Last updated: 2020-11-18
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Knit directory: cytomapper_publication/
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This script downloads 100 example images and masks form the pancreas IMC dataset available here. The dataset is associated to the following publication:
The images and masks have been created using the imctools package and the IMC segmentation pipeline. We will use the cytomapper package to read in the images and create CytoImageList objects.
Here, a subset of 100 images from the pancreas IMC dataset is downloaded. We use the loadImages function of the cytomapper package to read them into a CytoImageList object.
library(cytomapper)
# Download the zipped folder image and unzip it
url.images <- ("https://data.mendeley.com/public-files/datasets/cydmwsfztj/files/b37054d2-d5d0-4c48-a001-81ff77136f41/file_downloaded")
download.file(url.images, destfile = "data/PancreasData/ImageSubset.zip")
unzip("data/PancreasData/ImageSubset.zip", exdir = "data/PancreasData/")
file.remove("data/PancreasData/ImageSubset.zip")[1] TRUE# Load the images as a CytoImageList object
images <- loadImages("data/PancreasData/", pattern="_full_clean.tiff")
imagesCytoImageList containing 100 image(s)
names(100): E02_a0_full_clean E03_a0_full_clean E04_a0_full_clean E05_a0_full_clean E06_a0_full_clean E07_a0_full_clean E08_a0_full_clean E09_a0_full_clean E10_a0_full_clean E11_a0_full_clean E12_a0_full_clean E13_a0_full_clean E14_a0_full_clean E15_a0_full_clean E16_a0_full_clean E17_a0_full_clean E18_a0_full_clean E19_a0_full_clean E20_a0_full_clean E21_a0_full_clean E22_a0_full_clean E23_a0_full_clean E24_a0_full_clean E25_a0_full_clean E26_a0_full_clean E27_a0_full_clean E28_a0_full_clean E29_a0_full_clean E30_a0_full_clean E31_a0_full_clean E32_a0_full_clean E33_a0_full_clean E34_a0_full_clean G01_a0_full_clean G02_a0_full_clean G03_a0_full_clean G04_a0_full_clean G05_a0_full_clean G06_a0_full_clean G07_a0_full_clean G08_a0_full_clean G09_a0_full_clean G10_a0_full_clean G11_a0_full_clean G12_a0_full_clean G13_a0_full_clean G14_a0_full_clean G15_a0_full_clean G16_a0_full_clean G17_a0_full_clean G18_a0_full_clean G19_a0_full_clean G20_a0_full_clean G21_a0_full_clean G22_a0_full_clean G23_a0_full_clean G24_a0_full_clean G25_a0_full_clean G26_a0_full_clean G27_a0_full_clean G28_a0_full_clean G29_a0_full_clean G30_a0_full_clean G31_a0_full_clean G32_a0_full_clean G33_a0_full_clean J01_a0_full_clean J02_a0_full_clean J03_a0_full_clean J04_a0_full_clean J05_a0_full_clean J06_a0_full_clean J07_a0_full_clean J08_a0_full_clean J09_a0_full_clean J10_a0_full_clean J11_a0_full_clean J12_a0_full_clean J13_a0_full_clean J14_a0_full_clean J15_a0_full_clean J16_a0_full_clean J17_a0_full_clean J18_a0_full_clean J19_a0_full_clean J20_a0_full_clean J21_a0_full_clean J22_a0_full_clean J23_a0_full_clean J24_a0_full_clean J25_a0_full_clean J26_a0_full_clean J27_a0_full_clean J28_a0_full_clean J29_a0_full_clean J30_a0_full_clean J31_a0_full_clean J32_a0_full_clean J33_a0_full_clean J34_a0_full_clean
Each image contains 38 channel(s)We also download the associated segmentation masks and read them into a CytoImageList object.
