scripts/application_setup.R
In keshav-motwani/IBMR: What the package does (short line)
prepare_real_data_application = function(split_index,
n_genes,
n_sample,
cache_path,
replicate) {
set.seed(replicate, kind = "Mersenne-Twister", normal.kind = "Inversion", sample.kind = "Rejection")
dataset_names = read.csv(file.path("data/", "table_1.csv"))$dataset
stopifnot(length(dataset_names) == 10)
splits = expand.grid(setdiff(dataset_names, "hao_2020"), setdiff(dataset_names, "hao_2020"), stringsAsFactors = FALSE)
colnames(splits) = c("validation", "test")
splits = splits[splits[, 1] != splits[, 2], ]
rownames(splits) = 1:nrow(splits)
stopifnot(nrow(splits) == 72)
split = splits[split_index, , drop = TRUE]
n_sample = rep(n_sample, length(dataset_names))
names(n_sample) = dataset_names
n_sample[unlist(split)] = NA
datasets = mapply(
dataset_names,
n_sample,
FUN = function(dataset, n) {
result = get(paste0("prepare_", dataset))(cache_path, n_genes, n)
gc()
return(result)
},
SIMPLIFY = FALSE
)
names(datasets) = dataset_names
train_datasets = datasets[setdiff(dataset_names, unlist(split))]
validation_datasets = datasets[split$validation]
test_datasets = datasets[split$test]
categories = train_datasets[[1]]$categories
Y_list = unlist(lapply(train_datasets, `[[`, "Y_list"), recursive = FALSE)
X_list = unlist(lapply(train_datasets, `[[`, "X_list"), recursive = FALSE)
category_mappings = unlist(lapply(train_datasets, `[[`, "category_mappings"), recursive = FALSE)
inverse_category_mappings = unlist(lapply(train_datasets, `[[`, "inverse_category_mappings"), recursive = FALSE)
category_mappings = list(categories = categories, category_mappings = category_mappings, inverse_category_mappings = inverse_category_mappings)
sample_list = unlist(lapply(train_datasets, `[[`, "sample_list"), recursive = FALSE)
Y_list_val = unlist(lapply(validation_datasets, `[[`, "Y_list"), recursive = FALSE)
X_list_val = unlist(lapply(validation_datasets, `[[`, "X_list"), recursive = FALSE)
category_mappings_val = unlist(lapply(validation_datasets, `[[`, "category_mappings"), recursive = FALSE)
inverse_category_mappings_val = unlist(lapply(validation_datasets, `[[`, "inverse_category_mappings"), recursive = FALSE)
category_mappings_val = list(categories = categories, category_mappings = category_mappings_val, inverse_category_mappings = inverse_category_mappings_val)
Y_list_test = unlist(lapply(test_datasets, `[[`, "Y_list"), recursive = FALSE)
X_list_test = unlist(lapply(test_datasets, `[[`, "X_list"), recursive = FALSE)
category_mappings_test = unlist(lapply(test_datasets, `[[`, "category_mappings"), recursive = FALSE)
inverse_category_mappings_test = unlist(lapply(test_datasets, `[[`, "inverse_category_mappings"), recursive = FALSE)
category_mappings_test = list(categories = categories, category_mappings = category_mappings_test, inverse_category_mappings = inverse_category_mappings_test)
output = prepare_data(Y_list = Y_list,
category_mappings = category_mappings,
category_mappings_fine = NULL,
X_list = X_list,
X_star_list = NULL,
sample_list = sample_list,
Y_list_validation = Y_list_val,
category_mappings_validation = category_mappings_val,
category_mappings_fine_validation = NULL,
X_list_validation = X_list_val,
X_star_list_validation = NULL,
Y_list_test = Y_list_test,
category_mappings_test = category_mappings_test,
X_list_test = X_list_test,
alpha = NULL,
Beta = NULL)
}
prepare_hao_2020 = function(cache_path, n_genes = NA, n_sample = NA, sce = FALSE) {
data = AnnotatedPBMC::get_hao_2020(cache_path)
SingleCellExperiment::altExp(data) = NULL
if (!sce) SingleCellExperiment::counts(data) = NULL
if (!is.na(n_genes)) {
genes = read.csv(file.path("data/", "genes.csv"))[, 2][1:n_genes]
data = data[genes, ]
}
