scripts/populate_folder_spata2v3_objects.R
In theMILOlab/SPATAData: The MILOLab Spatial Transriptomic Database
######---------- script to populate spata2v3 folder ----------#####
# data sets are uploaded in batches according to their "source"
# this script is structured by:
# -> source -------------------------------------------------------
source("scripts/init_session.R")
# TENxVisiumData ----------------------------------------------------------
#BiocManager::install("HelenaLC/TENxVisiumData")
library(TENxVisiumData)
eh <- ExperimentHub() # initialize hub instance
q <- query(eh, "TENxVisium") # retrieve 'TENxVisiumData' records
ids <- q$ah_id
nms <- c("ah_id", "title", "dataprovider", "species", "taxonomyid", "description", "maintainer")
out <- list()
for(i in seq_along(ids)){
title <- q$title[[i]]
id <- q$ah_id[[i]]
out[[title]] <-
list(
id = ids[[i]],
title = title,
desc = q$description[[i]],
species = q$species[[i]],
dpr = q$dataprovider[[i]],
spe = eh[[id]]
)
}
obj_names <- map_chr(out, .f = ~.x$title) %>% unname()
obj_names <-
str_subset(obj_names, "v3", negate = T) %>%
str_subset("BreastCancer", negate = T) %>%
str_subset("Colorectal", negate = T) %>%
str_subset("Ovarian", negate = T)
pdf(file = "spata2v3_objects/TENxVisiumData/sample_overview.pdf")
for(i in seq_along(obj_names)){
obj_name <- obj_names[i]
lst <- out[[obj_name]]
spe_all <- out[[obj_name]]$spe
img_df <- imgData(spe_all)
print(img_df)
for(s_id in img_df$sample_id){
# assemble object
print(s_id)
spe <- spe_all[, spe_all$sample_id == s_id]
count_mtr <- spe@assays@data$counts
cdf <- colData(spe)
coords_df <-
spatialCoords(spe) %>%
as.data.frame() %>%
rownames_to_column("barcodes") %>%
as_tibble() %>%
dplyr::rename(x_orig = pxl_col_in_fullres, y_orig = pxl_row_in_fullres)
coords_df$barcodes <- str_replace(coords_df$barcodes, pattern = "\\.", replacement = "-")
isf <- img_df[img_df$sample_id == s_id,]$scaleFactor
image_obj <- getImg(spe)
image <-
image_obj@image %>%
as.matrix() %>%
EBImage::as.Image()
if(base::any(coords_df$barcodes %in% visium_spots$VisiumSmall$barcode)){
method <- "VisiumSmall"
} else if(base::any(coords_df$barcodes %in% visium_spots$VisiumLarge$barcode)){
method <- "VisiumLarge"
}
object <-
initiateSpataObject(
sample_name = s_id,
coords_df = coords_df,
count_mtr = count_mtr,
modality = "gene",
spatial_method = method,
img = image,
img_name = "lowres",
scale_factors = list(image = isf)
)
# add meta data
meta_data <- list()
meta_data$institution <- "10X Genomics"
meta_data$donor_species <- ifelse(str_detect(obj_name, "Human"), "Homo sapiens", "Mus musculus")
if(str_detect(obj_name, "HumanCerebellum")){
meta_data$organ <- "Cerebellum"
meta_data$histo_class = "Cortex"
} else if(obj_name == "HumanLymphNode"){
meta_data$organ <- "Lymph Node"
meta_data$histo_class = "Lymph Node"
} else if(str_detect(obj_name, "Glioblastoma")){
meta_data$organ <- "Cerebrum"
meta_data$pathology <- "tumor"
meta_data$histo_class = "Glioblastoma"
meta_data$grade <- "IV"
} else if(obj_name == "MouseKidneyCoronal"){
meta_data$organ <- "Kidney"
meta_data$histo_class = "Kidney"
} else if(obj_name == "HumanHeart"){
meta_data$organ <- "Heart"
meta_data$histo_class <- "Cardiac Muscle"
} else if(obj_name == "HumanSpinalCord"){
meta_data$organ <- "Spinal Cord"
meta_data$histo_class <- "Spinal Cord"
} else if(str_detect(obj_name, "Brain")){
meta_data$organ <- "Brain"
}
meta_data$platform <- method
# save object
file_name <- str_c(s_id, ".RDS")
dir <- file.path("spata2v3_objects", "TENxVisiumData", file_name)
object <- setSpataDir(object, dir = dir)
object <- addSampleMetaData(object, meta_data = meta_data)
object <- computePixelScaleFactor(object)
object <- identifyTissueOutline(object)
print(object)
print(dir)
saveRDS(object, dir)
p_overview <-
(plotSurface(object, pt_alpha = 0) + labs(subtitle = object@sample)) +
(plotSurface(object, color_by = "tissue_section") + legendBottom())
plot(p_overview)
}
}
dev.off()
# Kuppe et al. 2022 -------------------------------------------------------
dir.create("spata2v3_objects/Kuppe_et_al_2022")
heart_dirs <-
list.files(path = "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Kuppe_et_al_2022", full.names = T) %>%
str_subset(pattern = "\\.", negate = T) %>%
file.path("outs")
sample_names <-
list.files(path = "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Kuppe_et_al_2022") %>%
str_subset(pattern = "\\.", negate = T)
mdf <- readRDS("/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Kuppe_et_al_2022/meta_df_processed.RDS")
mdf$major_label <- mdf$major_labl
labels <- c("FZ" = "fibrotic_zone", "IZ" = "ischaemic_zone", "BZ" = "border_zone", "CTRL" = "control", "RZ" = "remote_zone")
pdf(file = "spata2v3_objects/Kuppe_et_al_2022/sample_overview.pdf")
for(i in seq_along(heart_dirs)){
sample_name <- sample_names[i]
dir <- heart_dirs[i]
object <-
initiateSpataObjectVisium(
sample_name = sample_name,
directory_visium = dir
)
# add meta data
mdf_sub <- filter(mdf, hca_sample_id == {{sample_name}})
meta_data <- list()
meta_data$donor_id <- mdf_sub$patient
meta_data$donor_species <- "Homo sapiens"
meta_data$institution <- "University Clinic Aachen"
meta_data$organ <- "Heart"
meta_data$histo_class = "Cardiac Muscle"
meta_data$histo_class_sub = labels[mdf$major_label[i]]
meta_data$pathology <- "myocardial_infarction"
meta_data$pub_citation <-
"Kuppe, C., Ramirez Flores, R.O., Li, Z. et al. Spatial multi-omic map of human myocardial infarction. Nature 608, 766–777 (2022). https://doi.org/10.1038/s41586-022-05060-x"
meta_data$pub_doi <- "doi.org/10.1038/s41586-022-05060-x"
meta_data$pub_journal <- "Nature"
meta_data$pub_year <- 2022
meta_data$patient_group <- mdf_sub$patient_group
meta_data$patient_region_id <- mdf_sub$patient_region_id
meta_data$tags <- c(mdf$major_label[i])
meta_data$platform <- "VisiumSmall"
meta_data$workgroup <- "KuppeLab"
object <- addSampleMetaData(object, meta_data = meta_data)
# save object
file_name <- str_c(sample_name, ".RDS")
dir <- file.path("spata2v3_objects", "Kuppe_et_al_2022", file_name)
object <- setSpataDir(object, dir = dir)
print(object)
print(dir)
saveRDS(object, dir)
# plot
p_overview <-
(plotSurface(object, pt_alpha = 0) + labs(subtitle = object@sample)) +
(plotSurface(object, color_by = "tissue_section") + legendBottom())
plot(p_overview)
}
dev.off()
# Ravi et al. 2022 --------------------------------------------------------
dir.create("spata2v3_objects/Ravi_et_al_2022")
mdf <- readRDS("/Users/heilandr/lab/data/spatial_seq/meta/spatial_seq_meta.RDS")
patient_ids <-
c('#275UKF', '#270UKF', '#269UKF', '#268UKF', '#266UKF', '#265UKF', '#265UKF', '#262UKF', '#260UKF', '#259UKF',
'#259UKF', '#256UKF', '#256UKF', '#255UKF', '#251UKF', '#248UKF', '#248UKF', '#243UKF', '#242UKF', '#242UKF',
'#241UKF', '#296UKF', '#304UKF', '#313UKF', '#313UKF', '#334UKF', '#334UKF', '#212UKF', '#217UKF', '#218UKF')
patient_ids_new <-
str_replace(patient_ids, "#", "UKF") %>%
str_remove("UKF$")
ravi_dirs <-
list_raw_visium() %>%
SPATA2::vselect(contains(patient_ids_new))
pdf("spata2v3_objects/Ravi_et_al_2022/sample_overview.pdf")
for(i in 1:(nrow(source_df)-1)){
old_id <- patient_ids[i]
new_id <- patient_ids_new[i]
raw_dirs <-
list_raw_visium() %>%
vselect(contains(new_id))
for(raw_dir in raw_dirs){
suffix <-
confuns::str_extract_after(raw_dir, pattern = new_id ) %>%
str_remove_all(pattern = "_") %>%
str_remove(pattern = "P$")
sample_name <- str_c(new_id, suffix)
object <-
initiateSpataObjectVisium(
sample_name = sample_name,
directory_visium = file.path(raw_dir, "outs")
)
sname <- sample_name
# add meta data
smdf <- source_df_v2[i, ]
mdf_sub <- filter(mdf, Samples_ID == smdf$sample)
meta_data <- list()
meta_data$donor_id <- new_id
meta_data$donor_species <- "Homo sapiens"
meta_data$grade <- ifelse(str_detect(sname, "T"), smdf$who_grade, NA_character_)
meta_data$histo_class <- ifelse(str_detect(sname, "C"), "Cortex", smdf$hist_classification)
meta_data$institution <- "University Clinic Freiburg"
meta_data$organ <- "Cerebrum"
meta_data$organ_part <- smdf$anatomical_region
meta_data$pathology <- ifelse(str_detect(sname, "T"), "tumor", "infiltrated")
meta_data$platform <- getSpatialMethod(object)@name
meta_data$pub_citation <-
"VM Ravi, P Will, J Kueckelhaus, et al. Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma. Cancer Cell. 2022 Jun 13;40(6):639-655.e13. doi: 10.1016/j.ccell.2022.05.009. PMID: 35700707."
