 10.0154972076416, 4.637526512146, 19.6513938903809,
29.3538608551025, 64.6947784423828, 27.3468551635742, 14.9151773452759,
43.2846260070801, 22.2493591308594, 13.0902843475342, 18.3537216186523,
24.8451747894287, 11.4956560134888, 9.78407001495361, 5.88088750839234,
0.986984312534332, 1.02242743968964, 0, 0, 2.58079719543457,
8.27865314483643, 3.21291995048523))
expect_equal(as.vector(as.array(cur_out$G01_imc)[1:5, 1:5, 1:5]),
c(42.6907119750976, 25.232837677002, 25.239236831665, 24.0195350646973,
14.9995908737183, 28.857421875, 11.1768398284912, 35.8239707946777,
13.4233913421631, 17.8999481201172, 31.6688098907471, 7.51331901550293,
34.4753074645996, 25.2723693847656, 4.44271850585937, 19.173656463623,
28.0249900817871, 15.9826612472534, 10.1179981231689, 9.8008460998535,
23.0579395294189, 12.4933423995972, 23.3956642150879, 8.8474531173706,
6.01483392715453, 0, 0, 3.00805682867537e-15, 1.14488792419434,
0, 0.998183071613309, 0, 2.83587074279785, 6.13080783286931e-06,
0, 1.52557253837585, 0.998160183429719, 3.98741555213928, 0.986646056175229,
7.75992011180919e-16, 0, 0, 0.981460273265839, 0.988971889019013,
1.80674989038669e-15, 1.98973262310028, 0, 2.99159264564514,
0.97893738746643, 4.8189868927002, 0, 6.03217214339641e-17, 0,
0, 0, 0, 0, 0, 0, 2.94078779220581, 1.20622252562452e-15, 9.94543109111735e-17,
0.999847590923308, 0.999899148941042, 0, 5.3520040343311e-18,
0, 0, 6.00943535474613e-16, 0, 7.71311780097397e-16, 0, 0.999503374099735,
1.59382124839064e-15, 0.997151911258697, 0, 0, 0, 0, 0, 0, 0,
0, 0, 2.44793419664594e-16, 0, 0, 0, 0, 0.998086810112, 1.2388428052818e-16,
0, 4.89905647259838e-17, 2.91804912988823e-16, 6.46897126899015e-16,
0, 0, 1.54041314125061, 0.994276285171509, 2.24675965309143,
1.18235528469086, 1.99191880226135, 27.6025657653809, 10.1698713302612,
1.99136519432068, 0.980086922645569, 7.58448457717896, 17.7307186126709,
3.36774706840515, 11.305700302124, 13.0433864593506, 9.24070262908936,
18.3178234100342, 7.87726354598999, 13.3933162689209, 23.2355976104736,
12.103588104248, 15.78102684021, 45.1690864562988, 51.3538932800293,
12.5505819320679, 3.39380836486816, 34.6967391967774, 44.0180168151856,
35.1083717346192))
expect_equal(as.vector(as.array(cur_out$J02_imc)[1:5, 1:5, 1:5]),
c(2.56513524055481, 3.98799896240234, 0.995009779930114, 1.99001955986023,
0, 2.98502922058105, 0.995009779930114, 0.998989760875702, 0,
2.22285175323486, 1.66348826885223, 0, 0.995009779930113, 4.10242557525635,
3.81057667732239, 4.97902917861938, 0.995009779930107, 0.999840378761293,
0, 0, 1.62286090850829, 2.02285480499268, 9.98057272913661e-16,
1.99001955986022, 3.99958419799804, 1.60733664035797, 1.27663969993591,
1.14893049049833e-16, 1.13901674747467, 0, 6.40914067195336e-16,
1.12921868149168e-16, 0.981931209564209, 1.10347361417425e-16,
4.38027567992899e-16, 1.97406804561615, 0, 1.99390268325806,
0.981185019016266, 0.994735479354859, 8.63601561217332e-16, 0.407142639160159,
2.255375623703, 0.978140294551849, 0, 1.53651146412609e-15, 5.91469812393188,