# Download the zipped folder masks and unzip it
url.masks <- ("https://data.mendeley.com/public-files/datasets/cydmwsfztj/files/13679a61-e9b4-4820-9f09-a5bbc697647c/file_downloaded")
download.file(url.masks, destfile = "data/PancreasData/Masks.zip")
unzip("data/PancreasData/Masks.zip", exdir = "data/PancreasData/")
file.remove("data/PancreasData/Masks.zip")[1] TRUE# Load the images as a CytoImageList object
masks <- loadImages("data/PancreasData/", pattern="_full_mask.tiff")
masksCytoImageList containing 845 image(s)
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N60_a0_full_mask O01_a0_full_mask O02_a0_full_mask O03_a0_full_mask O04_a0_full_mask O05_a0_full_mask O06_a0_full_mask O07_a0_full_mask O08_a0_full_mask O09_a0_full_mask O10_a0_full_mask O11_a0_full_mask O12_a0_full_mask O13_a0_full_mask O14_a0_full_mask O15_a0_full_mask O16_a0_full_mask O17_a0_full_mask O18_a0_full_mask O19_a0_full_mask O20_a0_full_mask O21_a0_full_mask O22_a0_full_mask O23_a0_full_mask O24_a0_full_mask O25_a0_full_mask O26_a0_full_mask O27_a0_full_mask O28_a0_full_mask O29_a0_full_mask O30_a0_full_mask O31_a0_full_mask O32_a0_full_mask O33_a0_full_mask O34_a0_full_mask O35_a0_full_mask O36_a0_full_mask P05_a0_full_mask P07_a0_full_mask P09_a0_full_mask P15_a0_full_mask P17_a0_full_mask P18_a0_full_mask P21_a0_full_mask P23_a0_full_mask P25_a0_full_mask P28_a0_full_mask P30_a0_full_mask P31_a0_full_mask P32_a0_full_mask P33_a0_full_mask P34_a0_full_mask P35_a0_full_mask P36_a0_full_mask P37_a0_full_mask P38_a0_full_mask P39_a0_full_mask P40_a0_full_mask P41_a0_full_mask P42_a0_full_mask P43_a0_full_mask P44_a0_full_mask P45_a0_full_mask P46_a0_full_mask P47_a0_full_mask P48_a0_full_mask P49_a0_full_mask Q01_a0_full_mask Q02_a0_full_mask Q03_a0_full_mask Q04_a0_full_mask Q05_a0_full_mask Q06_a0_full_mask Q07_a0_full_mask Q08_a0_full_mask Q09_a0_full_mask Q10_a0_full_mask Q11_a0_full_mask Q12_a0_full_mask Q13_a0_full_mask Q14_a0_full_mask Q15_a0_full_mask Q16_a0_full_mask Q17_a0_full_mask Q18_a0_full_mask Q19_a0_full_mask Q20_a0_full_mask Q21_a0_full_mask Q22_a0_full_mask Q23_a0_full_mask Q24_a0_full_mask Q25_a0_full_mask Q26_a0_full_mask Q27_a0_full_mask Q28_a0_full_mask Q29_a0_full_mask Q31_a0_full_mask Q32_a0_full_mask Q33_a0_full_mask Q34_a0_full_mask Q35_a0_full_mask Q36_a0_full_mask R01_a0_full_mask R02_a0_full_mask R03_a0_full_mask R04_a0_full_mask R05_a0_full_mask R06_a0_full_mask R07_a0_full_mask R08_a0_full_mask R09_a0_full_mask R10_a0_full_mask R11_a0_full_mask R12_a0_full_mask R13_a0_full_mask R14_a0_full_mask R15_a0_full_mask R16_a0_full_mask R17_a0_full_mask R18_a0_full_mask R19_a0_full_mask R20_a0_full_mask R21_a0_full_mask R22_a0_full_mask R23_a0_full_mask R24_a0_full_mask R25_a0_full_mask R26_a0_full_mask R27_a0_full_mask R28_a0_full_mask R29_a0_full_mask R30_a0_full_mask R31_a0_full_mask R32_a0_full_mask R33_a0_full_mask R34_a0_full_mask R35_a0_full_mask R36_a0_full_mask S01_a0_full_mask S02_a0_full_mask S03_a0_full_mask S04_a0_full_mask S05_a0_full_mask S06_a0_full_mask S07_a0_full_mask S08_a0_full_mask S09_a0_full_mask S10_a0_full_mask S11_a0_full_mask S12_a0_full_mask S13_a0_full_mask S14_a0_full_mask S15_a0_full_mask S16_a0_full_mask S17_a0_full_mask S18_a0_full_mask S19_a0_full_mask S20_a0_full_mask S21_a0_full_mask S22_a0_full_mask S23_a0_full_mask S25_a0_full_mask S26_a0_full_mask S27_a0_full_mask S28_a0_full_mask S29_a0_full_mask S30_a0_full_mask S31_a0_full_mask S32_a0_full_mask S33_a0_full_mask S34_a0_full_mask S35_a0_full_mask S36_a0_full_mask T05_a0_full_mask T06_a0_full_mask T08_a0_full_mask T09_a0_full_mask T10_a0_full_mask T11_a0_full_mask T12_a0_full_mask T13_a0_full_mask T14_a0_full_mask T15_a0_full_mask T16_a0_full_mask T17_a0_full_mask T18_a0_full_mask T19_a0_full_mask T20_a0_full_mask T21_a0_full_mask T22_a0_full_mask T23_a0_full_mask T24_a0_full_mask T25_a0_full_mask T26_a0_full_mask T27_a0_full_mask T28_a0_full_mask T29_a0_full_mask T30_a0_full_mask T31_a0_full_mask T32_a0_full_mask T33_a0_full_mask T34_a0_full_mask T35_a0_full_mask T36_a0_full_mask T37_a0_full_mask T38_a0_full_mask T39_a0_full_mask