meta_data$pub_doi <- "10.1016/j.ccell.2022.05.009"
meta_data$pub_journal <- "Cancer Cell"
meta_data$pub_year <- 2022
meta_data$tags <- c("brain", "cns", "cancer")
meta_data$workgroup <- "MILOlab"
if(nrow(mdf_sub) != 0){
meta_data$age <- as.numeric(mdf_sub$Age_atSampling) %>% round(digits = 0)
}
object <- addSampleMetaData(object, meta_data = meta_data)
# save object
file_name <- str_c(sample_name, ".RDS")
dir <- file.path("spata2v3_objects", "Ravi_et_al_2022", file_name)
object <- setSpataDir(object, dir = dir)
print(object)
print(dir)
saveRDS(object, dir)
p_overview <-
(plotSurface(object, pt_alpha = 0) + labs(subtitle = object@sample)) +
(plotSurface(object, color_by = "tissue_section") + legendBottom())
plot(p_overview)
}
}
dev.off()
# 10X Visium example data sets --------------------------------------------
dir.create("spata2v3_objects/10X_example_data_sets")
example_dirs <-
list.files(path = "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/10X_example_data_sets", full.names = T) %>%
str_subset(pattern = ".RDS", negate = T) %>%
str_subset(pattern = "targeted", negate = T)
mdf <- readRDS("/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/10X_example_data_sets/meta_df.RDS")
pdf(file = "spata2v3_objects/10X_example_data_sets/sample_overview.pdf")
for(ed in example_dirs){
sample_name <-
confuns::str_extract_after(ed, pattern = "10X_example_data_sets\\/") %>%
str_replace_all(pattern = "-", replacement = "_")
object <-
initiateSpataObjectVisium(
sample_name = sample_name,
directory_visium = ed
)
meta_data <- list()
meta_data$donor_species <- "Homo sapiens"
meta_data$institution <- "10X Genomics"
if(str_detect(sample_name, "Breast")){
meta_data$organ <- "Breast"
} else if(str_detect(sample_name, "Colorectal")){
meta_data$organ <- "Colon"
} else if(str_detect(sample_name, "Lung")){
meta_data$organ <- "Lung"
} else if(str_detect(sample_name, "Prostate")){
meta_data$organ <- "Prostate"
} else if(str_detect(sample_name, "Ovarian")){
meta_data$organ <- "Ovary"
}
meta_data$pathology <- "tumor"
meta_data$platform <- getSpatialMethod(object)@name
meta_data$tags <- c("cancer")
object <- addSampleMetaData(object, meta_data = meta_data)
file_name <- str_c(sample_name, ".RDS")
dir <- file.path("spata2v3_objects", "10X_example_data_sets", file_name)
object <- setSpataDir(object, dir = dir)
print(object)
print(dir)
saveRDS(object, dir)
p_overview <-
(plotSurface(object, pt_alpha = 0) + labs(subtitle = object@sample)) +
(plotSurface(object, color_by = "tissue_section") + legendBottom())
plot(p_overview)
}
dev.off()
# 10X Visium example data sets VisiumLarge --------------------------------
subfolder <- "10X_example_data_sets_VisiumLarge"
create_subfolder(subfolder)
dir_main <- "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/10X_example_data_sets_VisiumLarge"
all_folders <- list.files(dir_main, full.names = T)
organs <- c("Colon", "Cerebrum", "Kidney", "Lung", "Ovary")
pathology <- c("tumor", "tumor", NA, "tumor", "tumor")
open_overview_pdf(subfolder)
for(i in seq_along(all_folders)){
folder <- all_folders[i]
sample_name <- confuns::str_extract_after(folder, "VisiumLarge\\/")
object <-
initiateSpataObjectVisium(
directory_visium = folder,
sample_name = sample_name
)
meta_data <- list()
meta_data$donor_species <- "Homo sapiens"
meta_data$institution <- "10X Genomics"
meta_data$organ <- organs[i]
meta_data$pathology <- pathology[i]
meta_data$platform <- "VisiumLarge"
if(meta_data$organ == "Cerebrum"){
meta_data$histo_class <- "Glioblastoma"
}
object <- addSampleMetaData(object, meta_data = meta_data)
dir <- file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS"))
object <- setSpataDir(object, dir)
saveSpataObject(object)
plot_overview(object)
}
dev.off()
# 10X Visium example data sets GP -----------------------------------------
subfolder <- "10X_example_data_sets_GP"
create_subfolder(subfolder)
main_dir <- "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/10X_example_data_sets_GeneAndProtein"
all_folders <- list.files(main_dir, full.names = T)
organs <- c("Breast", "Cerebrum", "Tonsil")
histo_class <- c(NA, "Glioblastoma", NA)
open_overview_pdf(subfolder)
for(i in seq_along(all_folders)){
folder <- all_folders[i]
sample_name <- confuns::str_extract_after(folder, "GeneAndProtein\\/")
sample_name <- stringr::str_c(sample_name, "GP")
object <- initiateSpataObjectVisium(sample_name, directory_visium = folder, img_ref = "hires")
object <- loadImages(object)
# add meta data
meta_data <- list()
meta_data$donor_species <- "Homo sapiens"
meta_data$grade <- ifelse(organs[i] == "Cerebrum", "IV", NA_character_)
meta_data$histo_class <- histo_class[i]
meta_data$institution <- "10X Genomics"
meta_data$organ <- organs[i]
meta_data$pathology <- c("tumor", "tumor", NA)[i]
meta_data$platform <- object@platform
object <- addSampleMetaData(object, meta_data = meta_data)
dir <- file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS"))
saveSpataObject(object, dir = dir)
plot_overview(object)
}
dev.off()
# 10X example data sets Visium HD -----------------------------------------
subfolder <- "10X_example_data_sets_VHD"
create_subfolder(subfolder)
main_dir <- "/Users/heilandr/lab/data/spatial_seq/raw/10XVisiumHD/10X_example_data_sets_VHD"
all_folders <- list.files(main_dir, full.names = T)
open_overview_pdf(subfolder)
for(i in seq_along(all_folders)){
folder <- all_folders[i]
sample_name <- confuns::str_extract_after(folder, "10X_example_data_sets_VHD\\/")
object <-
initiateSpataObjectVisiumHD(
directory_visium = folder,
sample_name = sample_name,
img_ref = "hires",
img_active = "lowres"
)
object <- loadImages(object)
meta_data <- list()
meta_data$donor_species <- "Homo sapiens"
meta_data$organ <- c("Lung", "Pancreas")[i]
meta_data$institution <- "10X Genomics"
meta_data$pathology <- c("tumor", NA)[i]
meta_data$platform = "VisiumHD"
object <- addSampleMetaData(object, meta_data = meta_data)
dir <- file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS"))
object <- setSpataDir(object, dir)
saveSpataObject(object)
plot_overview(object)
}
dev.off()
# Regal et al. 2023 -------------------------------------------------------
dir.create("spata2v3_objects/Regal_et_al_2023")
dirs_spata <- list.files("/Users/heilandr/lab/data/spatial_seq/proc/spata_objects/SPATA2v1_objects", full.names = T)
age <- c(10, 16, 17, 20)
loc <- c(0, 2, 2, 2)
sex <- c("male", "male", "female", "male")
cd34 <- c("neg", rep("pos", 3))
pdf("spata2v3_objects/Regal_et_al_2023/sample_overview.pdf")
for(i in seq_along(dirs_spata)){
sd <- dirs_spata[i]
sobj <- readRDS(sd)
count_mtr <- sobj@data[[1]]$counts
img <- sobj@images[[1]]@image %>% EBImage::flip()
coords_df <- sobj@images[[1]]@coordinates[,c("barcodes", "x", "y")]
sample_name <- str_c("GG_Extern_", i)
object <-
initiateSpataObject(
sample_name = sample_name,
count_mtr = count_mtr,
coords_df = coords_df,
modality = "gene",
img = img,
img_name = "lowres",
scale_factors = list(image = 1),
spatial_method = "VisiumSmall"
)
object <- computePixelScaleFactor(object)
object <- identifyTissueOutline(object)
# add sample meta data
meta_data <- list()
meta_data$pub_citation <-
"Regal, J.A., Guerra García, M.E., Jain, V. et al. Ganglioglioma deep transcriptomics reveals primitive neuroectoderm neural precursor-like population.
acta neuropathol commun 11, 50 (2023). https://doi.org/10.1186/s40478-023-01548-3"
meta_data$pub_doi <- "https://doi.org/10.1186/s40478-023-01548-3"
meta_data$pub_journal <- "Acta neuropathologica"
meta_data$pub_year <- 2023
meta_data$institution <- "Duke University"
meta_data$workgroup <- "ReitmanLab"
meta_data$source <- "Regal_et_al_2023"
meta_data$organ <- ifelse(i == 1, "Brainstem", "Cerebrum")
if(i > 1){
meta_data$organ_part <- "temporal"
}
meta_data$histo_class <- "Ganglioglioma"
meta_data$grade <- "I"
meta_data$tissue_age <- age[i]
meta_data$platform <- "VisiumSmall"
meta_data$donor_species <- "Homo sapiens"
meta_data$sex <- sex[i]
meta_data$tags <- c(str_c("CD34_", cd34[i]))
meta_data$pathology <- "tumor"
object <- addSampleMetaData(object, meta_data = meta_data)
# save object
file_name <- str_c(sample_name, ".RDS")
dir <- file.path("spata2v3_objects", "Regal_et_al_2023", file_name)
object <- setSpataDir(object, dir = dir)
print(object)
print(dir)
saveRDS(object, dir)
p_overview <-
(plotSurface(object, pt_alpha = 0) + labs(subtitle = object@sample)) +
(plotSurface(object, color_by = "tissue_section") + legendBottom())
plot(p_overview)
}
dev.off()
# Kueckelhaus et al. 2024 -------------------------------------------------
dir.create("spata2v3_objects/Kueckelhaus_et_al_2024")
# uplaod mouse brain data set
obj <- readRDS("/Users/heilandr/lab/projects/SPATA2/review/data/mc_damaged.RDS")
obj@version$major <- 2
obj@version$minor <- 4
object <- updateSpataObject(obj)
meta_data <- list()
meta_data$comment <- "Injury annotations present as ImageAnotations."
meta_data$donor_species <- "Mus musculus"
meta_data$organ <- "Brain"
meta_data$pathology <- "stab_wound"
meta_data$pub_citation <- ""
meta_data$pub_journal <- "Nature Communications"
meta_data$platform <- "VisiumSmall"
meta_data$institution <- "Ludwig Maximilian University of Munich"
meta_data$workgroup <- "DichgansLab"
meta_data$sex <- "male"
meta_data$source <- "Kueckelhaus_et_al_2024"
object <- addSampleMetaData(object, meta_data = meta_data)
object <- renameSpataObject(object, sample_name = "LMU_MCI")
dir <- "spata2v3_objects/Kueckelhaus_et_al_2024/LMU_MCI.RDS"
object <- setSpataDir(object, dir = dir)
object <- setDefault(object, display_image = T)
object <- identifyTissueOutline(object)
saveSpataObject(object)
# Greenwald et al. 2024 ---------------------------------------------------
library(Seurat)
library(SPATA2)
library(stringr)
library(jsonlite)
library(tidyverse)
library(confuns)
subfolder <- "Greenwald_et_al_2024"
create_subfolder(subfolder)
citation <- readRDS("/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Greenwald_et_al_2024/citation.RDS")
# process meta
meta_df <-
read_csv("/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Greenwald_et_al_2024/meta_data_frame.csv")
colnames(meta_df) <- tolower(colnames(meta_df)) %>% str_replace_all("-", "_")
meta_df <-
tidyr::separate(meta_df, col = location, into = c("organ_side", "organ_part"))
meta_df <- meta_df[1:21, ]
meta_df$organ_part[meta_df$organ_side == "bifrontal"] <- "frontal"
meta_df$organ_side[meta_df$organ_side == "bifrontal"] <- "both"
main_dir <- "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Greenwald_et_al_2024"
all_dirs <-
list.files(main_dir, full.names = T) %>%
str_subset(".csv$", negate = T) %>%
str_subset("citation", negate = T)
open_overview_pdf(subfolder)
for(main_dir in all_dirs){
sample_name_prel <- confuns::str_extract_after(main_dir, pattern = "Greenwald_et_al_2024\\/")
sample_name <- str_suggest_vec(sample_name_prel, pool = meta_df$sample_id, max.dist = 10, n.top = 1)
mdf <- filter(meta_df, sample_id == {{sample_name}})
if(nrow(mdf) == 0){
message(glue::glue("No meta data for dir {main_dir}."))