data$cell_type = ifelse(data$cell_type_2 == "Treg", data$cell_type_3, data$cell_type_2)
removed_labels = "*Proliferating*"
data = data[, !grepl(removed_labels, data$cell_type)]
attr(data, "removed_labels") = removed_labels
data = data[, uniform_sample(data$cell_type, ifelse(is.na(n_sample), ncol(data), n_sample))]
binning_function = c(
ASDC = "ASDC",
`B intermediate` = "B intermediate",
`B memory` = "B memory",
`B naive` = "B naive",
`CD14 Mono` = "CD14 Mono",
`CD16 Mono` = "CD16 Mono",
`CD4 CTL` = "CD4 CTL",
`CD4 Naive` = "CD4 Naive",
`CD4 TCM` = "CD4 TCM",
`CD4 TEM` = "CD4 TEM",
`CD8 Naive` = "CD8 Naive",
`CD8 TCM` = "CD8 TCM",
`CD8 TEM` = "CD8 TEM",
cDC1 = "cDC1",
cDC2 = "cDC2",
dnT = "dnT",
Eryth = "Eryth",
gdT = "gdT",
HSPC = "HSPC",
ILC = "ILC",
MAIT = "MAIT",
NK = "NK",
NK_CD56bright = "NK_CD56bright",
pDC = "pDC",
Plasmablast = "Plasmablast",
Platelet = "Platelet",
`Treg Memory` = "Treg Memory",
`Treg Naive` = "Treg Naive"
)
return(prepare_dataset_output(data, binning_function, sce))
}
prepare_kotliarov_2020 = function(cache_path, n_genes = NA, n_sample = NA, sce = FALSE) {
data = AnnotatedPBMC::get_kotliarov_2020(cache_path)
if (!is.na(n_genes)) {
genes = read.csv(file.path("data/", "genes.csv"))[, 2][1:n_genes]
data = data[genes, ]
}
data$cell_type = data$cell_type_1
removed_labels = "Unconv T"
data = data[, data$cell_type != removed_labels]
attr(data, "removed_labels") = removed_labels
data = data[, uniform_sample(data$cell_type, ifelse(is.na(n_sample), ncol(data), n_sample))]
binning_function = c(
ASDC = "unobserved",
`B intermediate` = "B",
`B memory` = "B",
`B naive` = "B",
`CD14 Mono` = "Monocyte/mDC",
`CD16 Mono` = "Non-classical monocyte",
`CD4 CTL` = "unobserved",
`CD4 Naive` = "CD4+ naive T/DNT",
`CD4 TCM` = "CD4+ memory T",
`CD4 TEM` = "CD4+ memory T",
`CD8 Naive` = "CD8+ naive T",
`CD8 TCM` = "CD8+ memory T",
`CD8 TEM` = "CD8+ memory T",
cDC1 = "unobserved",
cDC2 = "unobserved",
dnT = "CD4+ naive T/DNT",
Eryth = "unobserved",
gdT = "unobserved",
HSPC = "unobserved",
ILC = "unobserved",
MAIT = "unobserved",
NK = "NK",
NK_CD56bright = "NK",
pDC = "pDC",
Plasmablast = "B",
Platelet = "unobserved",
`Treg Memory` = "CD4+ memory T",
`Treg Naive` = "CD4+ naive T/DNT"
)
return(prepare_dataset_output(data, binning_function, sce))
}
prepare_haniffa_2021 = function(cache_path, n_genes = NA, n_sample = NA, sce = FALSE) {
data = AnnotatedPBMC::get_haniffa_2021(cache_path)
if (!is.na(n_genes)) {
genes = read.csv(file.path("data/", "genes.csv"))[, 2][1:n_genes]
data = data[genes, ]
}
data$cell_type = data$cell_type_1
removed_labels = "*prolif*"
data = data[, !grepl(removed_labels, data$cell_type)]
attr(data, "removed_labels") = removed_labels
data = data[, ]
data = data[, uniform_sample(data$cell_type, ifelse(is.na(n_sample), ncol(data), n_sample))]
binning_function = c(
ASDC = "DCs",
`B intermediate` = "B_cell",
`B memory` = "B_cell",
`B naive` = "B_cell",
`CD14 Mono` = "CD14",
`CD16 Mono` = "CD16",
`CD4 CTL` = "CD4",
`CD4 Naive` = "CD4",
`CD4 TCM` = "CD4",
`CD4 TEM` = "CD4",
`CD8 Naive` = "CD8",
`CD8 TCM` = "CD8",
`CD8 TEM` = "CD8",
cDC1 = "DCs",
cDC2 = "DCs",
dnT = "unobserved",
Eryth = "RBC",
gdT = "gdT",
HSPC = "HSC",
ILC = "unobserved",
MAIT = "MAIT",
NK = "NK_16hi",
NK_CD56bright = "NK_56hi",
pDC = "pDC",
Plasmablast = "Plasmablast",
Platelet = "Platelets",
`Treg Memory` = "Treg",
`Treg Naive` = "Treg"
)
return(prepare_dataset_output(data, binning_function, sce))
}
prepare_tsang_2021 = function(cache_path, n_genes = NA, n_sample = NA, sce = FALSE) {
# data = AnnotatedPBMC::get_tsang_2021(cache_path)
data = AnnotatedPBMC::get_tsang_2021(cache_path)
if (!is.na(n_genes)) {
genes = read.csv(file.path("data/", "genes.csv"))[, 2][1:n_genes]
data = data[genes, ]
}
