In BodenmillerGroup/imcdatasets: Collection of publicly available imaging mass cytometry (IMC) datasets
library(BiocStyle)
knitr::opts_chunk$set(error = FALSE, message = FALSE, warning = FALSE)
knitr::opts_knit$set(root.dir = file.path("..", "extdata"))
Introduction
This script downloads two example Imaging Mass Cytometry (IMC) datasets (RNA and protein), each comprising fifty corresponding images and the associated cell segmentation masks and single cell data. The datasets are described in the following publication:
All data are available from zenodo. After obtaining the raw data, we will further process them to create a SingleCellExperiment object. We will then use the cytomapper package to read in the images and masks and create CytoImageList objects.
Settings
library(data.table)
library(dplyr)
library(S4Vectors)
library(SingleCellExperiment)
library(cytomapper)
dataset_name <- "HochSchulz_2022_Melanoma"
dataset_version <- "v1"
cat("Dataset version:", dataset_version)
Set the working and output directories
# Temporary directory to unzip files
workdir <- tempdir()
Sys.setenv(workdir = workdir)
# Output directory
dataset_dir <- file.path(".", dataset_name)
if(!(dir.exists(dataset_dir))) dir.create(dataset_dir)
outdir <- file.path(dataset_dir, dataset_version)
if(!(dir.exists(outdir))) dir.create(outdir)
# Increase timeout period so that large files can be downloaded
timeout <- getOption('timeout')
options(timeout = 1000)
Single cell data
Download single cell data
Here, a subset of single-cell data corresponding to two datasets (protein and RNA) from [@HochSchulz-2022-Melanoma] is downloaded.
# Download the zipped folder
url_data <- "https://zenodo.org/record/5994136/files/data_for_analysis.zip"
zip_name <- "sceFiles"
system2("curl", args = c(
"-o",
file.path(workdir, paste0(zip_name, ".zip")),
url_data,
"--max-time 1000"))
# Unzip SCE files
system2("unzip", args = c(
"-o", file.path(workdir, paste0(zip_name, ".zip")),
"*sce_*.rds",
"-d", workdir))
file.remove(file.path(workdir, paste0(zip_name, ".zip")))
Read in single-cell data
We read in the SingleCellExperiment objects from both datasets and subset 50 (corresponding) images based on the highest fraction of B cells.
# RNA dataset
sce_rna <- readRDS(
file.path(workdir, "data_for_analysis_zenodo/sce_RNA.rds"))
# Protein dataset
sce_protein <- readRDS(
file.path(workdir, "data_for_analysis_zenodo/sce_protein.rds"))
Subset images
Select the top 50 images with the most B cells.
images_to_keep <- as.data.frame(colData(sce_protein)) %>%
group_by(Description, celltype) %>%
summarise(numberOfcells = n(), .groups = "keep") %>%
filter(celltype == "B cell") %>%
ungroup() %>%
slice_max(numberOfcells, n = 50) %>%
pull(Description)
Prepare data
Cell-level metadata
Here, we rename the columns of the colData entry for consistency with the other datasets.
renaming_vector <- c(
cell_id = "cellID",
cell_number = "CellNumber",
image_number = "ImageNumber",
cell_x = "Center_X",
cell_y = "Center_Y",
cell_type = "celltype",
cell_cluster = "celltype_clustered",
cell_area = "Area",
cell_major_axis_length = "MajorAxisLength",
cell_minor_axis_length = "MinorAxisLength",
cell_in_tumour = "in_tumor",
milieu_Bcell_patch_score = "bcell_patch_score",
milieu_Tcell_density_score = "Tcell_density_score_image",
milieu_dysfunction_score = "dysfunction_score",
milieu_dysfunction_density = "dysfunction_density",
neighbors_number = "NumberOfNeighbors",
sample_block_id = "BlockID",
sample_location = "Location",
sample_description = "Description",
sample_tissue_type = "TissueType",
sample_MM_location = "MM_location",
sample_MM_location_short = "MM_location_simplified",
patient_id = "PatientID",
patient_age = "Age",
patient_gender = "Gender",
patient_cancer_stage = "Cancer_Stage",
patient_adjuvant_therapy = "Adjuvant",
patient_E_I_D = "E_I_D",
patient_mutation = "Mutation",
patient_status_at_3months = "Status_at_3m",
patient_relapse = "relapse",
patient_treatment_status_before_surgery = "treatment_status_before_surgery",
patient_treatment_group_before_surgery = "treatment_group_before_surgery",
patient_treatment_group_after_surgery = "treatment_group_after_surgery",
patient_death = "Death",
patient_death_date = "Date_death"
)
renaming_vector_protein = c(
cell_parent_nucleus = "Parent_nuclei",
milieu_Bcell_patch = "bcell_patch",
milieu_Bcell_milieu = "bcell_milieu",
milieu_TCF7 = "TCF7",
milieu_PD1 = "PD1"
)
renaming_vector_rna = c(
cell_type_rf = "celltype_rf",
cell_annotation = "cellAnnotation",
chemokine_expressor = "expressor"
)
# Rename protein dataset
protein_coldata <- as_tibble(colData(sce_protein))
protein_coldata <- protein_coldata %>%
dplyr::rename(all_of(renaming_vector)) %>%
dplyr::rename(all_of(renaming_vector_protein)) %>%
as("DataFrame")
protein_coldata$cell_id <- gsub("protein_", "", protein_coldata$cell_id)
protein_coldata$image_name <- as.character(protein_coldata$image_number)
rownames(protein_coldata) <- protein_coldata$cell_id
# Rename RNA dataset
rna_coldata <- as_tibble(colData(sce_rna))
rna_coldata <- rna_coldata %>%
dplyr::rename(all_of(renaming_vector)) %>%
dplyr::rename(all_of(renaming_vector_rna)) %>%
as("DataFrame")
rna_coldata$cell_id <- gsub("RNA_", "", rna_coldata$cell_id)
rna_coldata$image_name <- as.character(rna_coldata$image_number)
rownames(rna_coldata) <- rna_coldata$cell_id
Marker metadata
Here, we rename the columns of the rowData entry for consistency with the other datasets.
renaming_vector <- c(
channel = "channel",
metal = "Metal.Tag",
name = "Target",
short_name = "clean_target",
marker_class = "marker_class",
antibody_clone = "Antibody.Clone",
antibody_tube_number = "Tube.Number",
antibody_stock_conc = "Stock.Concentration",
antibody_final_conc_or_dilution = "Final.Concentration...Dilution",
antibody_ul_to_add = "uL.to.add",
full = "full",
keep = "good_marker",
ilastik = "ilastik"
)
# Rename protein dataset
protein_rowdata <- as_tibble(rowData(sce_protein))
protein_rowdata <- protein_rowdata %>%
dplyr::rename(all_of(renaming_vector)) %>%
dplyr::rename(c(tumor_mask = "tumorMask")) %>%
as.data.table()
protein_rowdata$full_name <- protein_rowdata$name
# Rename RNA dataset
rna_rowdata <- as_tibble(rowData(sce_rna))
rna_rowdata <- rna_rowdata %>%
dplyr::rename(all_of(renaming_vector)) %>%
dplyr::rename(c(marker_no_RNA = "noRNA_marker")) %>%
as.data.table()
rna_rowdata$full_name <- rna_rowdata$name
We also rename markers for consistency with other datasets.