} else {
message(glue::glue("Working on dir {main_dir}."))
# get directories
all_files <- list.files(main_dir, full.names = T)
dir_mtr <- str_subset(all_files, "\\.h5$")
dir_coords <- str_subset(all_files, "tissue_positions")
dir_img <- str_subset(all_files, "lowres_image|hires_image")
dir_sfs <- str_subset(all_files, "scalefactors")
# counts
mtr <- Seurat::Read10X_h5(filename = dir_mtr)
# image related
image <- EBImage::readImage(files = dir_img)
name_img_ref <- ifelse(str_detect(dir_img, "hires"), "hires", "lowres")
scale_factors <- read_json(dir_sfs)
image_sf <- scale_factors[[str_c("tissue_", name_img_ref, "_scalef")]]
# coords
if(is_empty(dir_coords)){
coords_df <- visium_spots$VisiumSmall %>% rename(barcodes = barcode)
coords_df$x_orig <- 0
coords_df$y_orig <- 0
coords_df <- filter(coords_df, barcodes %in% colnames(mtr))
warning(glue::glue("no coords for sample {sample_name}"))
} else {
coords_df <-
read_coords_visium(dir_coords) %>%
dplyr::filter(in_tissue == 1)
}
# initiate object
object <-
initiateSpataObject(
sample_name = sample_name,
count_mtr = mtr,
coords_df = coords_df,
modality = "gene",
img = image,
img_name = name_img_ref,
scale_factors = list(image = image_sf),
spatial_method = VisiumSmall
)
# visium specifics
object <- computePixelScaleFactor(object)
object <- identifyTissueOutline(object)
spot_size <-
scale_factors$fiducial_diameter_fullres *
scale_factors[[stringr::str_c("tissue", name_img_ref, "scalef", sep = "_")]] /
base::max(dim(image))*100
spot_scale_fct <- 1.15
sp_data <- getSpatialData(object)
sp_data@method@method_specifics[["spot_size"]] <- spot_size * spot_scale_fct
object <- setDefault(object, pt_size = spot_size*spot_scale_fct)
object <- setSpatialData(object, sp_data)
# add meta data
meta_data <- list()
meta_data$donor_id <- mdf$patient_id
meta_data$sex <- ifelse(mdf$sex == "F", "female", "male")
meta_data$tissue_age <- mdf$age
meta_data$donor_species <- "Homo sapiens"
meta_data$grade <- "IV"
if(str_detect(sample_name, "ZH")){
meta_data$institution <- "University Hospital Zurich"
} else if(str_detect(sample_name, "MGH")){
meta_data$institution <- "Massachusetts General Hospital"
} else if(str_detect(sample_name, "BWH")){
meta_data$institution <- "Brigham and Women's Hospital"
}
meta_data$organ <- "Cerebrum"
meta_data$organ_side <- mdf$organ_side
meta_data$organ_part <- mdf$organ_part
meta_data$grade <- WHOGrades(mdf$grade)
meta_data$pathology <- "tumor"
meta_data$platform <- "VisiumSmall"
if(mdf$histology == "GBM"){
meta_data$histo_class <- "Glioblastoma"
} else if(mdf$histology == "ODG"){
meta_data$histo_class <- "Oligodendroglioma"
} else if(mdf$histology == "AA"){
meta_data$histo_class <- "Astrocytoma"
}
meta_data$pub_citation <- citation
meta_data$pub_journal <- "Cell"
meta_data$pub_year <- 2024
meta_data$workgroup <- "TiroshLab"
meta_data$mgmt_status <- mdf$mgmt_status
meta_data$IDH_mut <- str_detect(mdf$tumor, "IDH-mut")
meta_data$tags <- c("cancer", "brain", "cns")
object <- addSampleMetaData(object, meta_data = meta_data)
# save object
dir <- file.path("spata2v3_objects", "Greenwald_et_al_2024", str_c(sample_name, ".RDS"))
object <- setSpataDir(object, dir)
saveSpataObject(object)
# plot
p_overview <-
(plotSurface(object, pt_alpha = 0) + labs(subtitle = object@sample)) +
(plotSurface(object, color_by = "tissue_section") + legendBottom())
plot(p_overview)
}
}
dev.off()
# Martinez-Hernandez_et_al_2024 -------------------------------------------
library(Matrix)
subfolder <- "Martinez_Hernandez_et_al_2024"
create_subfolder(subfolder)
dir_main <- "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Martinez_Hernandez_et_al_2024"
dir_samples <-
list.files(dir_main, full.names = T) %>%
str_subset("citation.RDS", negate = T) %>%
str_subset("meta_data_frame.csv", negate = T) %>%
str_subset("supp_info", negate = T)
citation <- readRDS(file.path(dir_main, "citation.RDS"))
meta_df <- read_csv(file.path(dir_main, "meta_data_frame.csv"))
colnames(meta_df) <- tolower(colnames(meta_df)) %>% str_replace_all("-| ", "_")
meta_df <- filter(meta_df, !is.na(visium_samples))
open_overview_pdf(subfolder)
for(dir_sample in dir_samples){
sample_name <- str_extract_after(dir_sample, "et_al_2024\\/")
mdf <- filter(meta_df, visium_samples == {{sample_name}})
all_files <- list.files(dir_sample, full.names = T)
# mtr
mtr <- read_matrix_mtx(dir_sample)
dir_coords <- str_subset(all_files, "tissue_positions")
dir_img <- str_subset(all_files, "hires_image")
dir_sfs <- str_subset(all_files, "scalefactors")
# image related
image <- EBImage::readImage(files = dir_img)
name_img_ref <- ifelse(str_detect(dir_img, "hires"), "hires", "lowres")
scale_factors <- read_json(dir_sfs)
image_sf <- scale_factors[[str_c("tissue_", name_img_ref, "_scalef")]]
# coords
if(is_empty(dir_coords)){
coords_df <- visium_spots$VisiumSmall %>% rename(barcodes = barcode)
coords_df$x_orig <- 0
coords_df$y_orig <- 0
coords_df <- filter(coords_df, barcodes %in% colnames(mtr))
warning(glue::glue("no coords for sample {sample_name}"))
} else {
coords_df <- read_coords_visium(dir_coords) %>% filter(in_tissue == 1)
}
object <-
initiateSpataObject(
sample_name = sample_name,
count_mtr = mtr,
modality = "gene",
coords_df = coords_df,
img = image,
img_name = "hires",
scale_factors = list(image = image_sf),
spatial_method = VisiumSmall
)
object <- computePixelScaleFactor(object)
object <- identifyTissueOutline(object)
image_lowres <- EBImage::readImage(str_subset(all_files, "lowres_image"))
object <-
registerImage(
object = object,
img = image_lowres,
img_name = "lowres"
)
object <- activateImage(object, img_name = "lowres", unload = F)
# add meta data
meta_data <- list()
meta_data$donor_species <- "Homo sapiens"
meta_data$histo_class <- ifelse(str_detect(mdf$pathological_diagnosis, "Healthy"), "Healthy tissue", mdf$pathological_diagnosis)
meta_data$institution <- "Hospital Universitario la Princesa"
meta_data$organ <- "Thyroid Gland"
meta_data$pathology <- ifelse(meta_data$histo_class == "Healthy", NA_character_, "autoimmune")
meta_data$pub_citation <- citation
meta_data$pub_journal <- "Nature Communications"
meta_data$pub_year <- 2024
meta_data$tissue_age <- mdf$age
meta_data$sex <- ifelse(mdf$sex == "F", "female", "male")
meta_data$workgroup <- "MarazuelaLab"
object <- addSampleMetaData(object, meta_data = meta_data)
# save object
dir <- file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS"))
object <- setSpataDir(object, dir)
saveSpataObject(object)
plot_overview(object)
}
dev.off()
# Ren et al. 2023 ---------------------------------------------------------
subfolder <- "Ren_et_al_2023"
create_subfolder(subfolder)
main_dir <- "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Ren_et_al_2023"
meta_df <- readxl::read_xlsx(path = file.path(main_dir, "meta_data_frame.xlsx"))
all_folders <- list.files(main_dir, full.names = T) %>% str_subset(pattern = "out$")
open_overview_pdf(subfolder)
for(folder in all_folders){
sample_name <-
str_extract_after(folder, pattern = "Ren_et_al_2023\\/") %>%
str_remove(pattern = "_spaceranger_out")
mdf <- filter(meta_df, Sample == {{sample_name}})
object <-
initiateSpataObjectVisium(
sample_name = sample_name,
directory_visium = folder,
img_ref = "hires",
img_active = "lowres"
)
object <- loadImages(object)
# add meta data
meta_data <- list()
meta_data$donor_species <- "Homo sapiens"
meta_data$grade <- "IV"
meta_data$histo_class <- mdf$histo_class
meta_data$comment <- as.character(mdf[["Integrated diagnosis"]])
meta_data$institution <- "West China Hospital"
meta_data$organ <- mdf$organ
meta_data$organ_part <- mdf$organ_part
meta_data$organ_side <- mdf$side
meta_data$pathology <- "tumor"
meta_data$platform <- "VisiumSmall"
meta_data$pub_citation <- citation
meta_data$pub_journal <- "Nature Communications"
meta_data$pub_year <- 2023
meta_data$sex <- NA
meta_data$tags <- c("brain;cns;cancer")
meta_data$tissue_age_approx <- mdf$`Age at diagnosis`
meta_data$workgroup <- "WangLab"
object <- addSampleMetaData(object, meta_data = meta_data)
# save object
dir <- file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS"))
saveSpataObject(object, dir = dir)
plot_overview(object)
}
dev.off()
# Valdeolivas et al 2024 --------------------------------------------------
subfolder <- "Valdeolivas_et_al_2024"
create_subfolder(subfolder)
dir_main <- "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Valdeolivas_et_al_2024"
all_folders <-
list.files(dir_main, full.names = T) %>%
str_subset(pattern = "meta_data", negate = T) %>%
str_subset(pattern = "citation", negate = T)
citation <- readRDS(file.path(dir_main, "citation.RDS"))
meta_df <- readxl::read_xlsx(file.path(dir_main, "meta_data_frame_proc.xlsx"))
annotation_dirs <-
list.files(all_folders[1], full.names = T) %>%
str_subset(".rds$") %>%
str_subset(".csv$", negate = T)
open_overview_pdf(subfolder)
for(folder in all_folders[2:length(all_folders)]){
sample_name <- confuns::str_extract_after(folder, "Valdeolivas_et_al_2024\\/")
sn <-
str_suggest_vec(str_extract(sample_name, "_.*_") %>% str_remove_all("_"), pool = meta_df$sample_name, max.dist = 20, n.top =1)
mdf <- filter(meta_df, sample_name == {{sn}})
if(nrow(mdf) == 0){
warning(glue::glue("no meta data for dir {folder}."))