data = data[, !grepl("TCRVbeta13.1pos|TissueResMemT|double-positive T cell \\(DPT)|granulocyte|intermediate monocyte|NK_CD56loCD16lo", data$cell_type)]
removed_labels = c("TCRVbeta13.1pos", "TissueResMemT", "double-positive T cell (DPT)", "granulocyte", "intermediate monocyte", "NK_CD56loCD16lo")
data = data[, !(data$cell_type %in% removed_labels)]
attr(data, "removed_labels") = removed_labels
data = data[, uniform_sample(data$cell_type, ifelse(is.na(n_sample), ncol(data), n_sample))]
binning_function = c(
ASDC = "conventional dendritic cell",
`B intermediate` = "unobserved",
`B memory` = "memory B cell",
`B naive` = "naive B cell",
`CD14 Mono` = "classical monocyte",
`CD16 Mono` = "non-classical monocyte",
`CD4 CTL` = "unobserved",
`CD4 Naive` = "naive CD4+ T cell",
`CD4 TCM` = "CD4-positive, alpha-beta memory T cell",
`CD4 TEM` = "CD4-positive, alpha-beta memory T cell",
`CD8 Naive` = "naive CD8+ T cell",
`CD8 TCM` = "CD8-positive, alpha-beta memory T cell",
`CD8 TEM` = "CD8-positive, alpha-beta memory T cell",
cDC1 = "conventional dendritic cell",
cDC2 = "conventional dendritic cell",
dnT = "double negative T cell (DNT)",
Eryth = "unobserved",
gdT = "gamma-delta T cell",
HSPC = "unobserved",
ILC = "unobserved",
MAIT = "mucosal invariant T cell (MAIT)",
NK = "NK_CD16hi",
NK_CD56bright = "NK_CD56hiCD16lo",
pDC = "plasmacytoid dendritic cell",
Plasmablast = "plasmablast",
Platelet = "platelet",
`Treg Memory` = "regulatory T cell",
`Treg Naive` = "regulatory T cell"
)
return(prepare_dataset_output(data, binning_function, sce))
}
prepare_blish_2020 = function(cache_path, n_genes = NA, n_sample = NA, sce = FALSE) {
data = AnnotatedPBMC::get_blish_2020(cache_path)
if (!is.na(n_genes)) {
genes = read.csv(file.path("data/", "genes.csv"))[, 2][1:n_genes]
data = data[genes, ]
}
data$cell_type = data$cell_type_1
removed_labels = "Granulocyte"
data = data[, data$cell_type != removed_labels]
attr(data, "removed_labels") = removed_labels
data = data[, uniform_sample(data$cell_type, ifelse(is.na(n_sample), ncol(data), n_sample))]
binning_function = c(
ASDC = "DC",
`B intermediate` = "B",
`B memory` = "B",
`B naive` = "B",
`CD14 Mono` = "CD14 Monocyte",
`CD16 Mono` = "CD16 Monocyte",
`CD4 CTL` = "CD4 T",
`CD4 Naive` = "CD4 T",
`CD4 TCM` = "CD4 T",
`CD4 TEM` = "CD4 T",
`CD8 Naive` = "CD8 T",
`CD8 TCM` = "CD8 T",
`CD8 TEM` = "CD8 T",
cDC1 = "DC",
cDC2 = "DC",
dnT = "unobserved",
Eryth = "RBC",
gdT = "gd T",
HSPC = "unobserved",
ILC = "unobserved",
MAIT = "unobserved",
NK = "NK",
NK_CD56bright = "NK",
pDC = "pDC",
Plasmablast = "PB",
Platelet = "Platelet",
`Treg Memory` = "CD4 T",
`Treg Naive` = "CD4 T"
)
return(prepare_dataset_output(data, binning_function, sce))
}
prepare_10x_sorted = function(cache_path, n_genes = NA, n_sample = NA, sce = FALSE) {
data = AnnotatedPBMC::get_10x_sorted(cache_path)
if (!is.na(n_genes)) {
genes = read.csv(file.path("data/", "genes.csv"))[, 2][1:n_genes]
data = data[genes, ]
}
removed_labels = c()
attr(data, "removed_labels") = removed_labels
data = data[, uniform_sample(data$cell_type, ifelse(is.na(n_sample), ncol(data), n_sample))]
binning_function = c(
ASDC = "unobserved",
`B intermediate` = "CD19+ B cells",
`B memory` = "CD19+ B cells",
`B naive` = "CD19+ B cells",
`CD14 Mono` = "CD14+ Monocytes",
`CD16 Mono` = "unobserved",
`CD4 CTL` = "unobserved",
`CD4 Naive` = "CD4+/CD45RA+/CD25- Naive T cells",
`CD4 TCM` = "CD4+/CD45RO+ Memory T Cells",
`CD4 TEM` = "CD4+/CD45RO+ Memory T Cells",
`CD8 Naive` = "CD8+/CD45RA+ Naive Cytotoxic T Cells",
`CD8 TCM` = "unobserved",
`CD8 TEM` = "unobserved",
cDC1 = "unobserved",
cDC2 = "unobserved",
dnT = "unobserved",
Eryth = "unobserved",
gdT = "unobserved",
HSPC = "CD34+ Cells",
ILC = "unobserved",
MAIT = "unobserved",