# Protein dataset
protein_rowdata[metal == "Cd111", `:=` (full_name = "Vimentin",
short_name = "VIM")]
protein_rowdata[metal == "Cd112", `:=` (short_name = "CAV1")]
protein_rowdata[metal == "In113", `:=` (short_name = "H3")]
protein_rowdata[metal == "In115", `:=` (full_name = "Smooth muscle actin")]
protein_rowdata[metal == "Nd142", `:=` (full_name = "CXCR2 IL-8 RB")]
protein_rowdata[metal == "Nd143", `:=` (short_name = "HLA_DR")]
protein_rowdata[metal == "Sm147", `:=` (full_name = "SOX-9",
short_name = "SOX9")]
protein_rowdata[metal == "Eu151", `:=` (
full_name = "phospho-ERK1/2 [T202/Y204]",
short_name = "p_ERK1_2")]
protein_rowdata[metal == "Sm152", `:=` (full_name = "CD3 epsilon",
short_name = "CD3e")]
protein_rowdata[metal == "Gd155", `:=` (
full_name = "Programmed cell death protein 1",
short_name = "PD_1")]
protein_rowdata[metal == "Gd156", `:=` (short_name = "MITF")]
protein_rowdata[metal == "Tb159", `:=` (short_name = "GZMB")]
protein_rowdata[metal == "Gd160", `:=` (full_name = "Programmed death-ligand 1",
short_name = "PD_L1")]
protein_rowdata[metal == "Dy163", `:=` (full_name = "Forkhead box P3")]
protein_rowdata[metal == "Dy164", `:=` (
full_name = "Inducible T-cell costimulator",
short_name = "ICOS")]
protein_rowdata[metal == "Ho165", `:=` (full_name = "Beta-catenin",
short_name = "CTNNB1")]
protein_rowdata[metal == "Er166", `:=` (full_name = "CD8 alpha",
short_name = "CD8a")]
protein_rowdata[metal == "Er167", `:=` (
short_name = "COL1A1",
antibody_clone = "polyclonal_Collagen I")]
protein_rowdata[metal == "Er168", `:=` (full_name = "Ki-67_Er168",
short_name = "Ki67_Er168")]
protein_rowdata[metal == "Er170", `:=` (full_name = "phospho-S6 [S235/S236]",
short_name = "p_S6")]
protein_rowdata[metal == "Yb172", `:=` (
full_name = "Indoleamine 2,3-dioxygenase 1")]
protein_rowdata[metal == "Yb173", `:=` (full_name = "SOX-10",
short_name = "SOX10")]
protein_rowdata[metal == "Yb174", `:=` (
antibody_clone = "polyclonal_DLEC_CLEC4C_BDCA-2")]
protein_rowdata[metal == "Lu175", `:=` (full_name = "CD206")]
protein_rowdata[metal == "Yb176", `:=` (full_name = "cleaved-PARP",
short_name = "c_PARP")]
protein_rowdata[metal == "Ir191", `:=` (full_name = "Iridum 191",
antibody_clone = NA)]
protein_rowdata[metal == "Ir193", `:=` (full_name = "Iridium 193",
antibody_clone = NA)]
protein_rowdata[metal == "Pt198", `:=` (full_name = "Ki-67_Pt198",
short_name = "Ki67_Pt198")]
protein_rowdata[metal == "Y89", `:=` (full_name = "Myeloperoxidase",
antibody_clone = "polyclonal_MPO")]
# RNA dataset
rna_rowdata[metal == "Cd111", `:=` (full_name = "Vimentin",
short_name = "VIM")]
rna_rowdata[metal == "In113", `:=` (short_name = "H3")]
rna_rowdata[metal == "In115", `:=` (full_name = "Smooth muscle actin")]
rna_rowdata[metal == "Pr141", `:=` (full_name = "Cytokeratin 5",
short_name = "KRT5")]
rna_rowdata[metal == "Nd143", `:=` (short_name = "HLA_DR")]
rna_rowdata[metal == "Nd145", `:=` (full_name = "Cadherin-11",
short_name = "CDH11")]
rna_rowdata[metal == "Nd146", `:=` (
full_name = "Fibroblast activation protein alpha",
short_name = "FAP")]
rna_rowdata[metal == "Nd148", `:=` (full_name = "Beta-2-microglobulin")]
rna_rowdata[metal == "Eu151", `:=` (full_name = "Glucose transporter 1",
short_name = "SLC2A12")]
rna_rowdata[metal == "Sm152", `:=` (full_name = "CD3 epsilon",
short_name = "CD3e")]
rna_rowdata[metal == "Eu153", `:=` (
full_name = "Lymphocyte activation gene 3 protein",
short_name = "LAG3")]
rna_rowdata[metal == "Gd155", `:=` (
full_name = "Programmed cell death protein 1",
short_name = "PD_1")]
rna_rowdata[metal == "Gd156", `:=` (short_name = "CCL4_mRNA",
antibody_clone = NA)]
rna_rowdata[metal == "Gd158", `:=` (short_name = "CCL18_mRNA",
antibody_clone = NA)]
rna_rowdata[metal == "Tb159", `:=` (full_name = "C-C chemokine receptor type 2",
short_name = "CCR2")]
rna_rowdata[metal == "Gd160", `:=` (full_name = "Programmed death-ligand 1",
short_name = "PD_L1")]
rna_rowdata[metal == "Dy161", `:=` (short_name = "CXCL8_mRNA",
antibody_clone = NA)]
rna_rowdata[metal == "Dy162", `:=` (short_name = "CXCL10_mRNA",
antibody_clone = NA)]
rna_rowdata[metal == "Dy163", `:=` (short_name = "CXCL12_mRNA",
antibody_clone = NA)]
rna_rowdata[metal == "Dy164", `:=` (short_name = "CXCL13_mRNA",
antibody_clone = NA)]
rna_rowdata[metal == "Ho165", `:=` (full_name = "CD8 alpha",
short_name = "CD8a")]
rna_rowdata[metal == "Er166", `:=` (short_name = "CCL2_mRNA",
antibody_clone = NA)]
rna_rowdata[metal == "Er167", `:=` (short_name = "CCL22_mRNA",
antibody_clone = NA)]
rna_rowdata[metal == "Er168", `:=` (short_name = "CXCL9_mRNA",
antibody_clone = NA)]
rna_rowdata[metal == "Tm169", `:=` (short_name = "DapB_mRNA",
antibody_clone = NA)]
rna_rowdata[metal == "Er170", `:=` (full_name = "SOX-10",
short_name = "SOX10")]
rna_rowdata[metal == "Yb171", `:=` (short_name = "CCL8_mRNA",
antibody_clone = NA)]
rna_rowdata[metal == "Yb173", `:=` (short_name = "CCL19_mRNA",
antibody_clone = NA)]
rna_rowdata[metal == "Yb174", `:=` (full_name = "MelanA / MART1",
short_name = "MLANA")]
rna_rowdata[metal == "Lu175", `:=` (full_name = "phopsho-Rb [S807/S811]",
short_name = "p_Rb")]
rna_rowdata[metal == "Yb176", `:=` (full_name = "cleaved-PARP",
short_name = "c_PARP")]
rna_rowdata[metal == "Ir191", `:=` (full_name = "Iridium 191",
antibody_clone = NA)]
rna_rowdata[metal == "Ir193", `:=` (full_name = "Iridium 193",
antibody_clone = NA)]
rna_rowdata[metal == "Y89", `:=` (full_name = "Myeloperoxidase",
antibody_clone = "polyclonal_MPO")]
We convert the panel table to a DataFrame and add target short_names as row names.