0.969482600688934, 0.924965262413028, 1.90180563926697, 8.00042565079957e-16,
2.38141574816903e-16, 2.43681164872445e-18, 1.30631671547082e-16,
0, 3.41020437597507e-17, 0, 1.33738276886231e-16, 0, 0, 0, 0,
0, 0, 0, 0, 0, 5.13395063957856e-16, 7.64635330341934e-17, 0,
0, 1.25358166550498e-15, 2.17904946155624e-16, 1.44892840762785e-16,
5.27977934518383e-16, 0, 0, 2.59678652173295e-17, 1.63205515154427e-17,
0, 0.998832404613495, 4.04310704311885e-17, 2.62118005054629e-18,
1.06167702530658e-17, 3.37113115874083e-17, 9.57085654436642e-17,
0, 0, 0, 0, 1.21760103020968e-16, 2.49650223556376e-16, 0, 3.06703334435003e-17,
0, 1.49992833090266e-16, 0, 0, 0.994928717613221, 0, 5.78729391098022,
2.12839007377625, 4.09265995025635, 0, 1.99699997901917, 10.0292625427246,
3.22248792648315, 0, 1.99693834781647, 13.6309814453125, 5.7886872291565,
2.06483316421509, 11.0637636184692, 0.987166047096252, 1.99694669246674,
12.1330223083496, 26.7878303527832, 1.76835513114929, 8.9838399887085,
22.0488262176514, 23.7198314666748, 7.28843069076538, 1.08120548725128,
42.1432800292969, 22.6564445495605))
channelNames(cur_out) <- channelNames(pancreasImages)
expect_false(isTRUE(all.equal(imageData(pancreasImages$E34_imc)[,,1], imageData(sm_images$E34_imc)[,,1])))
expect_equal(pancreasImages$E34_imc, cur_out$E34_imc)
expect_equal(pancreasImages$G01_imc, cur_out$G01_imc)
expect_equal(pancreasImages$J02_imc, cur_out$J02_imc)
# weak spillover
sm_images <- pancreasImages
out <- endoapply(sm_images, function(x){
cur_out <- apply(x, c(1,2), function(y) return(y %*% sm_real))
cur_out <- aperm(cur_out, c(2,3,1))
return(Image(cur_out))
})
channelNames(out) <- channelNames(pancreasImages)
sm_images <- out
# Visual inspection
plotPixels(pancreasImages, colour_by = "H3")
plotPixels(sm_images, colour_by = "H3")
plotPixels(pancreasImages, colour_by = "CD99")
plotPixels(sm_images, colour_by = "CD99")
plotPixels(pancreasImages, colour_by = "PIN")
plotPixels(sm_images, colour_by = "PIN")
plotPixels(pancreasImages, colour_by = "CD8a")
plotPixels(sm_images, colour_by = "CD8a")
plotPixels(pancreasImages, colour_by = "CDH")
plotPixels(sm_images, colour_by = "CDH")
channelNames(sm_images) <- rownames(sm_real)
expect_silent(cur_out <- compImage(sm_images, sm_real))
plotPixels(pancreasImages, colour_by = "H3")
plotPixels(cur_out, colour_by = "Dy161Di")
plotPixels(pancreasImages, colour_by = "CD99")
plotPixels(cur_out, colour_by = "Dy162Di")
plotPixels(pancreasImages, colour_by = "PIN")
plotPixels(cur_out, colour_by = "Dy163Di")
plotPixels(pancreasImages, colour_by = "CD8a")
plotPixels(cur_out, colour_by = "Dy164Di")
plotPixels(pancreasImages, colour_by = "CDH")
plotPixels(cur_out, colour_by = "Ho165Di")
expect_equal(as.vector(as.array(cur_out$E34_imc)[1:5, 1:5, 1:5]),
c(2.23578691482544, 2.88552832603455, 3.40094327926636, 3.22383165359497,
0.998766601085664, 0.25372749567032, 1.99001955986024, 0.99500977993013,
3.17507600784302, 1.99001955986023, 1.26963245868683, 2.26464223861695,