NK = "CD56+ Natural Killer Cells",
NK_CD56bright = "CD56+ Natural Killer Cells",
pDC = "unobserved",
Plasmablast = "CD19+ B cells",
Platelet = "unobserved",
`Treg Memory` = "CD4+/CD25+ Regulatory T Cells",
`Treg Naive` = "CD4+/CD25+ Regulatory T Cells"
)
return(prepare_dataset_output(data, binning_function, sce))
}
prepare_10x_pbmc_10k = function(cache_path, n_genes = NA, n_sample = NA, sce = FALSE) {
data = AnnotatedPBMC::get_10x_pbmc_10k(cache_path)
if (!is.na(n_genes)) {
genes = read.csv(file.path("data/", "genes.csv"))[, 2][1:n_genes]
data = data[genes, ]
}
data$cell_type = data$cell_type_2
removed_labels = "intermediate monocyte"
data = data[, data$cell_type != removed_labels]
attr(data, "removed_labels") = removed_labels
data = data[, uniform_sample(data$cell_type, ifelse(is.na(n_sample), ncol(data), n_sample))]
binning_function = c(
ASDC = "unobserved",
`B intermediate` = "B",
`B memory` = "B",
`B naive` = "B",
`CD14 Mono` = "classical monocyte",
`CD16 Mono` = "unobserved",
`CD4 CTL` = "unobserved",
`CD4 Naive` = "naive CD4",
`CD4 TCM` = "memory CD4",
`CD4 TEM` = "memory CD4",
`CD8 Naive` = "naive CD8",
`CD8 TCM` = "memory CD8",
`CD8 TEM` = "memory CD8",
cDC1 = "unobserved",
cDC2 = "unobserved",
dnT = "unobserved",
Eryth = "unobserved",
gdT = "unobserved",
HSPC = "unobserved",
ILC = "unobserved",
MAIT = "unobserved",
NK = "CD16+ NK",
NK_CD56bright = "CD16- NK",
pDC = "unobserved",
Plasmablast = "B",
Platelet = "unobserved",
`Treg Memory` = "Treg",
`Treg Naive` = "Treg"
)
return(prepare_dataset_output(data, binning_function, sce))
}
prepare_10x_pbmc_5k_v3 = function(cache_path, n_genes = NA, n_sample = NA, sce = FALSE) {
data = AnnotatedPBMC::get_10x_pbmc_5k_v3(cache_path)
if (!is.na(n_genes)) {
genes = read.csv(file.path("data/", "genes.csv"))[, 2][1:n_genes]
data = data[genes, ]
}
data$cell_type = data$cell_type_2
removed_labels = "intermediate monocyte"
data = data[, data$cell_type != removed_labels]
attr(data, "removed_labels") = removed_labels
data = data[, uniform_sample(data$cell_type, ifelse(is.na(n_sample), ncol(data), n_sample))]
binning_function = c(
ASDC = "DCs",
`B intermediate` = "unobserved",
`B memory` = "memory B",
`B naive` = "naive B",
`CD14 Mono` = "classical monocyte",
`CD16 Mono` = "non-classical CD16+ monocyte",
`CD4 CTL` = "unobserved",
`CD4 Naive` = "naive CD4",
`CD4 TCM` = "memory CD4",
`CD4 TEM` = "memory CD4",
`CD8 Naive` = "naive CD8",
`CD8 TCM` = "memory CD8",
`CD8 TEM` = "memory CD8",
cDC1 = "DCs",
cDC2 = "DCs",
dnT = "unobserved",
Eryth = "unobserved",
gdT = "unobserved",
HSPC = "unobserved",
ILC = "unobserved",
MAIT = "unobserved",
NK = "CD16+ NK",
NK_CD56bright = "CD16- NK",
pDC = "DCs",
Plasmablast = "unobserved",
Platelet = "unobserved",
`Treg Memory` = "Treg",
`Treg Naive` = "Treg"
)
return(prepare_dataset_output(data, binning_function, sce))
}
prepare_su_2020 = function(cache_path, n_genes = NA, n_sample = NA, sce = FALSE) {
data = AnnotatedPBMC::get_su_2020(cache_path)
if (!is.na(n_genes)) {
genes = read.csv(file.path("data/", "genes.csv"))[, 2][1:n_genes]
data = data[genes, ]
}
data$cell_type = data$cell_type_2
removed_labels = "intermediate monocyte"
data = data[, data$cell_type != removed_labels]
attr(data, "removed_labels") = removed_labels
data = data[, uniform_sample(data$cell_type, ifelse(is.na(n_sample), ncol(data), n_sample))]
binning_function = c(
ASDC = "myeloid DC",
`B intermediate` = "unobserved",
`B memory` = "memory B",
`B naive` = "naive B",
`CD14 Mono` = "classical monocyte",
`CD16 Mono` = "non-classical CD16+ monocyte",
`CD4 CTL` = "unobserved",
`CD4 Naive` = "naive CD4",
`CD4 TCM` = "memory CD4",
`CD4 TEM` = "memory CD4",
`CD8 Naive` = "naive CD8",
`CD8 TCM` = "memory CD8",
`CD8 TEM` = "memory CD8",