protein_rowdata <- as(protein_rowdata, "DataFrame")
rownames(protein_rowdata) <- protein_rowdata$short_name
rna_rowdata <- as(rna_rowdata, "DataFrame")
rownames(rna_rowdata) <- rna_rowdata$short_name
Update SingleCellExperiment object
We now update the SingleCellExperiment objects with new rowData and colData entries.
# Protein dataset
colData(sce_protein) <- protein_coldata
rowData(sce_protein) <- protein_rowdata
# RNA dataset
colData(sce_rna) <- rna_coldata
rowData(sce_rna) <- rna_rowdata
Finally, we rename the row and column names of the SingleCellExperiment objects.
# Protein dataset
rownames(sce_protein) <- rownames(protein_rowdata)
colnames(sce_protein) <- rownames(protein_coldata)
# RNA dataset
rownames(sce_rna) <- rownames(rna_rowdata)
colnames(sce_rna) <- rownames(rna_coldata)
# MainExpName
mainExpName(sce_protein) <- paste(
dataset_name, "protein", "FULL", dataset_version, sep = "_")
mainExpName(sce_rna) <- paste(
dataset_name, "RNA", "FULL", dataset_version, sep = "_")
We also rename the assays for consistency with other datasets.
assayNames(sce_protein) <- c("counts", "exprs", "scaled_counts", "scaled_exprs")
assayNames(sce_rna) <- c("counts", "exprs", "scaled_counts", "scaled_exprs")
Save on disk
We save the SingleCellExperiment objects for upload to r Biocpkg("ExperimentHub").
saveRDS(sce_protein, file.path(outdir, "sce_full_protein.rds"))
print(sce_protein)
saveRDS(sce_rna, file.path(outdir, "sce_full_rna.rds"))
print(sce_rna)
Subset
We also save subsets of the SingleCellExperiment objects, so they can be
matched with the multichannel image objects we are going to generate below.
The subset corresponds to the top 50 image with most B cells.
# Subset
sce_protein_sub <- sce_protein[
, sce_protein$sample_description %in% images_to_keep]
sce_rna_sub <- sce_rna[
, sce_rna$sample_description %in% images_to_keep]
# Rename
mainExpName(sce_protein_sub) <- paste(
dataset_name, "protein", dataset_version, sep = "_")
mainExpName(sce_rna_sub) <- paste(
dataset_name, "RNA", dataset_version, sep = "_")
# Save
saveRDS(sce_protein_sub, file.path(outdir, "sce_protein.rds"))
print(sce_protein_sub)
saveRDS(sce_rna_sub, file.path(outdir, "sce_rna.rds"))
print(sce_rna_sub)
Clean up
Finally, we remove the downloaded files and generated objects to save storage space.
remove(protein_coldata, protein_rowdata, rna_coldata, rna_rowdata)
unlink(file.path(workdir, "data_for_analysis_zenodo"), recursive = TRUE)
Images and cell masks
Import images and masks - Protein dataset
Download the dataset
Here, we download a subset of 50 images from the zenodo repository.
# Download the zipped folder image and unzip it
system2("curl", args = c(
"-o",
file.path(workdir, "ImageSubset.zip"),
"https://zenodo.org/record/6004986/files/full_data.zip?download=1",
"--max-time 900"))
# Unzip selected files
system2("unzip", args = c(
"-o", file.path(workdir, "ImageSubset.zip"),
"full_data/protein/cpout/*.tiff",
"-d", workdir))
system2("unzip", args = c(
"-o", file.path(workdir, "ImageSubset.zip"),
"full_data/protein/cpout/Image.csv",
"-d", workdir))
We select the fifty images that correspond to the dat in the SingleCellExperiment objects.
protein_directory = file.path(
workdir, file.path("full_data", "protein", "cpout"))
protein_info <- fread(file.path(protein_directory, "Image.csv"))
protein_info_sub <- protein_info[
protein_info$Metadata_Description %in% images_to_keep, ]
Multichannel images
We use the loadImages function of the cytomapper package to read the images into a CytoImageList object.
images_protein <- loadImages(
protein_directory, pattern = protein_info_sub$FileName_SpillCorrected)
Cell segmentation masks
We also read the masks into a CytoImageList object.
masks_protein <- loadImages(
protein_directory, pattern = protein_info$FileName_cellmask)
Clean-up
We remove the downloaded image and mask tiff files to save storage space.
files_to_delete <- list.files(protein_directory, full.names = TRUE)
file.remove(files_to_delete)
Import images and masks - RNA dataset
Unzip the dataset
The dataset has been already been downloaded from zenodo, together with the protein dataset.