}
object <-
initiateSpataObjectVisium(
sample_name = sample_name,
directory_visium = folder,
img_ref = "hires",
img_active = "lowres"
)
object <- loadImages(object)
# add meta data
meta_data <- list()
# add pathology annotations
ann_dir <- str_subset(annotation_dirs, sample_name)
if(length(ann_dir) == 1){
df <-
readRDS(ann_dir) %>%
as_tibble() %>%
dplyr::rename(barcodes = spot_id)
object <- addFeatures(object, feature_df = df, feature_names = c("seurat_clusters", "Pathologist_Annotations"))
object@meta_obs$Pathologist_Annotations <- as.factor(object@meta_obs$Pathologist_Annotations)
object@obj_info$spat_segm_vars <- "Pathologist_Annotations"
meta_data$comment <- "contains histological segmentation variable"
}
meta_data$donor_species <- "Homo sapiens"
meta_data$histo_class <- mdf$histo_class
meta_data$institution <- "Ludwig Maximilian University of Munich"
meta_data$organ <- mdf$organ
meta_data$organ_part <- mdf$organ_part
meta_data$pathology <- "tumor"
meta_data$platform <- "VisiumSmall"
meta_data$pub_citation <- citation
meta_data$pub_journal <- "Precision Oncology"
meta_data$pub_year <- 2024
meta_data$sex <- NA
object <- addSampleMetaData(object, meta_data = meta_data)
# add directory
object <- setSpataDir(object, dir = file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS")))
saveSpataObject(object)
}
dev.off()
# Akeret et al. 2022 -------------------------------------------------------
subfolder <- "Akeret_et_al_2022"
create_subfolder(subfolder)
main_dir <- "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Akeret_et_al_2022"
object <-
initiateSpataObjectVisium(sample_name = "MouseBrainHemeInjection", directory_visium = main_dir, img_ref = "hires")
object <- loadImages(object)
inj_df <-
read_csv(file.path(main_dir, "heme_1000_injection_site.csv")) %>%
dplyr::rename(barcodes = Barcode) %>%
mutate(injection_site = if_else(is.na(injection_site), "normal", "heme") %>% as.factor())
anatomy_df<-
read_csv(file.path(main_dir, "heme_1000_anatomy.csv")) %>%
dplyr::rename(barcodes = Barcode) %>%
mutate(anatomy = as.factor(anatomy))
object <- addFeatures(object, inj_df)
object <- addFeatures(object, anatomy_df)
getFeatureNames(object)
meta_data <- list()
meta_data$comment <- "contains grouping variables 'injection_site' and 'anatomy' from the original publication"
meta_data$donor_species <- "Mus musculus"
meta_data$institution <- "University Hospital Zurich"
meta_data$organ <- "Brain"
meta_data$pathology <- "bleeding_model"
meta_data$platform <- object@platform
meta_data$pub_citation <- readRDS(file.path(main_dir, "citation.RDS"))
meta_data$workgroup <- "Primelab"
object <- addSampleMetaData(object, meta_data = meta_data)
dir <- file.path("spata2v3_objects", subfolder, str_c(object@sample, ".RDS"))
saveSpataObject(object, dir)
# Maynard et al 2021 ------------------------------------------------------
subfolder <- "Maynard_et_al_2021"
create_subfolder(subfolder)
main_dir <- "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Maynard_et_al_2021"
all_folders <-
list.files(path = main_dir, full.names = T) %>%
str_subset(pattern = "[0-9]$")
open_overview_pdf(subfolder)
for(folder in all_folders){
sample_name <- confuns::str_extract_after(folder, pattern = "Maynard_et_al_2021\\/")
object <-
initiateSpataObjectVisium(
sample_name = sample_name,
directory_visium = folder,
img_ref = "hires",
img_active = "lowres"
)
meta_data <- list()
meta_data$donor_species <- "Homo sapiens"
meta_data$histo_class <- "Cortex"
meta_data$institution <- "Lieber Institute for Brain Development"
meta_data$organ <- "Cerebrum"
meta_data$organ_part <- "prefrontal"
meta_data$pub_citation <- readRDS(file.path(main_dir, "citation.RDS"))
meta_data$pub_year <- 2021
meta_data$pub_journal <- "Nature Neuroscience"
object <- addSampleMetaData(object, meta_data = meta_data)
dir <- file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS"))
object <- setSpataDir(object, dir)
saveSpataObject(object)
plot_overview(object)
}
dev.off()
# Vizgen MERFISH example data sets --------------------------------
subfolder <- "Vizgen_example_data_sets_Cancer"
create_subfolder(subfolder)
dir_main <- "/Users/heilandr/lab/data/spatial_seq/raw/MERFISH/MERSCOPE-FFPE-Human-Immuno-oncology"
all_folders <- list.files(dir_main, full.names = T)
organs <- c("Breast", "Colon", "Colon", "Liver", "Liver", "Lung", "Lung", "Skin", "Skin", "Ovary", "Ovary", "Ovary", "Ovary", "Prostate", "Prostate", "Uterus", "Uterus", "Uterus")
open_overview_pdf(subfolder)
for(i in seq_along(all_folders)){
folder <- all_folders[i]
sample_name <- dplyr::last(stringr::str_split(folder, "/")[[1]])
object <-
initiateSpataObjectMERFISH(
directory_merfish = folder,
sample_name = sample_name
)
meta_data <- list()
meta_data$donor_species <- "Homo sapiens"
meta_data$institution <- "Vizgen"
meta_data$organ <- organs[i]
meta_data$pathology <- "tumor"
meta_data$platform <- "MERFISH"
meta_data$pub_citation <- "Vizgen MERFISH FFPE Human Immuno-oncology Data Set, May 2022"
meta_data$download_date <- lubridate::as_datetime("2024/01/09")
object <- addSampleMetaData(object, meta_data = meta_data)
dir <- file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS"))
object <- setSpataDir(object, dir)
saveSpataObject(object)
plot_overview(object)
}
dev.off()
subfolder <- "Vizgen_example_data_sets_MouseBrain"
create_subfolder(subfolder)
dir_main <- "/Users/heilandr/lab/data/spatial_seq/raw/MERFISH/MERSCOPE-Mouse-Brain-Receptor-Map"
all_folders <- list.files(dir_main, full.names = T)
open_overview_pdf(subfolder)
for(i in seq_along(all_folders)){
folder <- all_folders[i]
sample_name <- dplyr::last(stringr::str_split(folder, "/")[[1]])
object <-
initiateSpataObjectMERFISH(
directory_merfish = folder,
sample_name = sample_name
)
meta_data <- list()
meta_data$donor_species <- "Mus musculus"
meta_data$institution <- "Vizgen"
meta_data$organ <- "Brain"
meta_data$pathology <- NA_character_
meta_data$platform <- "MERFISH"
meta_data$pub_citation <- "Vizgen MERFISH Mouse Brain Receptor Map"
meta_data$download_date <- lubridate::as_datetime("2024/08/19")
object <- addSampleMetaData(object, meta_data = meta_data)
dir <- file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS"))
object <- setSpataDir(object, dir)
saveSpataObject(object)
plot_overview(object)
}
dev.off()
subfolder <- "Vizgen_example_data_sets_MouseLiver"
create_subfolder(subfolder)
dir_main <- "/Users/heilandr/lab/data/spatial_seq/raw/MERFISH/MERSCOPE-Mouse-Liver-Map"
all_folders <- list.files(dir_main, full.names = T)
open_overview_pdf(subfolder)
for(i in seq_along(all_folders)){
folder <- all_folders[i]
sample_name <- dplyr::last(stringr::str_split(folder, "/")[[1]])
object <-
initiateSpataObjectMERFISH(
directory_merfish = folder,
sample_name = sample_name
)
meta_data <- list()
meta_data$donor_species <- "Mus musculus"
meta_data$institution <- "Vizgen"
meta_data$organ <- "Liver"
meta_data$pathology <- NA_character_
meta_data$platform <- "MERFISH"
meta_data$download_date <- "2024/08/21"
object <- addSampleMetaData(object, meta_data = meta_data)
dir <- file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS"))
object <- setSpataDir(object, dir)
saveSpataObject(object)
plot_overview(object)
}
dev.off()
subfolder <- "Vizgen_example_data_sets_HumanBrain_AD"
create_subfolder(subfolder)
dir_main <- "/Users/heilandr/lab/data/spatial_seq/raw/MERFISH/MERSCOPE-1000-Gene-Panel-Human-Brain-AD"
all_folders <- list.files(dir_main, full.names = T)
open_overview_pdf(subfolder)
for(i in seq_along(all_folders)){
folder <- all_folders[i]
sample_name <- dplyr::last(stringr::str_split(folder, "/")[[1]])
object <-
initiateSpataObjectMERFISH(
directory_merfish = folder,
sample_name = sample_name
)
meta_data <- list()
meta_data$donor_species <- "Homo sapiens"
meta_data$institution <- "Vizgen"
meta_data$organ <- "Brain"
meta_data$organ_part <- "Unspecified"
meta_data$pathology <- "AD"
meta_data$platform <- "MERFISH"
meta_data$download_date <- lubridate::as_datetime("2024/08/21")
object <- addSampleMetaData(object, meta_data = meta_data)
dir <- file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS"))
object <- setSpataDir(object, dir)
saveSpataObject(object)
plot_overview(object)
}
dev.off()
theMILOlab/SPATAData documentation built on Aug. 27, 2024, 5:04 p.m.