cDC1 = "myeloid DC",
cDC2 = "myeloid DC",
dnT = "unobserved",
Eryth = "unobserved",
gdT = "unobserved",
HSPC = "unobserved",
ILC = "unobserved",
MAIT = "unobserved",
NK = "CD16+ NK",
NK_CD56bright = "CD16- NK",
pDC = "plasmacytoid DC",
Plasmablast = "unobserved",
Platelet = "unobserved",
`Treg Memory` = "Treg",
`Treg Naive` = "Treg"
)
return(prepare_dataset_output(data, binning_function, sce))
}
prepare_ding_2019 = function(cache_path, n_genes = NA, n_sample = NA, sce = FALSE) {
data = AnnotatedPBMC::get_ding_2019(cache_path)
if (!is.na(n_genes)) {
genes = read.csv(file.path("data/", "genes.csv"))[, 2][1:n_genes]
data = data[genes, ]
}
removed_labels = "Megakaryocyte"
data = data[, grepl("10x", data$method) & data$cell_type != removed_labels]
attr(data, "removed_labels") = removed_labels
data = data[, uniform_sample(data$cell_type, ifelse(is.na(n_sample), ncol(data), n_sample))]
binning_function = c(
ASDC = "Dendritic cell",
`B intermediate` = "B cell",
`B memory` = "B cell",
`B naive` = "B cell",
`CD14 Mono` = "CD14+ monocyte",
`CD16 Mono` = "CD16+ monocyte",
`CD4 CTL` = "CD4+ T cell",
`CD4 Naive` = "CD4+ T cell",
`CD4 TCM` = "CD4+ T cell",
`CD4 TEM` = "CD4+ T cell",
`CD8 Naive` = "Cytotoxic T cell",
`CD8 TCM` = "Cytotoxic T cell",
`CD8 TEM` = "Cytotoxic T cell",
cDC1 = "Dendritic cell",
cDC2 = "Dendritic cell",
dnT = "unobserved",
Eryth = "unobserved",
gdT = "unobserved",
HSPC = "unobserved",
ILC = "unobserved",
MAIT = "unobserved",
NK = "Natural killer cell",
NK_CD56bright = "Natural killer cell",
pDC = "Plasmacytoid dendritic cell",
Plasmablast = "B cell",
Platelet = "unobserved",
`Treg Memory` = "CD4+ T cell",
`Treg Naive` = "CD4+ T cell"
)
return(prepare_dataset_output(data, binning_function, sce))
}
prepare_dataset_output = function(data, binning_function, sce) {
stopifnot(all(sort(names(binning_function)) == c("ASDC", "B intermediate", "B memory", "B naive", "CD14 Mono",
"CD16 Mono", "CD4 CTL", "CD4 Naive", "CD4 TCM", "CD4 TEM", "CD8 Naive",
"CD8 TCM", "CD8 TEM", "cDC1", "cDC2", "dnT", "Eryth", "gdT",
"HSPC", "ILC", "MAIT", "NK", "NK_CD56bright", "pDC", "Plasmablast",
"Platelet", "Treg Memory", "Treg Naive")))
stopifnot(length(setdiff(data$cell_type, binning_function)) == 0)
if(!(length(setdiff(binning_function, data$cell_type)) == 0 || all(setdiff(binning_function, data$cell_type) == "unobserved"))) warning("Some labels from binning function not observed")
if (sce) return(list(sce = data, binning_function = binning_function))
# binning_function[binning_function == "unobserved"] = names(binning_function)[binning_function == "unobserved"]
data = list(data) # split_data(data, data$dataset)
X_list = lapply(data, function(x) t(as.matrix(SingleCellExperiment::logcounts(x))))
Y_list = lapply(data, function(x) as.character(x$cell_type))
sample_list = lapply(data, function(x) as.character(x$dataset))
category_mapping = binning_function_to_category_mapping(binning_function)
return(list(Y_list = Y_list, X_list = X_list, categories = names(binning_function), category_mappings = replicate(length(Y_list), category_mapping, simplify = FALSE), inverse_category_mappings = replicate(length(Y_list), binning_function, simplify = FALSE), sample_list = sample_list))
}
uniform_sample = function(Y, n) {
indices = sample(1:length(Y), min(n, length(Y)))
return(indices)
}
split_data = function(sce, split_by) {
lapply(sort(unique(split_by)), function(x) sce[, split_by == x])
}
binning_function_to_category_mapping = function(binning_function) {
category_mapping = list()
for (label in unique(binning_function)) {
category_mapping[[label]] = names(binning_function)[which(binning_function == label)]
}
return(category_mapping)
}
keshav-motwani/IBMR documentation built on April 15, 2023, 9:47 a.m.