system2("unzip", args = c(
"-o", file.path(workdir, "ImageSubset.zip"),
"full_data/rna/cpout/*.tiff",
"-d", workdir))
system2("unzip", args = c(
"-o", file.path(workdir, "ImageSubset.zip"),
"full_data/rna/cpout/Image.csv",
"-d", workdir))
# Clean-up
file.remove(file.path(workdir, "ImageSubset.zip"))
We select the fifty images that correspond to the dat in the SingleCellExperiment objects.
rna_directory <- file.path(workdir, file.path("full_data", "rna", "cpout"))
rna_info <- fread(file.path(rna_directory, "Image.csv"))
rna_info_sub <- rna_info[rna_info$Metadata_Description %in% images_to_keep, ]
Multichannel images
We use the loadImages function of the cytomapper package to read the images into a CytoImageList object.
images_rna <- loadImages(
rna_directory, pattern = rna_info_sub$FileName_SpillCorrected)
Cell segmentation masks
We also read the masks into a CytoImageList object.
masks_rna <- loadImages(
rna_directory, pattern = rna_info$FileName_cellmask)
Clean-up
We remove the downloaded image and mask tiff files to save storage space.
files_to_delete <- list.files(rna_directory, full.names = TRUE)
file.remove(files_to_delete)
Prepare images and masks
We will now process the images and masks to make them compatible with the cytomapper package.
Rescale masks
The masks are 16-bit images and need to be re-scaled in order to obtain integer cell ids.
masks_protein <- scaleImages(masks_protein,
value = protein_info$Scaling_cellmask[[1]])
masks_rna <- scaleImages(masks_rna,
value = rna_info$Scaling_cellmask[[1]])
Add image names and numbers
Next, we add image names to the images and masks objects, these names correspond to the image_name column in colData(sce_*). This information is stored in the metadata columns of the CytoImageList objects and is used by cytomapper to match single cell data, images and masks.
# We extract the file names of the masks into data frames
df_masks_protein <- data.frame(
cell_mask = protein_info$FileName_cellmask,
image_number = protein_info$ImageNumber,
description = protein_info$Metadata_Description)
df_images_protein <- data.frame(
cell_mask = protein_info_sub$FileName_SpillCorrected,
image_number = protein_info_sub$ImageNumber,
description = protein_info_sub$Metadata_Description)
# We match the data frames row names with the names of the 'masks' object
rownames(df_masks_protein) <- gsub(".tiff", "",
protein_info$FileName_cellmask)
rownames(df_images_protein) <- gsub(".tiff", "",
protein_info_sub$FileName_SpillCorrected)
# We add the extracted information to the metadata of the 'masks' object
mcols(masks_protein) <- df_masks_protein[names(masks_protein), ]
mcols(images_protein) <- df_images_protein[names(images_protein), ]
# Add image names
mcols(masks_protein)$image_name <- as.character(
mcols(masks_protein)$image_number)
mcols(images_protein)$image_name <- as.character(
mcols(images_protein)$image_number)
names(masks_protein) <- mcols(masks_protein)$image_name
names(images_protein) <- mcols(images_protein)$image_name
# Add dataset specification
mcols(images_protein)$dataset <- rep("protein", nrow(mcols(images_protein)))
mcols(masks_protein)$dataset <- rep("protein", nrow(mcols(masks_protein)))
# We extract the file names of the masks into data frames
df_masks_rna <- data.frame(
cell_mask = rna_info$FileName_cellmask,
image_number = rna_info$ImageNumber,
description = rna_info$Metadata_Description)
df_images_rna <- data.frame(
cell_mask = rna_info_sub$FileName_SpillCorrected,
image_number = rna_info_sub$ImageNumber,
description = rna_info_sub$Metadata_Description)
# We match the data frames row names with the names of the 'masks' object
rownames(df_masks_rna) <- gsub(".tiff", "", rna_info$FileName_cellmask)
rownames(df_images_rna) <- gsub(".tiff", "",
rna_info_sub$FileName_SpillCorrected)
# We add the extracted information to the metadata of the 'masks' object
mcols(masks_rna) <- df_masks_rna[names(masks_rna), ]
mcols(images_rna) <- df_images_rna[names(images_rna), ]
# Add image names
mcols(masks_rna)$image_name <- as.character(
mcols(masks_rna)$image_number)
mcols(images_rna)$image_name <- as.character(
mcols(images_rna)$image_number)
names(masks_rna) <- mcols(masks_rna)$image_name
names(images_rna) <- mcols(images_rna)$image_name
# Add dataset specification
mcols(images_rna)$dataset <- rep("RNA", nrow(mcols(images_rna)))
mcols(masks_rna)$dataset <- rep("RNA", nrow(mcols(masks_rna)))
Sanity check
identical(mcols(masks_rna)$Description, mcols(images_rna)$Description)
identical(mcols(masks_protein)$Description, mcols(images_protein)$Description)
Add channel names
Finally, we will add protein short names as channel names of the images objects with , corresponding to the row names of the SingleCellExperiment objects and to the short_name column of rowData(sce).
# Protein dataset
panel_protein <- rowData(sce_protein)
panel_protein <- panel_protein[order(panel_protein$channel), ]
channelNames(images_protein) <- rownames(panel_protein)
# RNA dataset
panel_rna <- rowData(sce_rna)
panel_rna <- panel_rna[order(panel_rna$channel), ]
channelNames(images_rna) <- rownames(panel_rna)
Save masks on disk
Finally, we will save the generated CytoImageList images and masks objects for uploading to r Biocpkg("ExperimentHub").
saveRDS(masks_protein, file.path(outdir, "masks_full_protein.rds"))
print(masks_protein)
saveRDS(masks_rna, file.path(outdir, "masks_full_rna.rds"))
print(masks_rna)
We also subset the mask objects to match the multichannel image objects.