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######---------- script to populate spata2v3 folder ----------#####
# data sets are uploaded in batches according to their "source"
# this script is structured by:
# -> source -------------------------------------------------------
source("scripts/init_session.R")
# TENxVisiumData ----------------------------------------------------------
#BiocManager::install("HelenaLC/TENxVisiumData")
library(TENxVisiumData)
eh <- ExperimentHub() # initialize hub instance
q <- query(eh, "TENxVisium") # retrieve 'TENxVisiumData' records
ids <- q$ah_id
nms <- c("ah_id", "title", "dataprovider", "species", "taxonomyid", "description", "maintainer")
out <- list()
for(i in seq_along(ids)){
title <- q$title[[i]]
id <- q$ah_id[[i]]
out[[title]] <-
list(
id = ids[[i]],
title = title,
desc = q$description[[i]],
species = q$species[[i]],
dpr = q$dataprovider[[i]],
spe = eh[[id]]
)
}
obj_names <- map_chr(out, .f = ~.x$title) %>% unname()
obj_names <-
str_subset(obj_names, "v3", negate = T) %>%
str_subset("BreastCancer", negate = T) %>%
str_subset("Colorectal", negate = T) %>%
str_subset("Ovarian", negate = T)
pdf(file = "spata2v3_objects/TENxVisiumData/sample_overview.pdf")
for(i in seq_along(obj_names)){
obj_name <- obj_names[i]
lst <- out[[obj_name]]
spe_all <- out[[obj_name]]$spe
img_df <- imgData(spe_all)
print(img_df)
for(s_id in img_df$sample_id){
# assemble object
print(s_id)
spe <- spe_all[, spe_all$sample_id == s_id]
count_mtr <- spe@assays@data$counts
cdf <- colData(spe)
coords_df <-
spatialCoords(spe) %>%
as.data.frame() %>%
rownames_to_column("barcodes") %>%
as_tibble() %>%
dplyr::rename(x_orig = pxl_col_in_fullres, y_orig = pxl_row_in_fullres)
coords_df$barcodes <- str_replace(coords_df$barcodes, pattern = "\\.", replacement = "-")
isf <- img_df[img_df$sample_id == s_id,]$scaleFactor
image_obj <- getImg(spe)
image <-
image_obj@image %>%
as.matrix() %>%
EBImage::as.Image()
if(base::any(coords_df$barcodes %in% visium_spots$VisiumSmall$barcode)){
method <- "VisiumSmall"
} else if(base::any(coords_df$barcodes %in% visium_spots$VisiumLarge$barcode)){
method <- "VisiumLarge"
}
object <-
initiateSpataObject(
sample_name = s_id,
coords_df = coords_df,
count_mtr = count_mtr,
modality = "gene",
spatial_method = method,
img = image,
img_name = "lowres",
scale_factors = list(image = isf)
)
# add meta data
meta_data <- list()
meta_data$institution <- "10X Genomics"
meta_data$donor_species <- ifelse(str_detect(obj_name, "Human"), "Homo sapiens", "Mus musculus")
if(str_detect(obj_name, "HumanCerebellum")){
meta_data$organ <- "Cerebellum"
meta_data$histo_class = "Cortex"
} else if(obj_name == "HumanLymphNode"){
meta_data$organ <- "Lymph Node"
meta_data$histo_class = "Lymph Node"
} else if(str_detect(obj_name, "Glioblastoma")){
meta_data$organ <- "Cerebrum"
meta_data$pathology <- "tumor"
meta_data$histo_class = "Glioblastoma"
meta_data$grade <- "IV"
} else if(obj_name == "MouseKidneyCoronal"){
meta_data$organ <- "Kidney"
meta_data$histo_class = "Kidney"
} else if(obj_name == "HumanHeart"){
meta_data$organ <- "Heart"
meta_data$histo_class <- "Cardiac Muscle"
} else if(obj_name == "HumanSpinalCord"){
meta_data$organ <- "Spinal Cord"
meta_data$histo_class <- "Spinal Cord"
} else if(str_detect(obj_name, "Brain")){
meta_data$organ <- "Brain"
}
meta_data$platform <- method
# save object
file_name <- str_c(s_id, ".RDS")
dir <- file.path("spata2v3_objects", "TENxVisiumData", file_name)
object <- setSpataDir(object, dir = dir)
object <- addSampleMetaData(object, meta_data = meta_data)
object <- computePixelScaleFactor(object)
object <- identifyTissueOutline(object)
print(object)
print(dir)
saveRDS(object, dir)
p_overview <-
(plotSurface(object, pt_alpha = 0) + labs(subtitle = object@sample)) +
(plotSurface(object, color_by = "tissue_section") + legendBottom())
plot(p_overview)
}
}
dev.off()
# Kuppe et al. 2022 -------------------------------------------------------
dir.create("spata2v3_objects/Kuppe_et_al_2022")
heart_dirs <-
list.files(path = "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Kuppe_et_al_2022", full.names = T) %>%
str_subset(pattern = "\\.", negate = T) %>%
file.path("outs")
sample_names <-
list.files(path = "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Kuppe_et_al_2022") %>%
str_subset(pattern = "\\.", negate = T)
mdf <- readRDS("/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Kuppe_et_al_2022/meta_df_processed.RDS")
mdf$major_label <- mdf$major_labl
labels <- c("FZ" = "fibrotic_zone", "IZ" = "ischaemic_zone", "BZ" = "border_zone", "CTRL" = "control", "RZ" = "remote_zone")
pdf(file = "spata2v3_objects/Kuppe_et_al_2022/sample_overview.pdf")
for(i in seq_along(heart_dirs)){
sample_name <- sample_names[i]
dir <- heart_dirs[i]
object <-
initiateSpataObjectVisium(
sample_name = sample_name,
directory_visium = dir
)
# add meta data
mdf_sub <- filter(mdf, hca_sample_id == {{sample_name}})
meta_data <- list()
meta_data$donor_id <- mdf_sub$patient
meta_data$donor_species <- "Homo sapiens"
meta_data$institution <- "University Clinic Aachen"
meta_data$organ <- "Heart"
meta_data$histo_class = "Cardiac Muscle"
meta_data$histo_class_sub = labels[mdf$major_label[i]]
meta_data$pathology <- "myocardial_infarction"
meta_data$pub_citation <-
"Kuppe, C., Ramirez Flores, R.O., Li, Z. et al. Spatial multi-omic map of human myocardial infarction. Nature 608, 766–777 (2022). https://doi.org/10.1038/s41586-022-05060-x"
meta_data$pub_doi <- "doi.org/10.1038/s41586-022-05060-x"
meta_data$pub_journal <- "Nature"
meta_data$pub_year <- 2022
meta_data$patient_group <- mdf_sub$patient_group
meta_data$patient_region_id <- mdf_sub$patient_region_id
meta_data$tags <- c(mdf$major_label[i])
meta_data$platform <- "VisiumSmall"
meta_data$workgroup <- "KuppeLab"
object <- addSampleMetaData(object, meta_data = meta_data)
# save object
file_name <- str_c(sample_name, ".RDS")
dir <- file.path("spata2v3_objects", "Kuppe_et_al_2022", file_name)
object <- setSpataDir(object, dir = dir)
print(object)
print(dir)
saveRDS(object, dir)
# plot
p_overview <-
(plotSurface(object, pt_alpha = 0) + labs(subtitle = object@sample)) +
(plotSurface(object, color_by = "tissue_section") + legendBottom())
plot(p_overview)
}
dev.off()
# Ravi et al. 2022 --------------------------------------------------------
dir.create("spata2v3_objects/Ravi_et_al_2022")
mdf <- readRDS("/Users/heilandr/lab/data/spatial_seq/meta/spatial_seq_meta.RDS")
patient_ids <-
c('#275UKF', '#270UKF', '#269UKF', '#268UKF', '#266UKF', '#265UKF', '#265UKF', '#262UKF', '#260UKF', '#259UKF',
'#259UKF', '#256UKF', '#256UKF', '#255UKF', '#251UKF', '#248UKF', '#248UKF', '#243UKF', '#242UKF', '#242UKF',
'#241UKF', '#296UKF', '#304UKF', '#313UKF', '#313UKF', '#334UKF', '#334UKF', '#212UKF', '#217UKF', '#218UKF')
patient_ids_new <-
str_replace(patient_ids, "#", "UKF") %>%
str_remove("UKF$")
ravi_dirs <-
list_raw_visium() %>%
SPATA2::vselect(contains(patient_ids_new))
pdf("spata2v3_objects/Ravi_et_al_2022/sample_overview.pdf")
for(i in 1:(nrow(source_df)-1)){
old_id <- patient_ids[i]
new_id <- patient_ids_new[i]
raw_dirs <-
list_raw_visium() %>%
vselect(contains(new_id))
for(raw_dir in raw_dirs){
suffix <-
confuns::str_extract_after(raw_dir, pattern = new_id ) %>%
str_remove_all(pattern = "_") %>%
str_remove(pattern = "P$")
sample_name <- str_c(new_id, suffix)
object <-
initiateSpataObjectVisium(
sample_name = sample_name,
directory_visium = file.path(raw_dir, "outs")
)
sname <- sample_name
# add meta data
smdf <- source_df_v2[i, ]
mdf_sub <- filter(mdf, Samples_ID == smdf$sample)
meta_data <- list()
meta_data$donor_id <- new_id
meta_data$donor_species <- "Homo sapiens"
meta_data$grade <- ifelse(str_detect(sname, "T"), smdf$who_grade, NA_character_)
meta_data$histo_class <- ifelse(str_detect(sname, "C"), "Cortex", smdf$hist_classification)
meta_data$institution <- "University Clinic Freiburg"
meta_data$organ <- "Cerebrum"
meta_data$organ_part <- smdf$anatomical_region
meta_data$pathology <- ifelse(str_detect(sname, "T"), "tumor", "infiltrated")
meta_data$platform <- getSpatialMethod(object)@name
meta_data$pub_citation <-
"VM Ravi, P Will, J Kueckelhaus, et al. Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma. Cancer Cell. 2022 Jun 13;40(6):639-655.e13. doi: 10.1016/j.ccell.2022.05.009. PMID: 35700707."