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prepare_real_data_application = function(split_index,
n_genes,
n_sample,
cache_path,
replicate) {
set.seed(replicate, kind = "Mersenne-Twister", normal.kind = "Inversion", sample.kind = "Rejection")
dataset_names = read.csv(file.path("data/", "table_1.csv"))$dataset
stopifnot(length(dataset_names) == 10)
splits = expand.grid(setdiff(dataset_names, "hao_2020"), setdiff(dataset_names, "hao_2020"), stringsAsFactors = FALSE)
colnames(splits) = c("validation", "test")
splits = splits[splits[, 1] != splits[, 2], ]
rownames(splits) = 1:nrow(splits)
stopifnot(nrow(splits) == 72)
split = splits[split_index, , drop = TRUE]
n_sample = rep(n_sample, length(dataset_names))
names(n_sample) = dataset_names
n_sample[unlist(split)] = NA
datasets = mapply(
dataset_names,
n_sample,
FUN = function(dataset, n) {
result = get(paste0("prepare_", dataset))(cache_path, n_genes, n)
gc()
return(result)
},
SIMPLIFY = FALSE
)
names(datasets) = dataset_names
train_datasets = datasets[setdiff(dataset_names, unlist(split))]
validation_datasets = datasets[split$validation]
test_datasets = datasets[split$test]
categories = train_datasets[[1]]$categories
Y_list = unlist(lapply(train_datasets, `[[`, "Y_list"), recursive = FALSE)
X_list = unlist(lapply(train_datasets, `[[`, "X_list"), recursive = FALSE)
category_mappings = unlist(lapply(train_datasets, `[[`, "category_mappings"), recursive = FALSE)
inverse_category_mappings = unlist(lapply(train_datasets, `[[`, "inverse_category_mappings"), recursive = FALSE)
category_mappings = list(categories = categories, category_mappings = category_mappings, inverse_category_mappings = inverse_category_mappings)
sample_list = unlist(lapply(train_datasets, `[[`, "sample_list"), recursive = FALSE)
Y_list_val = unlist(lapply(validation_datasets, `[[`, "Y_list"), recursive = FALSE)
X_list_val = unlist(lapply(validation_datasets, `[[`, "X_list"), recursive = FALSE)
category_mappings_val = unlist(lapply(validation_datasets, `[[`, "category_mappings"), recursive = FALSE)
inverse_category_mappings_val = unlist(lapply(validation_datasets, `[[`, "inverse_category_mappings"), recursive = FALSE)
category_mappings_val = list(categories = categories, category_mappings = category_mappings_val, inverse_category_mappings = inverse_category_mappings_val)
Y_list_test = unlist(lapply(test_datasets, `[[`, "Y_list"), recursive = FALSE)
X_list_test = unlist(lapply(test_datasets, `[[`, "X_list"), recursive = FALSE)
category_mappings_test = unlist(lapply(test_datasets, `[[`, "category_mappings"), recursive = FALSE)
inverse_category_mappings_test = unlist(lapply(test_datasets, `[[`, "inverse_category_mappings"), recursive = FALSE)
category_mappings_test = list(categories = categories, category_mappings = category_mappings_test, inverse_category_mappings = inverse_category_mappings_test)
output = prepare_data(Y_list = Y_list,
category_mappings = category_mappings,
category_mappings_fine = NULL,
X_list = X_list,
X_star_list = NULL,
sample_list = sample_list,
Y_list_validation = Y_list_val,
category_mappings_validation = category_mappings_val,
category_mappings_fine_validation = NULL,
X_list_validation = X_list_val,
X_star_list_validation = NULL,