0.995009779930118, 1.12834095954895, 4.83596051887921e-15, 0.999198198318485,
2.69332780168834e-15, 2.18006634712219, 4.4866042137146, 2.21770122912683e-16,
1.99001955986023, 1.41092383861542, 4.55654073638636e-16, 3.32619462662008e-15,
3.83754286353452e-16, 2.27523970603943, 0.982835233211518, 1.00974380970001,
4.22755718231201, 0, 2.12543272972107, 0.981384634971619, 0.98214441537857,
4.6439061164856, 6.63651264716718e-16, 2.65206265449524, 4.41551510765918e-16,
2.20556729785805e-16, 1.98678028583527, 0.998160183429718, 1.09655057649601e-16,
1.0327327628663e-17, 8.86478916216937e-16, 1.13381838798523,
0, 0, 0, 0, 0.99363511800766, 0, 0, 1.22499415057453e-15, 1.11238882125062e-16,
0, 0, 1.11200103223052e-16, 0, 1.09352185873552e-16, 0, 0, 2.12880593022593e-07,
0, 0, 2.22308723148135e-16, 0, 0, 0, 0, 2.22459995021217e-16,
0, 0, 0, 0, 0, 0, 0, 1.74999046325684, 4.15025262455971e-19,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.994114995002747, 0,
1.48110139581551e-18, 0, 0, 0, 2.23528671264648, 0.999972105026245,
1.35901546478271, 0.940284013748169, 8.39323902130127, 0.99849933385849,
10.0154972076416, 4.637526512146, 19.6513938903809, 29.3538608551025,
64.6947784423828, 27.3468551635742, 14.9151773452759, 43.2846260070801,
22.2493591308594, 13.0902843475342, 18.3537216186523, 24.8451747894287,
11.4956560134888, 9.78407001495361, 5.88088750839233, 0.986984312534332,
1.02242743968964, 0, 9.22015961887504e-25, 2.58079719543457,
8.27865314483642, 3.21291995048523))
expect_equal(as.vector(as.array(cur_out$G01_imc)[1:5, 1:5, 1:5]),
c(42.6907119750977, 25.232837677002, 25.239236831665, 24.0195350646973,
14.9995908737183, 28.857421875, 11.1768398284912, 35.8239707946777,
13.4233913421631, 17.8999481201172, 31.6688098907471, 7.51331901550293,
34.4753074645996, 25.2723693847656, 4.44271850585938, 19.1736564636231,
28.0249900817871, 15.9826612472534, 10.117998123169, 9.80084609985353,
23.057939529419, 12.4933423995972, 23.3956642150879, 8.84745311737061,
6.01483392715455, 0, 0, 7.09533573928995e-15, 1.14488792419434,
2.20676415100722e-16, 0.998183071613312, 8.8651733037851e-16,
2.83587074279785, 6.13080783323267e-06, 2.16185774069926e-15,
1.52557253837586, 0.998160183429719, 3.98741555213928, 0.986646056175232,
9.14042689628838e-16, 7.09420260716262e-15, 1.77359768936059e-15,
0.981460273265841, 0.988971889019014, 1.77030327197491e-15, 1.98973262310028,
0, 2.99159264564515, 0.978937387466434, 4.8189868927002, 0, 0,
0, 2.21960738102303e-16, 0, 1.11085698156659e-16, 0, 0, 0, 2.94078779220581,
0, 1.10609433251125e-16, 0.99984759092331, 0.99989914894104,
5.04325723449548e-16, 0, 0, 0, 1.09942229769164e-16, 0, 0, 0,
0.999503374099731, 7.79541732183792e-16, 0.997151911258699, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.3343092841449e-16, 0, 0.998086810112,
0, 0, 0, 0, 0, 0, 0, 1.54041314125061, 0.994276285171509, 2.24675965309143,
1.18235528469086, 1.99191880226135, 27.6025657653809, 10.1698713302612,