# Subset
masks_protein_sub <- masks_protein[
mcols(masks_protein)$image_name %in% mcols(images_protein)$image_name, ]
masks_rna_sub <- masks_rna[
mcols(masks_rna)$image_name %in% mcols(images_rna)$image_name, ]
# Save
saveRDS(masks_protein_sub, file.path(outdir, "masks_protein.rds"))
print(masks_protein_sub)
saveRDS(masks_rna_sub, file.path(outdir, "masks_rna.rds"))
print(masks_rna_sub)
remove(sce_protein, sce_protein_sub, sce_rna, sce_rna_sub,
masks_protein, masks_protein_sub, masks_rna, masks_rna_sub)
gc()
Save images on disk
# RNA dataset
saveRDS(images_rna, file.path(outdir, "images_rna.rds"))
print(images_rna)
remove(images_rna)
gc()
# Protein dataset
saveRDS(images_protein, file.path(outdir, "images_protein.rds"))
print(images_protein)
Clean up
Remove all files from the temporary working directory.
downloaded_files <- list.files(workdir)
downloaded_files <- downloaded_files[!downloaded_files %in% "BiocStyle"]
unlink(file.path(workdir, downloaded_files), recursive = TRUE)
# Reset original timeout value
options(timeout = timeout)
Session information
sessionInfo()
References
BodenmillerGroup/imcdatasets documentation built on March 20, 2024, 9:24 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
library(BiocStyle) knitr::opts_chunk$set(error = FALSE, message = FALSE, warning = FALSE) knitr::opts_knit$set(root.dir = file.path("..", "extdata"))
Introduction
This script downloads two example Imaging Mass Cytometry (IMC) datasets (RNA and protein), each comprising fifty corresponding images and the associated cell segmentation masks and single cell data. The datasets are described in the following publication:
All data are available from zenodo. After obtaining the raw data, we will further process them to create a SingleCellExperiment object. We will then use the cytomapper package to read in the images and masks and create CytoImageList objects.
Settings
library(data.table) library(dplyr) library(S4Vectors) library(SingleCellExperiment) library(cytomapper)
dataset_name <- "HochSchulz_2022_Melanoma" dataset_version <- "v1" cat("Dataset version:", dataset_version)
Set the working and output directories
# Temporary directory to unzip files workdir <- tempdir() Sys.setenv(workdir = workdir) # Output directory dataset_dir <- file.path(".", dataset_name) if(!(dir.exists(dataset_dir))) dir.create(dataset_dir) outdir <- file.path(dataset_dir, dataset_version) if(!(dir.exists(outdir))) dir.create(outdir) # Increase timeout period so that large files can be downloaded timeout <- getOption('timeout') options(timeout = 1000)
Single cell data
Download single cell data
Here, a subset of single-cell data corresponding to two datasets (protein and RNA) from [@HochSchulz-2022-Melanoma] is downloaded.
# Download the zipped folder url_data <- "https://zenodo.org/record/5994136/files/data_for_analysis.zip" zip_name <- "sceFiles" system2("curl", args = c( "-o", file.path(workdir, paste0(zip_name, ".zip")), url_data, "--max-time 1000")) # Unzip SCE files system2("unzip", args = c( "-o", file.path(workdir, paste0(zip_name, ".zip")), "*sce_*.rds", "-d", workdir)) file.remove(file.path(workdir, paste0(zip_name, ".zip")))
Read in single-cell data
We read in the SingleCellExperiment objects from both datasets and subset 50 (corresponding) images based on the highest fraction of B cells.
# RNA dataset sce_rna <- readRDS( file.path(workdir, "data_for_analysis_zenodo/sce_RNA.rds")) # Protein dataset sce_protein <- readRDS( file.path(workdir, "data_for_analysis_zenodo/sce_protein.rds"))
Subset images
Select the top 50 images with the most B cells.
images_to_keep <- as.data.frame(colData(sce_protein)) %>% group_by(Description, celltype) %>% summarise(numberOfcells = n(), .groups = "keep") %>% filter(celltype == "B cell") %>% ungroup() %>% slice_max(numberOfcells, n = 50) %>% pull(Description)
Prepare data
Cell-level metadata
Here, we rename the columns of the colData entry for consistency with the other datasets.
renaming_vector <- c( cell_id = "cellID", cell_number = "CellNumber", image_number = "ImageNumber", cell_x = "Center_X", cell_y = "Center_Y", cell_type = "celltype", cell_cluster = "celltype_clustered", cell_area = "Area", cell_major_axis_length = "MajorAxisLength", cell_minor_axis_length = "MinorAxisLength", cell_in_tumour = "in_tumor", milieu_Bcell_patch_score = "bcell_patch_score", milieu_Tcell_density_score = "Tcell_density_score_image", milieu_dysfunction_score = "dysfunction_score", milieu_dysfunction_density = "dysfunction_density", neighbors_number = "NumberOfNeighbors", sample_block_id = "BlockID", sample_location = "Location", sample_description = "Description", sample_tissue_type = "TissueType", sample_MM_location = "MM_location", sample_MM_location_short = "MM_location_simplified", patient_id = "PatientID", patient_age = "Age", patient_gender = "Gender", patient_cancer_stage = "Cancer_Stage", patient_adjuvant_therapy = "Adjuvant", patient_E_I_D = "E_I_D", patient_mutation = "Mutation", patient_status_at_3months = "Status_at_3m", patient_relapse = "relapse", patient_treatment_status_before_surgery = "treatment_status_before_surgery", patient_treatment_group_before_surgery = "treatment_group_before_surgery", patient_treatment_group_after_surgery = "treatment_group_after_surgery", patient_death = "Death", patient_death_date = "Date_death" ) renaming_vector_protein = c( cell_parent_nucleus = "Parent_nuclei", milieu_Bcell_patch = "bcell_patch", milieu_Bcell_milieu = "bcell_milieu", milieu_TCF7 = "TCF7", milieu_PD1 = "PD1" ) renaming_vector_rna = c( cell_type_rf = "celltype_rf", cell_annotation = "cellAnnotation", chemokine_expressor = "expressor" ) # Rename protein dataset protein_coldata <- as_tibble(colData(sce_protein)) protein_coldata <- protein_coldata %>% dplyr::rename(all_of(renaming_vector)) %>% dplyr::rename(all_of(renaming_vector_protein)) %>% as("DataFrame") protein_coldata$cell_id <- gsub("protein_", "", protein_coldata$cell_id) protein_coldata$image_name <- as.character(protein_coldata$image_number) rownames(protein_coldata) <- protein_coldata$cell_id # Rename RNA dataset rna_coldata <- as_tibble(colData(sce_rna)) rna_coldata <- rna_coldata %>% dplyr::rename(all_of(renaming_vector)) %>% dplyr::rename(all_of(renaming_vector_rna)) %>% as("DataFrame") rna_coldata$cell_id <- gsub("RNA_", "", rna_coldata$cell_id) rna_coldata$image_name <- as.character(rna_coldata$image_number) rownames(rna_coldata) <- rna_coldata$cell_id
Marker metadata
Here, we rename the columns of the rowData entry for consistency with the other datasets.
renaming_vector <- c( channel = "channel", metal = "Metal.Tag", name = "Target", short_name = "clean_target", marker_class = "marker_class", antibody_clone = "Antibody.Clone", antibody_tube_number = "Tube.Number", antibody_stock_conc = "Stock.Concentration", antibody_final_conc_or_dilution = "Final.Concentration...Dilution", antibody_ul_to_add = "uL.to.add", full = "full", keep = "good_marker", ilastik = "ilastik" ) # Rename protein dataset protein_rowdata <- as_tibble(rowData(sce_protein)) protein_rowdata <- protein_rowdata %>% dplyr::rename(all_of(renaming_vector)) %>% dplyr::rename(c(tumor_mask = "tumorMask")) %>% as.data.table() protein_rowdata$full_name <- protein_rowdata$name # Rename RNA dataset rna_rowdata <- as_tibble(rowData(sce_rna)) rna_rowdata <- rna_rowdata %>% dplyr::rename(all_of(renaming_vector)) %>% dplyr::rename(c(marker_no_RNA = "noRNA_marker")) %>% as.data.table() rna_rowdata$full_name <- rna_rowdata$name
We also rename markers for consistency with other datasets.