Y_list_test = Y_list_test,
category_mappings_test = category_mappings_test,
X_list_test = X_list_test,
alpha = NULL,
Beta = NULL)
}
prepare_hao_2020 = function(cache_path, n_genes = NA, n_sample = NA, sce = FALSE) {
data = AnnotatedPBMC::get_hao_2020(cache_path)
SingleCellExperiment::altExp(data) = NULL
if (!sce) SingleCellExperiment::counts(data) = NULL
if (!is.na(n_genes)) {
genes = read.csv(file.path("data/", "genes.csv"))[, 2][1:n_genes]
data = data[genes, ]
}
data$cell_type = ifelse(data$cell_type_2 == "Treg", data$cell_type_3, data$cell_type_2)
removed_labels = "*Proliferating*"
data = data[, !grepl(removed_labels, data$cell_type)]
attr(data, "removed_labels") = removed_labels
data = data[, uniform_sample(data$cell_type, ifelse(is.na(n_sample), ncol(data), n_sample))]
binning_function = c(
ASDC = "ASDC",
`B intermediate` = "B intermediate",
`B memory` = "B memory",
`B naive` = "B naive",
`CD14 Mono` = "CD14 Mono",
`CD16 Mono` = "CD16 Mono",
`CD4 CTL` = "CD4 CTL",
`CD4 Naive` = "CD4 Naive",
`CD4 TCM` = "CD4 TCM",
`CD4 TEM` = "CD4 TEM",
`CD8 Naive` = "CD8 Naive",
`CD8 TCM` = "CD8 TCM",
`CD8 TEM` = "CD8 TEM",
cDC1 = "cDC1",
cDC2 = "cDC2",
dnT = "dnT",
Eryth = "Eryth",
gdT = "gdT",
HSPC = "HSPC",
ILC = "ILC",
MAIT = "MAIT",
NK = "NK",
NK_CD56bright = "NK_CD56bright",
pDC = "pDC",
Plasmablast = "Plasmablast",
Platelet = "Platelet",
`Treg Memory` = "Treg Memory",
`Treg Naive` = "Treg Naive"
)
return(prepare_dataset_output(data, binning_function, sce))
}
prepare_kotliarov_2020 = function(cache_path, n_genes = NA, n_sample = NA, sce = FALSE) {
data = AnnotatedPBMC::get_kotliarov_2020(cache_path)
if (!is.na(n_genes)) {
genes = read.csv(file.path("data/", "genes.csv"))[, 2][1:n_genes]
data = data[genes, ]
}
data$cell_type = data$cell_type_1
removed_labels = "Unconv T"
data = data[, data$cell_type != removed_labels]
attr(data, "removed_labels") = removed_labels
data = data[, uniform_sample(data$cell_type, ifelse(is.na(n_sample), ncol(data), n_sample))]
binning_function = c(
ASDC = "unobserved",
`B intermediate` = "B",
`B memory` = "B",
`B naive` = "B",
`CD14 Mono` = "Monocyte/mDC",
`CD16 Mono` = "Non-classical monocyte",
`CD4 CTL` = "unobserved",
`CD4 Naive` = "CD4+ naive T/DNT",
`CD4 TCM` = "CD4+ memory T",
`CD4 TEM` = "CD4+ memory T",
`CD8 Naive` = "CD8+ naive T",
`CD8 TCM` = "CD8+ memory T",
`CD8 TEM` = "CD8+ memory T",
cDC1 = "unobserved",
cDC2 = "unobserved",
dnT = "CD4+ naive T/DNT",
Eryth = "unobserved",
gdT = "unobserved",
HSPC = "unobserved",
ILC = "unobserved",
MAIT = "unobserved",
NK = "NK",
NK_CD56bright = "NK",
pDC = "pDC",
Plasmablast = "B",
Platelet = "unobserved",
`Treg Memory` = "CD4+ memory T",
`Treg Naive` = "CD4+ naive T/DNT"
)
return(prepare_dataset_output(data, binning_function, sce))
}
prepare_haniffa_2021 = function(cache_path, n_genes = NA, n_sample = NA, sce = FALSE) {
data = AnnotatedPBMC::get_haniffa_2021(cache_path)
if (!is.na(n_genes)) {
genes = read.csv(file.path("data/", "genes.csv"))[, 2][1:n_genes]
data = data[genes, ]
}
data$cell_type = data$cell_type_1
removed_labels = "*prolif*"
data = data[, !grepl(removed_labels, data$cell_type)]
attr(data, "removed_labels") = removed_labels
data = data[, ]
data = data[, uniform_sample(data$cell_type, ifelse(is.na(n_sample), ncol(data), n_sample))]