1.99136519432068, 0.980086922645569, 7.58448457717896, 17.7307186126709,
3.36774706840515, 11.305700302124, 13.0433864593506, 9.24070262908936,
18.3178234100342, 7.87726354598999, 13.3933162689209, 23.2355976104736,
12.103588104248, 15.78102684021, 45.1690864562988, 51.3538932800293,
12.5505819320679, 3.39380836486816, 34.6967391967773, 44.0180168151855,
35.1083717346191))
expect_equal(as.vector(as.array(cur_out$J02_imc)[1:5, 1:5, 1:5]),
c(2.56513524055481, 3.98799896240234, 0.995009779930116, 1.99001955986023,
4.43552575162044e-16, 2.98502922058106, 0.995009779930115, 0.998989760875703,
8.87549248467535e-16, 2.22285175323487, 1.66348826885224, 4.43540245825365e-16,
0.995009779930115, 4.10242557525634, 3.81057667732239, 4.97902917861939,
0.995009779930118, 0.999840378761293, 9.82461080743612e-16, 3.54887573951098e-15,
1.6228609085083, 2.02285480499268, 9.54709748276902e-16, 1.99001955986023,
3.99958419799805, 1.60733664035797, 1.27663969993591, 3.32189581133068e-16,
1.13901674747467, 0, 1.04468528051004e-15, 3.32556811569418e-16,
0.981931209564209, 0, 0, 1.97406804561615, 0, 1.99390268325806,
0.981185019016266, 0.994735479354859, 1.77237813972365e-15, 0.407142639160156,
2.255375623703, 0.978140294551849, 0, 4.42882072657039e-16, 5.91469812393188,
0.969482600688934, 0.924965262413025, 1.90180563926697, 2.22353566301504e-16,
0, 0, 1.3718357599068e-17, 0, 7.19271442227435e-16, 0, 0, 0,
0, 0, 0, 0, 0, 1.1119013885545e-16, 0, 0, 0, 1.30570954634137e-17,
0, 0, 0, 6.34017592606454e-18, 7.79541732183792e-16, 2.22008617562531e-16,
0, 1.73307244964571e-18, 0, 6.63040732823723e-18, 0, 0.998832404613496,
0, 1.73361070713585e-18, 0, 0, 0, 0, 0, 3.46614489929143e-18,
0, 0, 0, 0, 9.24391799796069e-19, 0, 0, 0, 0, 0.994928717613221,
0, 5.78729391098022, 2.12839007377625, 4.09265995025635, 0, 1.99699997901916,
10.0292625427246, 3.22248792648315, 0, 1.99693834781647, 13.6309814453125,
5.78868722915649, 2.06483316421509, 11.0637636184692, 0.987166047096252,
1.99694669246674, 12.1330223083496, 26.7878303527832, 1.76835513114929,
8.9838399887085, 22.0488262176514, 23.7198314666748, 7.28843069076538,
1.08120548725128, 42.1432800292969, 22.6564445495605))
channelNames(cur_out) <- channelNames(pancreasImages)
expect_false(isTRUE(all.equal(imageData(pancreasImages$E34_imc)[,,1], imageData(sm_images$E34_imc)[,,1])))
expect_equal(imageData(pancreasImages$E34_imc), imageData(cur_out$E34_imc))
expect_equal(imageData(pancreasImages$G01_imc), imageData(cur_out$G01_imc))
expect_equal(imageData(pancreasImages$J02_imc), imageData(cur_out$J02_imc))
# on disk
cur_images_ondisk <- CytoImageList(sm_images, on_disk = TRUE,
h5FilesPath = cur_path)
cur_size <- file.info(paste0(cur_path, "/E34_imc.h5"))[,"size"]
expect_s4_class(cur_images_ondisk$E34_imc, "DelayedArray")
expect_s4_class(cur_images_ondisk$G01_imc, "DelayedArray")
expect_s4_class(cur_images_ondisk$J02_imc, "DelayedArray")
expect_silent(cur_out_2 <- compImage(cur_images_ondisk, sm_real))
expect_s4_class(cur_out_2, "CytoImageList")
