Last updated: 2020-07-08
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Knit directory: cytomapper_publication/
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library(S4Vectors)
library(SingleCellExperiment)
library(cytomapper)Here, a subset of single-cell data, corresponding to 100 images from the full dataset is downloaded.
# Download the zipped folder image and unzip it
url.cells <- ("https://data.mendeley.com/datasets/cydmwsfztj/2/files/f1e3b8dc-56be-4172-bbc4-3a6f9de97563/CellSubset.zip?dl=1")
download.file(url.cells, destfile = "data/PancreasData/CellSubset.zip")
unzip("data/PancreasData/CellSubset.zip", exdir = "data/PancreasData")
file.remove("data/PancreasData/CellSubset.zip")[1] TRUE# Read-in the data
cells <- read.csv("data/PancreasData/CellSubset.csv", stringsAsFactors = FALSE)
# Order the dataset by ImageNumber and ObjectNumber
cells <- cells[order(cells$ImageNumber, cells$ObjectNumber), ]# Download the zipped folder image and unzip it
url.image <- ("https://data.mendeley.com/datasets/cydmwsfztj/2/files/0b236273-d21b-4566-84a2-f1c56324a900/Image.zip?dl=1")
download.file(url.image, destfile = "data/PancreasData/Image.zip")
unzip("data/PancreasData/Image.zip", exdir = "data/PancreasData")
file.remove("data/PancreasData/Image.zip")[1] TRUE# Read-in the data
image <- read.csv("data/PancreasData/All_Image.csv", stringsAsFactors = FALSE)# Download the zipped folder image and unzip it
url.celltypes <- ("https://data.mendeley.com/datasets/cydmwsfztj/2/files/59e8da72-5bfe-4289-b95b-28348a6e1222/CellTypes.zip?dl=1")
download.file(url.celltypes, destfile = "data/PancreasData/CellTypes.zip")
unzip("data/PancreasData/CellTypes.zip", exdir = "data/PancreasData")
file.remove("data/PancreasData/CellTypes.zip")[1] TRUE# Read-in the data
celltypes <- read.csv("data/PancreasData/CellTypes.csv", stringsAsFactors = FALSE)# Download the zipped folder image and unzip it
url.donors <- ("https://data.mendeley.com/datasets/cydmwsfztj/2/files/9074990e-1b93-4c79-8c49-1db01a66398b/Donors.zip?dl=1")
download.file(url.donors, destfile = "data/PancreasData/Donors.zip")
unzip("data/PancreasData/Donors.zip", exdir = "data/PancreasData")
file.remove("data/PancreasData/Donors.zip")[1] TRUE# Read-in the data
donors <- read.csv("data/PancreasData/Donors.csv", stringsAsFactors = FALSE)cell.metadata <- DataFrame(ImageNumber = cells$ImageNumber,
CellNumber = cells$ObjectNumber,
Pos_X = cells$Location_Center_X,
Pos_Y = cells$Location_Center_Y,
ParentIslet = cells$Parent_Islets,
ClosestIslet = cells$Parent_ExpandedIslets,
Area = cells$AreaShape_Area,
NbNeighbours = cells$Neighbors_NumberOfNeighbors_3)image.metadata <- DataFrame(ImageNumber = image$ImageNumber,
ImageFullName = image$FileName_CleanStack,
slide = image$Metadata_Slide,
width = image$Width_CleanStack,
height = image$Height_CleanStack)cell.metadata <- merge(cell.metadata, image.metadata, by="ImageNumber")This information is used by cytomapper to match single-cell data with images and masks
cell.metadata$ImageName <- sub("_a0_full_clean.tiff", "", cell.metadata$ImageFullName)# Add cell ids to cell metadata (format: "ImageName_CellNumber")
cell.metadata$id <- paste(cell.metadata$ImageName, cell.metadata$CellNumber, sep="_")
# Merge cell metadata and cell type information
cell.metadata <- merge(cell.metadata,
celltypes[, c("id", "CellCat", "CellType")],
by="id")cell.metadata <- merge(cell.metadata, donors, by="slide")# Rows are ordered by ImageNumber and CellNumber
cell.metadata <- cell.metadata[order(cell.metadata$ImageNumber, cell.metadata$CellNumber), ]
# Cell ids are used as row names
rownames(cell.metadata) <- cell.metadata$idThe panel contains antibody-related metadata. The channel-mass file is used to match panel information and image stack slices.