meta_data$pub_doi <- "10.1016/j.ccell.2022.05.009"
meta_data$pub_journal <- "Cancer Cell"
meta_data$pub_year <- 2022
meta_data$tags <- c("brain", "cns", "cancer")
meta_data$workgroup <- "MILOlab"
if(nrow(mdf_sub) != 0){
meta_data$age <- as.numeric(mdf_sub$Age_atSampling) %>% round(digits = 0)
}
object <- addSampleMetaData(object, meta_data = meta_data)
# save object
file_name <- str_c(sample_name, ".RDS")
dir <- file.path("spata2v3_objects", "Ravi_et_al_2022", file_name)
object <- setSpataDir(object, dir = dir)
print(object)
print(dir)
saveRDS(object, dir)
p_overview <-
(plotSurface(object, pt_alpha = 0) + labs(subtitle = object@sample)) +
(plotSurface(object, color_by = "tissue_section") + legendBottom())
plot(p_overview)
}
}
dev.off()
# 10X Visium example data sets --------------------------------------------
dir.create("spata2v3_objects/10X_example_data_sets")
example_dirs <-
list.files(path = "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/10X_example_data_sets", full.names = T) %>%
str_subset(pattern = ".RDS", negate = T) %>%
str_subset(pattern = "targeted", negate = T)
mdf <- readRDS("/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/10X_example_data_sets/meta_df.RDS")
pdf(file = "spata2v3_objects/10X_example_data_sets/sample_overview.pdf")
for(ed in example_dirs){
sample_name <-
confuns::str_extract_after(ed, pattern = "10X_example_data_sets\\/") %>%
str_replace_all(pattern = "-", replacement = "_")
object <-
initiateSpataObjectVisium(
sample_name = sample_name,
directory_visium = ed
)
meta_data <- list()
meta_data$donor_species <- "Homo sapiens"
meta_data$institution <- "10X Genomics"
if(str_detect(sample_name, "Breast")){
meta_data$organ <- "Breast"
} else if(str_detect(sample_name, "Colorectal")){
meta_data$organ <- "Colon"
} else if(str_detect(sample_name, "Lung")){
meta_data$organ <- "Lung"
} else if(str_detect(sample_name, "Prostate")){
meta_data$organ <- "Prostate"
} else if(str_detect(sample_name, "Ovarian")){
meta_data$organ <- "Ovary"
}
meta_data$pathology <- "tumor"
meta_data$platform <- getSpatialMethod(object)@name
meta_data$tags <- c("cancer")
object <- addSampleMetaData(object, meta_data = meta_data)
file_name <- str_c(sample_name, ".RDS")
dir <- file.path("spata2v3_objects", "10X_example_data_sets", file_name)
object <- setSpataDir(object, dir = dir)
print(object)
print(dir)
saveRDS(object, dir)
p_overview <-
(plotSurface(object, pt_alpha = 0) + labs(subtitle = object@sample)) +
(plotSurface(object, color_by = "tissue_section") + legendBottom())
plot(p_overview)
}
dev.off()
# 10X Visium example data sets VisiumLarge --------------------------------
subfolder <- "10X_example_data_sets_VisiumLarge"
create_subfolder(subfolder)
dir_main <- "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/10X_example_data_sets_VisiumLarge"
all_folders <- list.files(dir_main, full.names = T)
organs <- c("Colon", "Cerebrum", "Kidney", "Lung", "Ovary")
pathology <- c("tumor", "tumor", NA, "tumor", "tumor")
open_overview_pdf(subfolder)
for(i in seq_along(all_folders)){
folder <- all_folders[i]
sample_name <- confuns::str_extract_after(folder, "VisiumLarge\\/")
object <-
initiateSpataObjectVisium(
directory_visium = folder,
sample_name = sample_name
)
meta_data <- list()
meta_data$donor_species <- "Homo sapiens"
meta_data$institution <- "10X Genomics"
meta_data$organ <- organs[i]
meta_data$pathology <- pathology[i]
meta_data$platform <- "VisiumLarge"
if(meta_data$organ == "Cerebrum"){
meta_data$histo_class <- "Glioblastoma"
}
object <- addSampleMetaData(object, meta_data = meta_data)
dir <- file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS"))
object <- setSpataDir(object, dir)
saveSpataObject(object)
plot_overview(object)
}
dev.off()
# 10X Visium example data sets GP -----------------------------------------
subfolder <- "10X_example_data_sets_GP"
create_subfolder(subfolder)
main_dir <- "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/10X_example_data_sets_GeneAndProtein"
all_folders <- list.files(main_dir, full.names = T)
organs <- c("Breast", "Cerebrum", "Tonsil")
histo_class <- c(NA, "Glioblastoma", NA)
open_overview_pdf(subfolder)
for(i in seq_along(all_folders)){
folder <- all_folders[i]
sample_name <- confuns::str_extract_after(folder, "GeneAndProtein\\/")
sample_name <- stringr::str_c(sample_name, "GP")
object <- initiateSpataObjectVisium(sample_name, directory_visium = folder, img_ref = "hires")
object <- loadImages(object)
# add meta data
meta_data <- list()
meta_data$donor_species <- "Homo sapiens"
meta_data$grade <- ifelse(organs[i] == "Cerebrum", "IV", NA_character_)
meta_data$histo_class <- histo_class[i]
meta_data$institution <- "10X Genomics"
meta_data$organ <- organs[i]
meta_data$pathology <- c("tumor", "tumor", NA)[i]
meta_data$platform <- object@platform
object <- addSampleMetaData(object, meta_data = meta_data)
dir <- file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS"))
saveSpataObject(object, dir = dir)
plot_overview(object)
}
dev.off()
# 10X example data sets Visium HD -----------------------------------------
subfolder <- "10X_example_data_sets_VHD"
create_subfolder(subfolder)
main_dir <- "/Users/heilandr/lab/data/spatial_seq/raw/10XVisiumHD/10X_example_data_sets_VHD"
all_folders <- list.files(main_dir, full.names = T)
open_overview_pdf(subfolder)
for(i in seq_along(all_folders)){
folder <- all_folders[i]
sample_name <- confuns::str_extract_after(folder, "10X_example_data_sets_VHD\\/")
object <-
initiateSpataObjectVisiumHD(
directory_visium = folder,
sample_name = sample_name,
img_ref = "hires",
img_active = "lowres"
)
object <- loadImages(object)
meta_data <- list()
meta_data$donor_species <- "Homo sapiens"
meta_data$organ <- c("Lung", "Pancreas")[i]
meta_data$institution <- "10X Genomics"
meta_data$pathology <- c("tumor", NA)[i]
meta_data$platform = "VisiumHD"
object <- addSampleMetaData(object, meta_data = meta_data)
dir <- file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS"))
object <- setSpataDir(object, dir)
saveSpataObject(object)
plot_overview(object)
}
dev.off()
# Regal et al. 2023 -------------------------------------------------------
dir.create("spata2v3_objects/Regal_et_al_2023")
dirs_spata <- list.files("/Users/heilandr/lab/data/spatial_seq/proc/spata_objects/SPATA2v1_objects", full.names = T)
age <- c(10, 16, 17, 20)
loc <- c(0, 2, 2, 2)
sex <- c("male", "male", "female", "male")
cd34 <- c("neg", rep("pos", 3))
pdf("spata2v3_objects/Regal_et_al_2023/sample_overview.pdf")
for(i in seq_along(dirs_spata)){
sd <- dirs_spata[i]
sobj <- readRDS(sd)
count_mtr <- sobj@data[[1]]$counts
img <- sobj@images[[1]]@image %>% EBImage::flip()
coords_df <- sobj@images[[1]]@coordinates[,c("barcodes", "x", "y")]
sample_name <- str_c("GG_Extern_", i)
object <-
initiateSpataObject(
sample_name = sample_name,
count_mtr = count_mtr,
coords_df = coords_df,
modality = "gene",
img = img,
img_name = "lowres",
scale_factors = list(image = 1),
spatial_method = "VisiumSmall"
)
object <- computePixelScaleFactor(object)
object <- identifyTissueOutline(object)
# add sample meta data
meta_data <- list()
meta_data$pub_citation <-
"Regal, J.A., Guerra García, M.E., Jain, V. et al. Ganglioglioma deep transcriptomics reveals primitive neuroectoderm neural precursor-like population.
acta neuropathol commun 11, 50 (2023). https://doi.org/10.1186/s40478-023-01548-3"
meta_data$pub_doi <- "https://doi.org/10.1186/s40478-023-01548-3"
meta_data$pub_journal <- "Acta neuropathologica"
meta_data$pub_year <- 2023
meta_data$institution <- "Duke University"
meta_data$workgroup <- "ReitmanLab"
meta_data$source <- "Regal_et_al_2023"
meta_data$organ <- ifelse(i == 1, "Brainstem", "Cerebrum")
if(i > 1){
meta_data$organ_part <- "temporal"
}
meta_data$histo_class <- "Ganglioglioma"
meta_data$grade <- "I"
meta_data$tissue_age <- age[i]
meta_data$platform <- "VisiumSmall"
meta_data$donor_species <- "Homo sapiens"
meta_data$sex <- sex[i]
meta_data$tags <- c(str_c("CD34_", cd34[i]))
meta_data$pathology <- "tumor"
object <- addSampleMetaData(object, meta_data = meta_data)
# save object
file_name <- str_c(sample_name, ".RDS")
dir <- file.path("spata2v3_objects", "Regal_et_al_2023", file_name)
object <- setSpataDir(object, dir = dir)
print(object)
print(dir)
saveRDS(object, dir)
p_overview <-
(plotSurface(object, pt_alpha = 0) + labs(subtitle = object@sample)) +
(plotSurface(object, color_by = "tissue_section") + legendBottom())
plot(p_overview)
}
dev.off()
# Kueckelhaus et al. 2024 -------------------------------------------------
dir.create("spata2v3_objects/Kueckelhaus_et_al_2024")
# uplaod mouse brain data set
obj <- readRDS("/Users/heilandr/lab/projects/SPATA2/review/data/mc_damaged.RDS")
obj@version$major <- 2
obj@version$minor <- 4
object <- updateSpataObject(obj)
meta_data <- list()
meta_data$comment <- "Injury annotations present as ImageAnotations."
meta_data$donor_species <- "Mus musculus"
meta_data$organ <- "Brain"
meta_data$pathology <- "stab_wound"
meta_data$pub_citation <- ""
meta_data$pub_journal <- "Nature Communications"
meta_data$platform <- "VisiumSmall"
meta_data$institution <- "Ludwig Maximilian University of Munich"
meta_data$workgroup <- "DichgansLab"
meta_data$sex <- "male"
meta_data$source <- "Kueckelhaus_et_al_2024"
object <- addSampleMetaData(object, meta_data = meta_data)
object <- renameSpataObject(object, sample_name = "LMU_MCI")
dir <- "spata2v3_objects/Kueckelhaus_et_al_2024/LMU_MCI.RDS"
object <- setSpataDir(object, dir = dir)
object <- setDefault(object, display_image = T)
object <- identifyTissueOutline(object)
saveSpataObject(object)
# Greenwald et al. 2024 ---------------------------------------------------
library(Seurat)
library(SPATA2)
library(stringr)
library(jsonlite)
library(tidyverse)
library(confuns)
subfolder <- "Greenwald_et_al_2024"
create_subfolder(subfolder)
citation <- readRDS("/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Greenwald_et_al_2024/citation.RDS")
# process meta
meta_df <-
read_csv("/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Greenwald_et_al_2024/meta_data_frame.csv")
colnames(meta_df) <- tolower(colnames(meta_df)) %>% str_replace_all("-", "_")
meta_df <-
tidyr::separate(meta_df, col = location, into = c("organ_side", "organ_part"))
meta_df <- meta_df[1:21, ]
meta_df$organ_part[meta_df$organ_side == "bifrontal"] <- "frontal"
meta_df$organ_side[meta_df$organ_side == "bifrontal"] <- "both"
main_dir <- "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Greenwald_et_al_2024"
all_dirs <-
list.files(main_dir, full.names = T) %>%
str_subset(".csv$", negate = T) %>%
str_subset("citation", negate = T)
open_overview_pdf(subfolder)
for(main_dir in all_dirs){
sample_name_prel <- confuns::str_extract_after(main_dir, pattern = "Greenwald_et_al_2024\\/")
sample_name <- str_suggest_vec(sample_name_prel, pool = meta_df$sample_id, max.dist = 10, n.top = 1)
mdf <- filter(meta_df, sample_id == {{sample_name}})
if(nrow(mdf) == 0){
message(glue::glue("No meta data for dir {main_dir}."))