expect_equal(cur_out_2@int_metadata, sm_images@int_metadata)
expect_equal(cur_out_2@elementType, sm_images@elementType)
expect_equal(cur_out_2@elementMetadata, sm_images@elementMetadata)
expect_equal(cur_out_2@metadata, sm_images@metadata)
channelNames(cur_out_2) <- channelNames(cur_out)
expect_equal(as.array(cur_out$E34_imc),
as.array(cur_out_2$E34_imc))
expect_equal(as.array(cur_out$G01_imc),
as.array(cur_out_2$G01_imc))
expect_equal(as.array(cur_out$G01_imc),
as.array(cur_out_2$G01_imc))
cur_size_2 <- file.info(paste0(cur_path, "/E34_imc.h5"))[,"size"]
expect_gt(cur_size_2, cur_size)
expect_s4_class(cur_out_2$E34_imc, "DelayedArray")
expect_s4_class(cur_out_2$G01_imc, "DelayedArray")
expect_s4_class(cur_out_2$J02_imc, "DelayedArray")
# Check if second layer was created
library(rhdf5)
expect_true("E34_imc_comp" %in% h5ls(path(cur_out_2$E34_imc@seed))$name)
expect_true("E34_imc_comp" %in% h5ls(path(cur_images_ondisk$E34_imc@seed))$name)
expect_equal(h5ls(path(cur_out_2$E34_imc@seed))$name,
c(".E34_imc_dimnames", "3", "E34_imc", "E34_imc_comp"))
expect_silent(cur_out_3 <- compImage(cur_images_ondisk, sm_real))
channelNames(cur_out_3) <- channelNames(cur_out)
expect_equal(as.array(cur_out$E34_imc),
as.array(cur_out_3$E34_imc))
expect_equal(as.array(cur_out$G01_imc),
as.array(cur_out_3$G01_imc))
expect_equal(as.array(cur_out$G01_imc),
as.array(cur_out_3$G01_imc))
expect_equal(h5ls(path(cur_out_3$E34_imc@seed))$name,
c(".E34_imc_dimnames", "3", "E34_imc", "E34_imc_comp"))
# Overwrite
cur_images_ondisk <- CytoImageList(sm_images, on_disk = TRUE,
h5FilesPath = cur_path)
expect_silent(cur_out_2 <- compImage(cur_images_ondisk, sm_real, overwrite = TRUE))
expect_equal(h5ls(path(cur_out_2$E34_imc@seed))$name,
c("E34_imc_comp"))
channelNames(cur_out_2) <- channelNames(pancreasImages)
expect_equal(as.array(cur_out$E34_imc),
as.array(cur_out_2$E34_imc))
expect_equal(as.array(cur_out$G01_imc),
as.array(cur_out_2$G01_imc))
expect_equal(as.array(cur_out$J02_imc),
as.array(cur_out_2$J02_imc))
# Fail
expect_error(compImage("test"),
regexp = "'object' not of type 'CytoImageList'",
fixed = TRUE)
expect_error(compImage(pancreasImages),
regexp = "argument \"sm\" is missing, with no default",
fixed = TRUE)
expect_error(compImage(pancreasImages, "test"),
regexp = "'sm' is not of type 'matrix'",
fixed = TRUE)
expect_error(compImage(pancreasImages, matrix("test")),
regexp = "'sm' contains values outside 0-1 range.",
fixed = TRUE)
expect_error(compImage(pancreasImages, sm_real),
regexp = "Not all 'channelNames(object)' are of the format (mt)(mass)Di.",
fixed = TRUE)
sm <- sm_real
colnames(sm) <- c(1,2,3,4,5)
expect_error(compImage(sm_images, sm),
regexp = "'channelNames(object)' and 'colnames(sm)' do not match.",
fixed = TRUE)
channelNames(sm_images) <- c(1,2,3,4,5)
expect_error(compImage(sm_images, sm_real),
regexp = "Not all 'channelNames(object)' are of the format (mt)(mass)Di.",
fixed = TRUE)
})
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