Last updated: 2020-11-18
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Knit directory: cytomapper_publication/
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This script downloads the single-cell data from the pancreas IMC dataset available here. The dataset is associated to the following publication:
After obtaining the raw data as produced by the IMC segmentation pipeline, we will further process them to create a SingleCellExperiment object.
Here, a subset of single-cell data, corresponding to 100 images from the full dataset is downloaded.
We read in the single-cell meta- and expression data and order them based on the image and cell number.
library(S4Vectors)
library(SingleCellExperiment)
library(cytomapper)
# Download the zipped folder image and unzip it
url.cells <- ("https://data.mendeley.com/public-files/datasets/cydmwsfztj/files/f1e3b8dc-56be-4172-bbc4-3a6f9de97563/file_downloaded")
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), ]Next, we will read in the image-specific metadata.
# Download the zipped folder image and unzip it
url.image <- ("https://data.mendeley.com/public-files/datasets/cydmwsfztj/files/0b236273-d21b-4566-84a2-f1c56324a900/file_downloaded")
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)In the original publication, cells were phenotyped based on informative marker expression. These phenotype labels are supplied in the online repository.
# Download the zipped folder image and unzip it
url.celltypes <- ("https://data.mendeley.com/public-files/datasets/cydmwsfztj/files/59e8da72-5bfe-4289-b95b-28348a6e1222/file_downloaded")
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)We will furthermore read in the metadata per donor.
# Download the zipped folder image and unzip it
url.donors <- ("https://data.mendeley.com/public-files/datasets/cydmwsfztj/files/9074990e-1b93-4c79-8c49-1db01a66398b/file_downloaded")
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)In this part of the workflow, we will select cell-specific metadata and collect them in a single DataFrame. This will be the colData entry of the final SingleCellExperiment object.
The cell-specific metadata can be obtained from the cells object.
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)Specific image metadata can be obtained from column-entries of the image object.
image.metadata <- DataFrame(ImageNumber = image$ImageNumber,
ImageFullName = image$FileName_CleanStack,
slide = image$Metadata_Slide,
width = image$Width_CleanStack,
height = image$Height_CleanStack)We will next merge the cell- and image-specific metadata.
cell.metadata <- merge(cell.metadata, image.metadata, by="ImageNumber")We will also add the ImageName entry to the metadata object. This information is used by cytomapper to match single-cell data with images and masks.
cell.metadata$ImageName <- sub("_a0_full_clean.tiff", "", cell.metadata$ImageFullName)We will now add the cell-type information to the metadata object.
# Add cell ids to cell metadata (format: "ImageName_CellNumber")
cell.metadata$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")We will add the donor information to the metadata object.
cell.metadata <- merge(cell.metadata, donors, by="slide")Finally, we order the cell-metadata object based on ImageNumber and CellNumber and add rownames.
# 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$idHere, we will download the panel information, which contains antibody-related metadata. However, for some datasets, the channel-order and the panel order do not match. For this, the channel-mass file is used to match panel information and image stack slices.
# Import panel
url.panel <- ("https://data.mendeley.com/public-files/datasets/cydmwsfztj/files/2f9fecfc-b98f-4937-bc38-ae1b959bd74d/file_downloaded")
download.file(url.panel, destfile = "data/PancreasData/panel.csv")
panel <- read.csv("data/PancreasData/panel.csv")
# Import channel-mass file
url.channelmass <- ("https://data.mendeley.com/public-files/datasets/cydmwsfztj/files/704312eb-377c-42e2-8227-44bb9aca0fb3/file_downloaded")
download.file(url.channelmass, destfile = "data/PancreasData/ChannelMass.csv")
channel.mass <- read.csv("data/PancreasData/ChannelMass.csv", header = FALSE)First, we will select the relevant channels used for analysis and order the pannel based on channel mass.
# Match panel and stack slice information
panel <- panel[panel$full == 1,]
panel <- panel[match(channel.mass[,1], panel$MetalTag),]
# Add short protein names as panel rownames
rownames(panel) <- panel$shortnameCellProfiler measures a number of different statistics per marker and cell. We will select the mean intensity per channel and per cell to obtain single-cell expression counts.
cur_counts <- cells[, grepl("Intensity_MeanIntensity_CleanStack", colnames(cells))]Next, we will reorder the channels based on channel number.
channelNumber <- as.numeric(sub("^.*_c", "", colnames(cur_counts)))
cur_counts <- cur_counts[, order(channelNumber, decreasing = FALSE)]We have now obtained all metadata and feature data to create the SingleCellExperiment object. We will first create it is based on the raw expression counts.
sce <- SingleCellExperiment(assays = list(counts = t(as.matrix(cur_counts))))Furthermore, we will store the arcsinh-transformed (using a co-factor of 1) counts in the exprs assay slot.
assay(sce, "exprs") <- asinh(counts(sce)/1)Now, we will set the dimnames of the object.
rownames(sce) <- rownames(panel)
colnames(sce) <- rownames(cell.metadata)Finally, we will store the marker- and cell-specific metadata in the SingleCellExperiment object. Here, columns are cells and rows are markers.
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):For further analysis, we will save the SingleCellExperiment object.
saveRDS(sce, "data/PancreasData/pancreas_sce.rds")Finally, we remove the downloaded objects to save storage space.
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 4.0.3 (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 EBImage_4.32.0
[3] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[5] Biobase_2.50.0 GenomicRanges_1.42.0
[7] GenomeInfoDb_1.26.0 IRanges_2.24.0
[9] MatrixGenerics_1.2.0 matrixStats_0.57.0
[11] S4Vectors_0.28.0 BiocGenerics_0.36.0
[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