} else {
message(glue::glue("Working on dir {main_dir}."))
# get directories
all_files <- list.files(main_dir, full.names = T)
dir_mtr <- str_subset(all_files, "\\.h5$")
dir_coords <- str_subset(all_files, "tissue_positions")
dir_img <- str_subset(all_files, "lowres_image|hires_image")
dir_sfs <- str_subset(all_files, "scalefactors")
# counts
mtr <- Seurat::Read10X_h5(filename = dir_mtr)
# image related
image <- EBImage::readImage(files = dir_img)
name_img_ref <- ifelse(str_detect(dir_img, "hires"), "hires", "lowres")
scale_factors <- read_json(dir_sfs)
image_sf <- scale_factors[[str_c("tissue_", name_img_ref, "_scalef")]]
# coords
if(is_empty(dir_coords)){
coords_df <- visium_spots$VisiumSmall %>% rename(barcodes = barcode)
coords_df$x_orig <- 0
coords_df$y_orig <- 0
coords_df <- filter(coords_df, barcodes %in% colnames(mtr))
warning(glue::glue("no coords for sample {sample_name}"))
} else {
coords_df <-
read_coords_visium(dir_coords) %>%
dplyr::filter(in_tissue == 1)
}
# initiate object
object <-
initiateSpataObject(
sample_name = sample_name,
count_mtr = mtr,
coords_df = coords_df,
modality = "gene",
img = image,
img_name = name_img_ref,
scale_factors = list(image = image_sf),
spatial_method = VisiumSmall
)
# visium specifics
object <- computePixelScaleFactor(object)
object <- identifyTissueOutline(object)
spot_size <-
scale_factors$fiducial_diameter_fullres *
scale_factors[[stringr::str_c("tissue", name_img_ref, "scalef", sep = "_")]] /
base::max(dim(image))*100
spot_scale_fct <- 1.15
sp_data <- getSpatialData(object)
sp_data@method@method_specifics[["spot_size"]] <- spot_size * spot_scale_fct
object <- setDefault(object, pt_size = spot_size*spot_scale_fct)
object <- setSpatialData(object, sp_data)
# add meta data
meta_data <- list()
meta_data$donor_id <- mdf$patient_id
meta_data$sex <- ifelse(mdf$sex == "F", "female", "male")
meta_data$tissue_age <- mdf$age
meta_data$donor_species <- "Homo sapiens"
meta_data$grade <- "IV"
if(str_detect(sample_name, "ZH")){
meta_data$institution <- "University Hospital Zurich"
} else if(str_detect(sample_name, "MGH")){
meta_data$institution <- "Massachusetts General Hospital"
} else if(str_detect(sample_name, "BWH")){
meta_data$institution <- "Brigham and Women's Hospital"
}
meta_data$organ <- "Cerebrum"
meta_data$organ_side <- mdf$organ_side
meta_data$organ_part <- mdf$organ_part
meta_data$grade <- WHOGrades(mdf$grade)
meta_data$pathology <- "tumor"
meta_data$platform <- "VisiumSmall"
if(mdf$histology == "GBM"){
meta_data$histo_class <- "Glioblastoma"
} else if(mdf$histology == "ODG"){
meta_data$histo_class <- "Oligodendroglioma"
} else if(mdf$histology == "AA"){
meta_data$histo_class <- "Astrocytoma"
}
meta_data$pub_citation <- citation
meta_data$pub_journal <- "Cell"
meta_data$pub_year <- 2024
meta_data$workgroup <- "TiroshLab"
meta_data$mgmt_status <- mdf$mgmt_status
meta_data$IDH_mut <- str_detect(mdf$tumor, "IDH-mut")
meta_data$tags <- c("cancer", "brain", "cns")
object <- addSampleMetaData(object, meta_data = meta_data)
# save object
dir <- file.path("spata2v3_objects", "Greenwald_et_al_2024", str_c(sample_name, ".RDS"))
object <- setSpataDir(object, dir)
saveSpataObject(object)
# plot
p_overview <-
(plotSurface(object, pt_alpha = 0) + labs(subtitle = object@sample)) +
(plotSurface(object, color_by = "tissue_section") + legendBottom())
plot(p_overview)
}
}
dev.off()
# Martinez-Hernandez_et_al_2024 -------------------------------------------
library(Matrix)
subfolder <- "Martinez_Hernandez_et_al_2024"
create_subfolder(subfolder)
dir_main <- "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Martinez_Hernandez_et_al_2024"
dir_samples <-
list.files(dir_main, full.names = T) %>%
str_subset("citation.RDS", negate = T) %>%
str_subset("meta_data_frame.csv", negate = T) %>%
str_subset("supp_info", negate = T)
citation <- readRDS(file.path(dir_main, "citation.RDS"))
meta_df <- read_csv(file.path(dir_main, "meta_data_frame.csv"))
colnames(meta_df) <- tolower(colnames(meta_df)) %>% str_replace_all("-| ", "_")
meta_df <- filter(meta_df, !is.na(visium_samples))
open_overview_pdf(subfolder)
for(dir_sample in dir_samples){
sample_name <- str_extract_after(dir_sample, "et_al_2024\\/")
mdf <- filter(meta_df, visium_samples == {{sample_name}})
all_files <- list.files(dir_sample, full.names = T)
# mtr
mtr <- read_matrix_mtx(dir_sample)
dir_coords <- str_subset(all_files, "tissue_positions")
dir_img <- str_subset(all_files, "hires_image")
dir_sfs <- str_subset(all_files, "scalefactors")
# image related
image <- EBImage::readImage(files = dir_img)
name_img_ref <- ifelse(str_detect(dir_img, "hires"), "hires", "lowres")
scale_factors <- read_json(dir_sfs)
image_sf <- scale_factors[[str_c("tissue_", name_img_ref, "_scalef")]]
# coords
if(is_empty(dir_coords)){
coords_df <- visium_spots$VisiumSmall %>% rename(barcodes = barcode)
coords_df$x_orig <- 0
coords_df$y_orig <- 0
coords_df <- filter(coords_df, barcodes %in% colnames(mtr))
warning(glue::glue("no coords for sample {sample_name}"))
} else {
coords_df <- read_coords_visium(dir_coords) %>% filter(in_tissue == 1)
}
object <-
initiateSpataObject(
sample_name = sample_name,
count_mtr = mtr,
modality = "gene",
coords_df = coords_df,
img = image,
img_name = "hires",
scale_factors = list(image = image_sf),
spatial_method = VisiumSmall
)
object <- computePixelScaleFactor(object)
object <- identifyTissueOutline(object)
image_lowres <- EBImage::readImage(str_subset(all_files, "lowres_image"))
object <-
registerImage(
object = object,
img = image_lowres,
img_name = "lowres"
)
object <- activateImage(object, img_name = "lowres", unload = F)
# add meta data
meta_data <- list()
meta_data$donor_species <- "Homo sapiens"
meta_data$histo_class <- ifelse(str_detect(mdf$pathological_diagnosis, "Healthy"), "Healthy tissue", mdf$pathological_diagnosis)
meta_data$institution <- "Hospital Universitario la Princesa"
meta_data$organ <- "Thyroid Gland"
meta_data$pathology <- ifelse(meta_data$histo_class == "Healthy", NA_character_, "autoimmune")
meta_data$pub_citation <- citation
meta_data$pub_journal <- "Nature Communications"
meta_data$pub_year <- 2024
meta_data$tissue_age <- mdf$age
meta_data$sex <- ifelse(mdf$sex == "F", "female", "male")
meta_data$workgroup <- "MarazuelaLab"
object <- addSampleMetaData(object, meta_data = meta_data)
# save object
dir <- file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS"))
object <- setSpataDir(object, dir)
saveSpataObject(object)
plot_overview(object)
}
dev.off()
# Ren et al. 2023 ---------------------------------------------------------
subfolder <- "Ren_et_al_2023"
create_subfolder(subfolder)
main_dir <- "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Ren_et_al_2023"
meta_df <- readxl::read_xlsx(path = file.path(main_dir, "meta_data_frame.xlsx"))
all_folders <- list.files(main_dir, full.names = T) %>% str_subset(pattern = "out$")
open_overview_pdf(subfolder)
for(folder in all_folders){
sample_name <-
str_extract_after(folder, pattern = "Ren_et_al_2023\\/") %>%
str_remove(pattern = "_spaceranger_out")
mdf <- filter(meta_df, Sample == {{sample_name}})
object <-
initiateSpataObjectVisium(
sample_name = sample_name,
directory_visium = folder,
img_ref = "hires",
img_active = "lowres"
)
object <- loadImages(object)
# add meta data
meta_data <- list()
meta_data$donor_species <- "Homo sapiens"
meta_data$grade <- "IV"
meta_data$histo_class <- mdf$histo_class
meta_data$comment <- as.character(mdf[["Integrated diagnosis"]])
meta_data$institution <- "West China Hospital"
meta_data$organ <- mdf$organ
meta_data$organ_part <- mdf$organ_part
meta_data$organ_side <- mdf$side
meta_data$pathology <- "tumor"
meta_data$platform <- "VisiumSmall"
meta_data$pub_citation <- citation
meta_data$pub_journal <- "Nature Communications"
meta_data$pub_year <- 2023
meta_data$sex <- NA
meta_data$tags <- c("brain;cns;cancer")
meta_data$tissue_age_approx <- mdf$`Age at diagnosis`
meta_data$workgroup <- "WangLab"
object <- addSampleMetaData(object, meta_data = meta_data)
# save object
dir <- file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS"))
saveSpataObject(object, dir = dir)
plot_overview(object)
}
dev.off()
# Valdeolivas et al 2024 --------------------------------------------------
subfolder <- "Valdeolivas_et_al_2024"
create_subfolder(subfolder)
dir_main <- "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Valdeolivas_et_al_2024"
all_folders <-
list.files(dir_main, full.names = T) %>%
str_subset(pattern = "meta_data", negate = T) %>%
str_subset(pattern = "citation", negate = T)
citation <- readRDS(file.path(dir_main, "citation.RDS"))
meta_df <- readxl::read_xlsx(file.path(dir_main, "meta_data_frame_proc.xlsx"))
annotation_dirs <-
list.files(all_folders[1], full.names = T) %>%
str_subset(".rds$") %>%
str_subset(".csv$", negate = T)
open_overview_pdf(subfolder)
for(folder in all_folders[2:length(all_folders)]){
sample_name <- confuns::str_extract_after(folder, "Valdeolivas_et_al_2024\\/")
sn <-
str_suggest_vec(str_extract(sample_name, "_.*_") %>% str_remove_all("_"), pool = meta_df$sample_name, max.dist = 20, n.top =1)
mdf <- filter(meta_df, sample_name == {{sn}})
if(nrow(mdf) == 0){
warning(glue::glue("no meta data for dir {folder}."))