T40_a0_full_mask U02_a0_full_mask U03_a0_full_mask U04_a0_full_mask U10_a0_full_mask U11_a0_full_mask U12_a0_full_mask U13_a0_full_mask U14_a0_full_mask U15_a0_full_mask U16_a0_full_mask U22_a0_full_mask U27_a0_full_mask U28_a0_full_mask U29_a0_full_mask U30_a0_full_mask U32_a0_full_mask U33_a0_full_mask U34_a0_full_mask U35_a0_full_mask U36_a0_full_mask U37_a0_full_mask U38_a0_full_mask U39_a0_full_mask U40_a0_full_mask U41_a0_full_mask U42_a0_full_mask U43_a0_full_mask U44_a0_full_mask U45_a0_full_mask U46_a0_full_mask U47_a0_full_mask U48_a0_full_mask U50_a0_full_mask U51_a0_full_mask U52_a0_full_mask V01_a0_full_mask V02_a0_full_mask V03_a0_full_mask V04_a0_full_mask V05_a0_full_mask V06_a0_full_mask V07_a0_full_mask V08_a0_full_mask V09_a0_full_mask V10_a0_full_mask V11_a0_full_mask V12_a0_full_mask V13_a0_full_mask V14_a0_full_mask V15_a0_full_mask V16_a0_full_mask V17_a0_full_mask V18_a0_full_mask V19_a0_full_mask V20_a0_full_mask V21_a0_full_mask V22_a0_full_mask V23_a0_full_mask V24_a0_full_mask V25_a0_full_mask V26_a0_full_mask V27_a0_full_mask V28_a0_full_mask V29_a0_full_mask V30_a0_full_mask V31_a0_full_mask V32_a0_full_mask V33_a0_full_mask V34_a0_full_mask V35_a0_full_mask V36_a0_full_mask W01_a0_full_mask W02_a0_full_mask W03_a0_full_mask W04_a0_full_mask W05_a0_full_mask W06_a0_full_mask W07_a0_full_mask W08_a0_full_mask W09_a0_full_mask W10_a0_full_mask W11_a0_full_mask W12_a0_full_mask W13_a0_full_mask W14_a0_full_mask W15_a0_full_mask W16_a0_full_mask W17_a0_full_mask W18_a0_full_mask W19_a0_full_mask W20_a0_full_mask W21_a0_full_mask W22_a0_full_mask W23_a0_full_mask W24_a0_full_mask W25_a0_full_mask W26_a0_full_mask W27_a0_full_mask W28_a0_full_mask W29_a0_full_mask W31_a0_full_mask W32_a0_full_mask W33_a0_full_mask W34_a0_full_mask W35_a0_full_mask W36_a0_full_mask W37_a0_full_mask W38_a0_full_mask W39_a0_full_mask X01_a0_full_mask X02_a0_full_mask X03_a0_full_mask X04_a0_full_mask X05_a0_full_mask X06_a0_full_mask X07_a0_full_mask X08_a0_full_mask X09_a0_full_mask X10_a0_full_mask X11_a0_full_mask X12_a0_full_mask X13_a0_full_mask X14_a0_full_mask X15_a0_full_mask X16_a0_full_mask X17_a0_full_mask X18_a0_full_mask X19_a0_full_mask X20_a0_full_mask X21_a0_full_mask X22_a0_full_mask X23_a0_full_mask X24_a0_full_mask X26_a0_full_mask X27_a0_full_mask X28_a0_full_mask X29_a0_full_mask X30_a0_full_mask X31_a0_full_mask X32_a0_full_mask X34_a0_full_mask X35_a0_full_mask X36_a0_full_mask Y01_a0_full_mask Y02_a0_full_mask Y03_a0_full_mask Y04_a0_full_mask Y05_a0_full_mask Y06_a0_full_mask Y07_a0_full_mask Y08_a0_full_mask Y09_a0_full_mask Y10_a0_full_mask Y11_a0_full_mask Y12_a0_full_mask Y14_a0_full_mask Y15_a0_full_mask Y16_a0_full_mask Y17_a0_full_mask Y18_a0_full_mask Y19_a0_full_mask Y20_a0_full_mask Y21_a0_full_mask Y22_a0_full_mask Y23_a0_full_mask Y24_a0_full_mask Y25_a0_full_mask Y26_a0_full_mask Y27_a0_full_mask Y28_a0_full_mask Y29_a0_full_mask Y30_a0_full_mask Y31_a0_full_mask
Each image contains 1 channelHere, we remove the downloaded images again.
# Remove image stacks
images.del <- list.files("data/PancreasData/", pattern="_full_clean.tiff", full.names = TRUE)
file.remove(images.del) [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE# Remove masks
masks.del <- list.files("data/PancreasData/", pattern="_full_mask.tiff", full.names = TRUE)
file.remove(masks.del) [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[241] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[256] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[271] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[286] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[301] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[316] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[331] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[346] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[361] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[376] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[391] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[406] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