# Protein dataset protein_rowdata[metal == "Cd111", `:=` (full_name = "Vimentin", short_name = "VIM")] protein_rowdata[metal == "Cd112", `:=` (short_name = "CAV1")] protein_rowdata[metal == "In113", `:=` (short_name = "H3")] protein_rowdata[metal == "In115", `:=` (full_name = "Smooth muscle actin")] protein_rowdata[metal == "Nd142", `:=` (full_name = "CXCR2 IL-8 RB")] protein_rowdata[metal == "Nd143", `:=` (short_name = "HLA_DR")] protein_rowdata[metal == "Sm147", `:=` (full_name = "SOX-9", short_name = "SOX9")] protein_rowdata[metal == "Eu151", `:=` ( full_name = "phospho-ERK1/2 [T202/Y204]", short_name = "p_ERK1_2")] protein_rowdata[metal == "Sm152", `:=` (full_name = "CD3 epsilon", short_name = "CD3e")] protein_rowdata[metal == "Gd155", `:=` ( full_name = "Programmed cell death protein 1", short_name = "PD_1")] protein_rowdata[metal == "Gd156", `:=` (short_name = "MITF")] protein_rowdata[metal == "Tb159", `:=` (short_name = "GZMB")] protein_rowdata[metal == "Gd160", `:=` (full_name = "Programmed death-ligand 1", short_name = "PD_L1")] protein_rowdata[metal == "Dy163", `:=` (full_name = "Forkhead box P3")] protein_rowdata[metal == "Dy164", `:=` ( full_name = "Inducible T-cell costimulator", short_name = "ICOS")] protein_rowdata[metal == "Ho165", `:=` (full_name = "Beta-catenin", short_name = "CTNNB1")] protein_rowdata[metal == "Er166", `:=` (full_name = "CD8 alpha", short_name = "CD8a")] protein_rowdata[metal == "Er167", `:=` ( short_name = "COL1A1", antibody_clone = "polyclonal_Collagen I")] protein_rowdata[metal == "Er168", `:=` (full_name = "Ki-67_Er168", short_name = "Ki67_Er168")] protein_rowdata[metal == "Er170", `:=` (full_name = "phospho-S6 [S235/S236]", short_name = "p_S6")] protein_rowdata[metal == "Yb172", `:=` ( full_name = "Indoleamine 2,3-dioxygenase 1")] protein_rowdata[metal == "Yb173", `:=` (full_name = "SOX-10", short_name = "SOX10")] protein_rowdata[metal == "Yb174", `:=` ( antibody_clone = "polyclonal_DLEC_CLEC4C_BDCA-2")] protein_rowdata[metal == "Lu175", `:=` (full_name = "CD206")] protein_rowdata[metal == "Yb176", `:=` (full_name = "cleaved-PARP", short_name = "c_PARP")] protein_rowdata[metal == "Ir191", `:=` (full_name = "Iridum 191", antibody_clone = NA)] protein_rowdata[metal == "Ir193", `:=` (full_name = "Iridium 193", antibody_clone = NA)] protein_rowdata[metal == "Pt198", `:=` (full_name = "Ki-67_Pt198", short_name = "Ki67_Pt198")] protein_rowdata[metal == "Y89", `:=` (full_name = "Myeloperoxidase", antibody_clone = "polyclonal_MPO")] # RNA dataset rna_rowdata[metal == "Cd111", `:=` (full_name = "Vimentin", short_name = "VIM")] rna_rowdata[metal == "In113", `:=` (short_name = "H3")] rna_rowdata[metal == "In115", `:=` (full_name = "Smooth muscle actin")] rna_rowdata[metal == "Pr141", `:=` (full_name = "Cytokeratin 5", short_name = "KRT5")] rna_rowdata[metal == "Nd143", `:=` (short_name = "HLA_DR")] rna_rowdata[metal == "Nd145", `:=` (full_name = "Cadherin-11", short_name = "CDH11")] rna_rowdata[metal == "Nd146", `:=` ( full_name = "Fibroblast activation protein alpha", short_name = "FAP")] rna_rowdata[metal == "Nd148", `:=` (full_name = "Beta-2-microglobulin")] rna_rowdata[metal == "Eu151", `:=` (full_name = "Glucose transporter 1", short_name = "SLC2A12")] rna_rowdata[metal == "Sm152", `:=` (full_name = "CD3 epsilon", short_name = "CD3e")] rna_rowdata[metal == "Eu153", `:=` ( full_name = "Lymphocyte activation gene 3 protein", short_name = "LAG3")] rna_rowdata[metal == "Gd155", `:=` ( full_name = "Programmed cell death protein 1", short_name = "PD_1")] rna_rowdata[metal == "Gd156", `:=` (short_name = "CCL4_mRNA", antibody_clone = NA)] rna_rowdata[metal == "Gd158", `:=` (short_name = "CCL18_mRNA", antibody_clone = NA)] rna_rowdata[metal == "Tb159", `:=` (full_name = "C-C chemokine receptor type 2", short_name = "CCR2")] rna_rowdata[metal == "Gd160", `:=` (full_name = "Programmed death-ligand 1", short_name = "PD_L1")] rna_rowdata[metal == "Dy161", `:=` (short_name = "CXCL8_mRNA", antibody_clone = NA)] rna_rowdata[metal == "Dy162", `:=` (short_name = "CXCL10_mRNA", antibody_clone = NA)] rna_rowdata[metal == "Dy163", `:=` (short_name = "CXCL12_mRNA", antibody_clone = NA)] rna_rowdata[metal == "Dy164", `:=` (short_name = "CXCL13_mRNA", antibody_clone = NA)] rna_rowdata[metal == "Ho165", `:=` (full_name = "CD8 alpha", short_name = "CD8a")] rna_rowdata[metal == "Er166", `:=` (short_name = "CCL2_mRNA", antibody_clone = NA)] rna_rowdata[metal == "Er167", `:=` (short_name = "CCL22_mRNA", antibody_clone = NA)] rna_rowdata[metal == "Er168", `:=` (short_name = "CXCL9_mRNA", antibody_clone = NA)] rna_rowdata[metal == "Tm169", `:=` (short_name = "DapB_mRNA", antibody_clone = NA)] rna_rowdata[metal == "Er170", `:=` (full_name = "SOX-10", short_name = "SOX10")] rna_rowdata[metal == "Yb171", `:=` (short_name = "CCL8_mRNA", antibody_clone = NA)] rna_rowdata[metal == "Yb173", `:=` (short_name = "CCL19_mRNA", antibody_clone = NA)] rna_rowdata[metal == "Yb174", `:=` (full_name = "MelanA / MART1", short_name = "MLANA")] rna_rowdata[metal == "Lu175", `:=` (full_name = "phopsho-Rb [S807/S811]", short_name = "p_Rb")] rna_rowdata[metal == "Yb176", `:=` (full_name = "cleaved-PARP", short_name = "c_PARP")] rna_rowdata[metal == "Ir191", `:=` (full_name = "Iridium 191", antibody_clone = NA)] rna_rowdata[metal == "Ir193", `:=` (full_name = "Iridium 193", antibody_clone = NA)] rna_rowdata[metal == "Y89", `:=` (full_name = "Myeloperoxidase", antibody_clone = "polyclonal_MPO")]
We convert the panel table to a DataFrame and add target short_names as row names.