}
object <-
initiateSpataObjectVisium(
sample_name = sample_name,
directory_visium = folder,
img_ref = "hires",
img_active = "lowres"
)
object <- loadImages(object)
# add meta data
meta_data <- list()
# add pathology annotations
ann_dir <- str_subset(annotation_dirs, sample_name)
if(length(ann_dir) == 1){
df <-
readRDS(ann_dir) %>%
as_tibble() %>%
dplyr::rename(barcodes = spot_id)
object <- addFeatures(object, feature_df = df, feature_names = c("seurat_clusters", "Pathologist_Annotations"))
object@meta_obs$Pathologist_Annotations <- as.factor(object@meta_obs$Pathologist_Annotations)
object@obj_info$spat_segm_vars <- "Pathologist_Annotations"
meta_data$comment <- "contains histological segmentation variable"
}
meta_data$donor_species <- "Homo sapiens"
meta_data$histo_class <- mdf$histo_class
meta_data$institution <- "Ludwig Maximilian University of Munich"
meta_data$organ <- mdf$organ
meta_data$organ_part <- mdf$organ_part
meta_data$pathology <- "tumor"
meta_data$platform <- "VisiumSmall"
meta_data$pub_citation <- citation
meta_data$pub_journal <- "Precision Oncology"
meta_data$pub_year <- 2024
meta_data$sex <- NA
object <- addSampleMetaData(object, meta_data = meta_data)
# add directory
object <- setSpataDir(object, dir = file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS")))
saveSpataObject(object)
}
dev.off()
# Akeret et al. 2022 -------------------------------------------------------
subfolder <- "Akeret_et_al_2022"
create_subfolder(subfolder)
main_dir <- "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Akeret_et_al_2022"
object <-
initiateSpataObjectVisium(sample_name = "MouseBrainHemeInjection", directory_visium = main_dir, img_ref = "hires")
object <- loadImages(object)
inj_df <-
read_csv(file.path(main_dir, "heme_1000_injection_site.csv")) %>%
dplyr::rename(barcodes = Barcode) %>%
mutate(injection_site = if_else(is.na(injection_site), "normal", "heme") %>% as.factor())
anatomy_df<-
read_csv(file.path(main_dir, "heme_1000_anatomy.csv")) %>%
dplyr::rename(barcodes = Barcode) %>%
mutate(anatomy = as.factor(anatomy))
object <- addFeatures(object, inj_df)
object <- addFeatures(object, anatomy_df)
getFeatureNames(object)
meta_data <- list()
meta_data$comment <- "contains grouping variables 'injection_site' and 'anatomy' from the original publication"
meta_data$donor_species <- "Mus musculus"
meta_data$institution <- "University Hospital Zurich"
meta_data$organ <- "Brain"
meta_data$pathology <- "bleeding_model"
meta_data$platform <- object@platform
meta_data$pub_citation <- readRDS(file.path(main_dir, "citation.RDS"))
meta_data$workgroup <- "Primelab"
object <- addSampleMetaData(object, meta_data = meta_data)
dir <- file.path("spata2v3_objects", subfolder, str_c(object@sample, ".RDS"))
saveSpataObject(object, dir)
# Maynard et al 2021 ------------------------------------------------------
subfolder <- "Maynard_et_al_2021"
create_subfolder(subfolder)
main_dir <- "/Users/heilandr/lab/data/spatial_seq/raw/10XVisium/Maynard_et_al_2021"
all_folders <-
list.files(path = main_dir, full.names = T) %>%
str_subset(pattern = "[0-9]$")
open_overview_pdf(subfolder)
for(folder in all_folders){
sample_name <- confuns::str_extract_after(folder, pattern = "Maynard_et_al_2021\\/")
object <-
initiateSpataObjectVisium(
sample_name = sample_name,
directory_visium = folder,
img_ref = "hires",
img_active = "lowres"
)
meta_data <- list()
meta_data$donor_species <- "Homo sapiens"
meta_data$histo_class <- "Cortex"
meta_data$institution <- "Lieber Institute for Brain Development"
meta_data$organ <- "Cerebrum"
meta_data$organ_part <- "prefrontal"
meta_data$pub_citation <- readRDS(file.path(main_dir, "citation.RDS"))
meta_data$pub_year <- 2021
meta_data$pub_journal <- "Nature Neuroscience"
object <- addSampleMetaData(object, meta_data = meta_data)
dir <- file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS"))
object <- setSpataDir(object, dir)
saveSpataObject(object)
plot_overview(object)
}
dev.off()
# Vizgen MERFISH example data sets --------------------------------
subfolder <- "Vizgen_example_data_sets_Cancer"
create_subfolder(subfolder)
dir_main <- "/Users/heilandr/lab/data/spatial_seq/raw/MERFISH/MERSCOPE-FFPE-Human-Immuno-oncology"
all_folders <- list.files(dir_main, full.names = T)
organs <- c("Breast", "Colon", "Colon", "Liver", "Liver", "Lung", "Lung", "Skin", "Skin", "Ovary", "Ovary", "Ovary", "Ovary", "Prostate", "Prostate", "Uterus", "Uterus", "Uterus")
open_overview_pdf(subfolder)
for(i in seq_along(all_folders)){
folder <- all_folders[i]
sample_name <- dplyr::last(stringr::str_split(folder, "/")[[1]])
object <-
initiateSpataObjectMERFISH(
directory_merfish = folder,
sample_name = sample_name
)
meta_data <- list()
meta_data$donor_species <- "Homo sapiens"
meta_data$institution <- "Vizgen"
meta_data$organ <- organs[i]
meta_data$pathology <- "tumor"
meta_data$platform <- "MERFISH"
meta_data$pub_citation <- "Vizgen MERFISH FFPE Human Immuno-oncology Data Set, May 2022"
meta_data$download_date <- lubridate::as_datetime("2024/01/09")
object <- addSampleMetaData(object, meta_data = meta_data)
dir <- file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS"))
object <- setSpataDir(object, dir)
saveSpataObject(object)
plot_overview(object)
}
dev.off()
subfolder <- "Vizgen_example_data_sets_MouseBrain"
create_subfolder(subfolder)
dir_main <- "/Users/heilandr/lab/data/spatial_seq/raw/MERFISH/MERSCOPE-Mouse-Brain-Receptor-Map"
all_folders <- list.files(dir_main, full.names = T)
open_overview_pdf(subfolder)
for(i in seq_along(all_folders)){
folder <- all_folders[i]
sample_name <- dplyr::last(stringr::str_split(folder, "/")[[1]])
object <-
initiateSpataObjectMERFISH(
directory_merfish = folder,
sample_name = sample_name
)
meta_data <- list()
meta_data$donor_species <- "Mus musculus"
meta_data$institution <- "Vizgen"
meta_data$organ <- "Brain"
meta_data$pathology <- NA_character_
meta_data$platform <- "MERFISH"
meta_data$pub_citation <- "Vizgen MERFISH Mouse Brain Receptor Map"
meta_data$download_date <- lubridate::as_datetime("2024/08/19")
object <- addSampleMetaData(object, meta_data = meta_data)
dir <- file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS"))
object <- setSpataDir(object, dir)
saveSpataObject(object)
plot_overview(object)
}
dev.off()
subfolder <- "Vizgen_example_data_sets_MouseLiver"
create_subfolder(subfolder)
dir_main <- "/Users/heilandr/lab/data/spatial_seq/raw/MERFISH/MERSCOPE-Mouse-Liver-Map"
all_folders <- list.files(dir_main, full.names = T)
open_overview_pdf(subfolder)
for(i in seq_along(all_folders)){
folder <- all_folders[i]
sample_name <- dplyr::last(stringr::str_split(folder, "/")[[1]])
object <-
initiateSpataObjectMERFISH(
directory_merfish = folder,
sample_name = sample_name
)
meta_data <- list()
meta_data$donor_species <- "Mus musculus"
meta_data$institution <- "Vizgen"
meta_data$organ <- "Liver"
meta_data$pathology <- NA_character_
meta_data$platform <- "MERFISH"
meta_data$download_date <- "2024/08/21"
object <- addSampleMetaData(object, meta_data = meta_data)
dir <- file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS"))
object <- setSpataDir(object, dir)
saveSpataObject(object)
plot_overview(object)
}
dev.off()
subfolder <- "Vizgen_example_data_sets_HumanBrain_AD"
create_subfolder(subfolder)
dir_main <- "/Users/heilandr/lab/data/spatial_seq/raw/MERFISH/MERSCOPE-1000-Gene-Panel-Human-Brain-AD"
all_folders <- list.files(dir_main, full.names = T)
open_overview_pdf(subfolder)
for(i in seq_along(all_folders)){
folder <- all_folders[i]
sample_name <- dplyr::last(stringr::str_split(folder, "/")[[1]])
object <-
initiateSpataObjectMERFISH(
directory_merfish = folder,
sample_name = sample_name
)
meta_data <- list()
meta_data$donor_species <- "Homo sapiens"
meta_data$institution <- "Vizgen"
meta_data$organ <- "Brain"
meta_data$organ_part <- "Unspecified"
meta_data$pathology <- "AD"
meta_data$platform <- "MERFISH"
meta_data$download_date <- lubridate::as_datetime("2024/08/21")
object <- addSampleMetaData(object, meta_data = meta_data)
dir <- file.path("spata2v3_objects", subfolder, str_c(sample_name, ".RDS"))
object <- setSpataDir(object, dir)
saveSpataObject(object)
plot_overview(object)
}
dev.off()
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