[421] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[436] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[451] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[466] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[481] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[496] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[511] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[526] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[541] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[556] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[571] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[586] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[601] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[616] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[631] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[646] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[661] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[676] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[691] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[706] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[721] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[736] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[751] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[766] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[781] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[796] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[811] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[826] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[841] TRUE TRUE TRUE TRUE TRUEHere, we will download the panel information, which contains antibody-related metadata. However, for some datasets, the channel-order and the panel order do not match. For this, the channel-mass file is used to match panel information and image stack slices. This will be important later to set the channelNames of the CytoImageList objects.
# Import panel
url.panel <- ("https://data.mendeley.com/public-files/datasets/cydmwsfztj/files/2f9fecfc-b98f-4937-bc38-ae1b959bd74d/file_downloaded")
download.file(url.panel, destfile = "data/PancreasData/panel.csv")
panel <- read.csv("data/PancreasData/panel.csv")
# Import channel-mass file
url.channelmass <- ("https://data.mendeley.com/public-files/datasets/cydmwsfztj/files/704312eb-377c-42e2-8227-44bb9aca0fb3/file_downloaded")
download.file(url.channelmass, destfile = "data/PancreasData/ChannelMass.csv")
channel.mass <- read.csv("data/PancreasData/ChannelMass.csv", header = FALSE)We will now have to process the images to make them compatible with cytomapper. The masks are 16-bit images and need to be re-scaled in order to obtain integer cell IDs.
# Before scaling
masks[[1]]Image
colorMode : Grayscale
storage.mode : double
dim : 794 655
frames.total : 1
frames.render: 1
imageData(object)[1:5,1:6]
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 0 0 0 0 0.0008850233 0.0008850233
[2,] 0 0 0 0 0.0008850233 0.0008850233
[3,] 0 0 0 0 0.0008850233 0.0008850233
[4,] 0 0 0 0 0.0008850233 0.0008850233
[5,] 0 0 0 0 0.0008850233 0.0008850233masks <- scaleImages(masks, value = (2 ^ 16) - 1)
# After scaling
masks[[1]]Image
colorMode : Grayscale
storage.mode : double
dim : 794 655
frames.total : 1
frames.render: 1
imageData(object)[1:5,1:6]
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 0 0 0 0 58 58
[2,] 0 0 0 0 58 58
[3,] 0 0 0 0 58 58
[4,] 0 0 0 0 58 58
[5,] 0 0 0 0 58 58Next, we will add the ImageName to the images and masks objects. This information is stored in the metadata columns of the CytoImageList objects and is used by cytomapper to match single cell data, images and mask
mcols(images)$ImageName <- gsub("_a0_full_clean", "", names(images))
mcols(masks)$ImageName <- gsub("_a0_full_mask", "", names(masks))We downloaded the full set of segmentation masks. To match the segmentation masks to the corresponding images, we will subset them. As a safety check, we will make sure that the ImageNames of the masks are identical to those of the images.