protein_rowdata <- as(protein_rowdata, "DataFrame") rownames(protein_rowdata) <- protein_rowdata$short_name rna_rowdata <- as(rna_rowdata, "DataFrame") rownames(rna_rowdata) <- rna_rowdata$short_name
Update SingleCellExperiment object
We now update the SingleCellExperiment objects with new rowData and colData entries.
# Protein dataset colData(sce_protein) <- protein_coldata rowData(sce_protein) <- protein_rowdata # RNA dataset colData(sce_rna) <- rna_coldata rowData(sce_rna) <- rna_rowdata
Finally, we rename the row and column names of the SingleCellExperiment objects.
# Protein dataset rownames(sce_protein) <- rownames(protein_rowdata) colnames(sce_protein) <- rownames(protein_coldata) # RNA dataset rownames(sce_rna) <- rownames(rna_rowdata) colnames(sce_rna) <- rownames(rna_coldata) # MainExpName mainExpName(sce_protein) <- paste( dataset_name, "protein", "FULL", dataset_version, sep = "_") mainExpName(sce_rna) <- paste( dataset_name, "RNA", "FULL", dataset_version, sep = "_")
We also rename the assays for consistency with other datasets.
assayNames(sce_protein) <- c("counts", "exprs", "scaled_counts", "scaled_exprs") assayNames(sce_rna) <- c("counts", "exprs", "scaled_counts", "scaled_exprs")
Save on disk
We save the SingleCellExperiment objects for upload to r Biocpkg("ExperimentHub").
saveRDS(sce_protein, file.path(outdir, "sce_full_protein.rds")) print(sce_protein) saveRDS(sce_rna, file.path(outdir, "sce_full_rna.rds")) print(sce_rna)
Subset
We also save subsets of the SingleCellExperiment objects, so they can be
matched with the multichannel image objects we are going to generate below.
The subset corresponds to the top 50 image with most B cells.
# Subset sce_protein_sub <- sce_protein[ , sce_protein$sample_description %in% images_to_keep] sce_rna_sub <- sce_rna[ , sce_rna$sample_description %in% images_to_keep] # Rename mainExpName(sce_protein_sub) <- paste( dataset_name, "protein", dataset_version, sep = "_") mainExpName(sce_rna_sub) <- paste( dataset_name, "RNA", dataset_version, sep = "_") # Save saveRDS(sce_protein_sub, file.path(outdir, "sce_protein.rds")) print(sce_protein_sub) saveRDS(sce_rna_sub, file.path(outdir, "sce_rna.rds")) print(sce_rna_sub)
Clean up
Finally, we remove the downloaded files and generated objects to save storage space.
remove(protein_coldata, protein_rowdata, rna_coldata, rna_rowdata) unlink(file.path(workdir, "data_for_analysis_zenodo"), recursive = TRUE)
Images and cell masks
Import images and masks - Protein dataset
Download the dataset
Here, we download a subset of 50 images from the zenodo repository.
# Download the zipped folder image and unzip it system2("curl", args = c( "-o", file.path(workdir, "ImageSubset.zip"), "https://zenodo.org/record/6004986/files/full_data.zip?download=1", "--max-time 900")) # Unzip selected files system2("unzip", args = c( "-o", file.path(workdir, "ImageSubset.zip"), "full_data/protein/cpout/*.tiff", "-d", workdir)) system2("unzip", args = c( "-o", file.path(workdir, "ImageSubset.zip"), "full_data/protein/cpout/Image.csv", "-d", workdir))
We select the fifty images that correspond to the dat in the SingleCellExperiment objects.
protein_directory = file.path( workdir, file.path("full_data", "protein", "cpout")) protein_info <- fread(file.path(protein_directory, "Image.csv")) protein_info_sub <- protein_info[ protein_info$Metadata_Description %in% images_to_keep, ]
Multichannel images
We use the loadImages function of the cytomapper package to read the images into a CytoImageList object.
images_protein <- loadImages( protein_directory, pattern = protein_info_sub$FileName_SpillCorrected)
Cell segmentation masks
We also read the masks into a CytoImageList object.
masks_protein <- loadImages( protein_directory, pattern = protein_info$FileName_cellmask)
Clean-up
We remove the downloaded image and mask tiff files to save storage space.
files_to_delete <- list.files(protein_directory, full.names = TRUE) file.remove(files_to_delete)
Import images and masks - RNA dataset
Unzip the dataset
The dataset has been already been downloaded from zenodo, together with the protein dataset.
system2("unzip", args = c( "-o", file.path(workdir, "ImageSubset.zip"), "full_data/rna/cpout/*.tiff", "-d", workdir)) system2("unzip", args = c( "-o", file.path(workdir, "ImageSubset.zip"), "full_data/rna/cpout/Image.csv", "-d", workdir)) # Clean-up file.remove(file.path(workdir, "ImageSubset.zip"))
We select the fifty images that correspond to the dat in the SingleCellExperiment objects.