# Import panel
url.panel <- ("https://data.mendeley.com/datasets/cydmwsfztj/2/files/2f9fecfc-b98f-4937-bc38-ae1b959bd74d/Panel.csv?dl=1")
download.file(url.panel, destfile = "data/PancreasData/panel.csv")
panel <- read.csv("data/PancreasData/panel.csv")
# Import channel-mass file
url.channelmass <- ("https://data.mendeley.com/datasets/cydmwsfztj/2/files/704312eb-377c-42e2-8227-44bb9aca0fb3/ChannelMass.csv?dl=1")
download.file(url.channelmass, destfile = "data/PancreasData/ChannelMass.csv")
channel.mass <- read.csv("data/PancreasData/ChannelMass.csv", header = FALSE)# Match panel and stack slice information
panel <- panel[panel$full == 1,]
panel <- panel[match(channel.mass[,1], panel$MetalTag),]
# Add short protein names as panel rownames
rownames(panel) <- panel$shortnameHere, we import the mean intensity per cell
cur_counts <- cells[, grepl("Intensity_MeanIntensity_CleanStack", colnames(cells))]channelNumber <- as.numeric(sub("^.*_c", "", colnames(cur_counts)))
cur_counts <- cur_counts[, order(channelNumber, decreasing = FALSE)]sce <- SingleCellExperiment(assays = list(counts = t(as.matrix(cur_counts))))exprs = asinh-transformed counts
assay(sce, "exprs") <- asinh(counts(sce)/1)rownames(sce) <- rownames(panel)
colnames(sce) <- rownames(cell.metadata)colData(sce) <- cell.metadata
rowData(sce) <- panel
sceclass: SingleCellExperiment
dim: 38 252059
metadata(0):
assays(2): counts exprs
rownames(38): H3 SMA ... Ir191 Ir193
rowData names(15): TubeNb MetalTag ... miCAT2 miCAT
colnames(252059): E02_1 E02_2 ... J34_1149 J34_1150
colData names(26): slide id ... Ethnicity BMI
reducedDimNames(0):
altExpNames(0):saveRDS(sce, "data/PancreasData/pancreas_sce.rds")file.remove("data/PancreasData/All_Image.csv",
"data/PancreasData/CellSubset.csv",
"data/PancreasData/CellTypes.csv",
"data/PancreasData/Donors.csv",
"data/PancreasData/panel.csv",
"data/PancreasData/ChannelMass.csv")[1] TRUE TRUE TRUE TRUE TRUE TRUE
sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.5
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] cytomapper_1.1.1 EBImage_4.30.0
[3] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.1
[5] DelayedArray_0.14.0 matrixStats_0.56.0
[7] Biobase_2.48.0 GenomicRanges_1.40.0
[9] GenomeInfoDb_1.24.2 IRanges_2.22.2
[11] S4Vectors_0.26.1 BiocGenerics_0.34.0
[13] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4.6 locfit_1.5-9.4 lattice_0.20-41
[4] fftwtools_0.9-8 png_0.1-7 rprojroot_1.3-2
[7] digest_0.6.25 R6_2.4.1 tiff_0.1-5
[10] backports_1.1.7 evaluate_0.14 ggplot2_3.3.1
[13] pillar_1.4.4 zlibbioc_1.34.0 rlang_0.4.6
[16] whisker_0.4 raster_3.1-5 Matrix_1.2-18
[19] rmarkdown_2.2 stringr_1.4.0 htmlwidgets_1.5.1
[22] RCurl_1.98-1.2 munsell_0.5.0 compiler_4.0.0
[25] httpuv_1.5.4 xfun_0.14 pkgconfig_2.0.3
[28] htmltools_0.4.0 tidyselect_1.1.0 gridExtra_2.3
[31] tibble_3.0.1 GenomeInfoDbData_1.2.3 codetools_0.2-16
[34] viridisLite_0.3.0 crayon_1.3.4 dplyr_1.0.0
[37] later_1.1.0.1 bitops_1.0-6 grid_4.0.0
[40] gtable_0.3.0 lifecycle_0.2.0 git2r_0.27.1
[43] magrittr_1.5 scales_1.1.1 stringi_1.4.6
[46] XVector_0.28.0 viridis_0.5.1 fs_1.4.1
[49] promises_1.1.1 sp_1.4-2 generics_0.0.2
[52] ellipsis_0.3.1 vctrs_0.3.1 RColorBrewer_1.1-2
[55] tools_4.0.0 glue_1.4.1 purrr_0.3.4
[58] jpeg_0.1-8.1 abind_1.4-5 yaml_2.2.1
[61] colorspace_1.4-1 knitr_1.28