masks <- masks[mcols(masks)$ImageName %in% mcols(images)$ImageName]
identical(mcols(masks)$ImageName, mcols(images)$ImageName)[1] TRUEFinally, we will use the protein short name as channelNames. Again, we need to make sure that the names match the correct order of the channels.
# Match panel and stack slice information
panel <- panel[panel$full == 1,]
panel <- panel[match(channel.mass[,1], panel$MetalTag),]
# Add channel names to the image stacks CytoImageList object
channelNames(images) <- panel$shortnameHere, we will save the generated CytoImageList objects for convenient access later on.
saveRDS(images, "data/PancreasData/pancreas_images.rds")
saveRDS(masks, "data/PancreasData/pancreas_masks.rds")We will delete all unnecessary files.
file.remove("data/PancreasData/panel.csv", "data/PancreasData/ChannelMass.csv")[1] TRUE TRUE
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.1 LTS
Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] cytomapper_1.2.0 SingleCellExperiment_1.12.0
[3] SummarizedExperiment_1.20.0 Biobase_2.50.0
[5] GenomicRanges_1.42.0 GenomeInfoDb_1.26.0
[7] IRanges_2.24.0 S4Vectors_0.28.0
[9] BiocGenerics_0.36.0 MatrixGenerics_1.2.0
[11] matrixStats_0.57.0 EBImage_4.32.0
[13] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] viridis_0.5.1 viridisLite_0.3.0 svgPanZoom_0.3.4
[4] shiny_1.5.0 sp_1.4-4 GenomeInfoDbData_1.2.4
[7] vipor_0.4.5 tiff_0.1-6 yaml_2.2.1
[10] gdtools_0.2.2 pillar_1.4.6 lattice_0.20-41
[13] glue_1.4.2 digest_0.6.27 RColorBrewer_1.1-2
[16] promises_1.1.1 XVector_0.30.0 colorspace_2.0-0
[19] htmltools_0.5.0 httpuv_1.5.4 Matrix_1.2-18
[22] pkgconfig_2.0.3 raster_3.4-5 zlibbioc_1.36.0
[25] purrr_0.3.4 xtable_1.8-4 fftwtools_0.9-9
[28] scales_1.1.1 svglite_1.2.3.2 whisker_0.4
[31] jpeg_0.1-8.1 later_1.1.0.1 git2r_0.27.1
[34] tibble_3.0.4 generics_0.1.0 ggplot2_3.3.2
[37] ellipsis_0.3.1 magrittr_2.0.1 crayon_1.3.4
[40] mime_0.9 evaluate_0.14 fs_1.5.0
[43] beeswarm_0.2.3 shinydashboard_0.7.1 tools_4.0.3
[46] lifecycle_0.2.0 stringr_1.4.0 munsell_0.5.0
[49] locfit_1.5-9.4 DelayedArray_0.16.0 compiler_4.0.3
[52] systemfonts_0.3.2 rlang_0.4.8 grid_4.0.3
[55] RCurl_1.98-1.2 rstudioapi_0.13 htmlwidgets_1.5.2
[58] bitops_1.0-6 rmarkdown_2.5 gtable_0.3.0
[61] codetools_0.2-18 abind_1.4-5 R6_2.5.0
[64] gridExtra_2.3 knitr_1.30 dplyr_1.0.2
[67] fastmap_1.0.1 rprojroot_2.0.2 stringi_1.5.3
[70] ggbeeswarm_0.6.0 Rcpp_1.0.5 vctrs_0.3.5
[73] png_0.1-7 tidyselect_1.1.0 xfun_0.19