rna_directory <- file.path(workdir, file.path("full_data", "rna", "cpout")) rna_info <- fread(file.path(rna_directory, "Image.csv")) rna_info_sub <- rna_info[rna_info$Metadata_Description %in% images_to_keep, ]
Multichannel images
We use the loadImages function of the cytomapper package to read the images into a CytoImageList object.
images_rna <- loadImages( rna_directory, pattern = rna_info_sub$FileName_SpillCorrected)
Cell segmentation masks
We also read the masks into a CytoImageList object.
masks_rna <- loadImages( rna_directory, pattern = rna_info$FileName_cellmask)
Clean-up
We remove the downloaded image and mask tiff files to save storage space.
files_to_delete <- list.files(rna_directory, full.names = TRUE) file.remove(files_to_delete)
Prepare images and masks
We will now process the images and masks to make them compatible with the cytomapper package.
Rescale masks
The masks are 16-bit images and need to be re-scaled in order to obtain integer cell ids.
masks_protein <- scaleImages(masks_protein, value = protein_info$Scaling_cellmask[[1]]) masks_rna <- scaleImages(masks_rna, value = rna_info$Scaling_cellmask[[1]])
Add image names and numbers
Next, we add image names to the images and masks objects, these names correspond to the image_name column in colData(sce_*). This information is stored in the metadata columns of the CytoImageList objects and is used by cytomapper to match single cell data, images and masks.
# We extract the file names of the masks into data frames df_masks_protein <- data.frame( cell_mask = protein_info$FileName_cellmask, image_number = protein_info$ImageNumber, description = protein_info$Metadata_Description) df_images_protein <- data.frame( cell_mask = protein_info_sub$FileName_SpillCorrected, image_number = protein_info_sub$ImageNumber, description = protein_info_sub$Metadata_Description) # We match the data frames row names with the names of the 'masks' object rownames(df_masks_protein) <- gsub(".tiff", "", protein_info$FileName_cellmask) rownames(df_images_protein) <- gsub(".tiff", "", protein_info_sub$FileName_SpillCorrected) # We add the extracted information to the metadata of the 'masks' object mcols(masks_protein) <- df_masks_protein[names(masks_protein), ] mcols(images_protein) <- df_images_protein[names(images_protein), ] # Add image names mcols(masks_protein)$image_name <- as.character( mcols(masks_protein)$image_number) mcols(images_protein)$image_name <- as.character( mcols(images_protein)$image_number) names(masks_protein) <- mcols(masks_protein)$image_name names(images_protein) <- mcols(images_protein)$image_name # Add dataset specification mcols(images_protein)$dataset <- rep("protein", nrow(mcols(images_protein))) mcols(masks_protein)$dataset <- rep("protein", nrow(mcols(masks_protein)))
# We extract the file names of the masks into data frames df_masks_rna <- data.frame( cell_mask = rna_info$FileName_cellmask, image_number = rna_info$ImageNumber, description = rna_info$Metadata_Description) df_images_rna <- data.frame( cell_mask = rna_info_sub$FileName_SpillCorrected, image_number = rna_info_sub$ImageNumber, description = rna_info_sub$Metadata_Description) # We match the data frames row names with the names of the 'masks' object rownames(df_masks_rna) <- gsub(".tiff", "", rna_info$FileName_cellmask) rownames(df_images_rna) <- gsub(".tiff", "", rna_info_sub$FileName_SpillCorrected) # We add the extracted information to the metadata of the 'masks' object mcols(masks_rna) <- df_masks_rna[names(masks_rna), ] mcols(images_rna) <- df_images_rna[names(images_rna), ] # Add image names mcols(masks_rna)$image_name <- as.character( mcols(masks_rna)$image_number) mcols(images_rna)$image_name <- as.character( mcols(images_rna)$image_number) names(masks_rna) <- mcols(masks_rna)$image_name names(images_rna) <- mcols(images_rna)$image_name # Add dataset specification mcols(images_rna)$dataset <- rep("RNA", nrow(mcols(images_rna))) mcols(masks_rna)$dataset <- rep("RNA", nrow(mcols(masks_rna)))
Sanity check
identical(mcols(masks_rna)$Description, mcols(images_rna)$Description) identical(mcols(masks_protein)$Description, mcols(images_protein)$Description)
Add channel names
Finally, we will add protein short names as channel names of the images objects with , corresponding to the row names of the SingleCellExperiment objects and to the short_name column of rowData(sce).
# Protein dataset panel_protein <- rowData(sce_protein) panel_protein <- panel_protein[order(panel_protein$channel), ] channelNames(images_protein) <- rownames(panel_protein) # RNA dataset panel_rna <- rowData(sce_rna) panel_rna <- panel_rna[order(panel_rna$channel), ] channelNames(images_rna) <- rownames(panel_rna)
Save masks on disk
Finally, we will save the generated CytoImageList images and masks objects for uploading to r Biocpkg("ExperimentHub").
saveRDS(masks_protein, file.path(outdir, "masks_full_protein.rds")) print(masks_protein) saveRDS(masks_rna, file.path(outdir, "masks_full_rna.rds")) print(masks_rna)
We also subset the mask objects to match the multichannel image objects.
# Subset masks_protein_sub <- masks_protein[ mcols(masks_protein)$image_name %in% mcols(images_protein)$image_name, ] masks_rna_sub <- masks_rna[ mcols(masks_rna)$image_name %in% mcols(images_rna)$image_name, ] # Save saveRDS(masks_protein_sub, file.path(outdir, "masks_protein.rds")) print(masks_protein_sub) saveRDS(masks_rna_sub, file.path(outdir, "masks_rna.rds")) print(masks_rna_sub)
remove(sce_protein, sce_protein_sub, sce_rna, sce_rna_sub, masks_protein, masks_protein_sub, masks_rna, masks_rna_sub) gc()
Save images on disk
# RNA dataset saveRDS(images_rna, file.path(outdir, "images_rna.rds")) print(images_rna) remove(images_rna) gc() # Protein dataset saveRDS(images_protein, file.path(outdir, "images_protein.rds")) print(images_protein)
Clean up
Remove all files from the temporary working directory.
downloaded_files <- list.files(workdir) downloaded_files <- downloaded_files[!downloaded_files %in% "BiocStyle"] unlink(file.path(workdir, downloaded_files), recursive = TRUE) # Reset original timeout value options(timeout = timeout)
Session information
sessionInfo()
References
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.