binning_function = c(
ASDC = "DCs",
`B intermediate` = "B_cell",
`B memory` = "B_cell",
`B naive` = "B_cell",
`CD14 Mono` = "CD14",
`CD16 Mono` = "CD16",
`CD4 CTL` = "CD4",
`CD4 Naive` = "CD4",
`CD4 TCM` = "CD4",
`CD4 TEM` = "CD4",
`CD8 Naive` = "CD8",
`CD8 TCM` = "CD8",
`CD8 TEM` = "CD8",
cDC1 = "DCs",
cDC2 = "DCs",
dnT = "unobserved",
Eryth = "RBC",
gdT = "gdT",
HSPC = "HSC",
ILC = "unobserved",
MAIT = "MAIT",
NK = "NK_16hi",
NK_CD56bright = "NK_56hi",
pDC = "pDC",
Plasmablast = "Plasmablast",
Platelet = "Platelets",
`Treg Memory` = "Treg",
`Treg Naive` = "Treg"
)
return(prepare_dataset_output(data, binning_function, sce))
}
prepare_tsang_2021 = function(cache_path, n_genes = NA, n_sample = NA, sce = FALSE) {
# data = AnnotatedPBMC::get_tsang_2021(cache_path)
data = AnnotatedPBMC::get_tsang_2021(cache_path)
if (!is.na(n_genes)) {
genes = read.csv(file.path("data/", "genes.csv"))[, 2][1:n_genes]
data = data[genes, ]
}
data = data[, !grepl("TCRVbeta13.1pos|TissueResMemT|double-positive T cell \\(DPT)|granulocyte|intermediate monocyte|NK_CD56loCD16lo", data$cell_type)]
removed_labels = c("TCRVbeta13.1pos", "TissueResMemT", "double-positive T cell (DPT)", "granulocyte", "intermediate monocyte", "NK_CD56loCD16lo")
data = data[, !(data$cell_type %in% removed_labels)]
attr(data, "removed_labels") = removed_labels
data = data[, uniform_sample(data$cell_type, ifelse(is.na(n_sample), ncol(data), n_sample))]
binning_function = c(
ASDC = "conventional dendritic cell",
`B intermediate` = "unobserved",
`B memory` = "memory B cell",
`B naive` = "naive B cell",
`CD14 Mono` = "classical monocyte",
`CD16 Mono` = "non-classical monocyte",
`CD4 CTL` = "unobserved",
`CD4 Naive` = "naive CD4+ T cell",
`CD4 TCM` = "CD4-positive, alpha-beta memory T cell",
`CD4 TEM` = "CD4-positive, alpha-beta memory T cell",
`CD8 Naive` = "naive CD8+ T cell",
`CD8 TCM` = "CD8-positive, alpha-beta memory T cell",
`CD8 TEM` = "CD8-positive, alpha-beta memory T cell",
cDC1 = "conventional dendritic cell",
cDC2 = "conventional dendritic cell",
dnT = "double negative T cell (DNT)",
Eryth = "unobserved",
gdT = "gamma-delta T cell",
HSPC = "unobserved",
ILC = "unobserved",
MAIT = "mucosal invariant T cell (MAIT)",
NK = "NK_CD16hi",
NK_CD56bright = "NK_CD56hiCD16lo",
pDC = "plasmacytoid dendritic cell",
Plasmablast = "plasmablast",
Platelet = "platelet",
`Treg Memory` = "regulatory T cell",
`Treg Naive` = "regulatory T cell"
)
return(prepare_dataset_output(data, binning_function, sce))
}
prepare_blish_2020 = function(cache_path, n_genes = NA, n_sample = NA, sce = FALSE) {
data = AnnotatedPBMC::get_blish_2020(cache_path)
if (!is.na(n_genes)) {
genes = read.csv(file.path("data/", "genes.csv"))[, 2][1:n_genes]
data = data[genes, ]
}
data$cell_type = data$cell_type_1
removed_labels = "Granulocyte"
data = data[, data$cell_type != removed_labels]
attr(data, "removed_labels") = removed_labels
data = data[, uniform_sample(data$cell_type, ifelse(is.na(n_sample), ncol(data), n_sample))]
binning_function = c(
ASDC = "DC",
`B intermediate` = "B",
`B memory` = "B",
`B naive` = "B",
`CD14 Mono` = "CD14 Monocyte",
`CD16 Mono` = "CD16 Monocyte",
`CD4 CTL` = "CD4 T",
`CD4 Naive` = "CD4 T",
`CD4 TCM` = "CD4 T",
`CD4 TEM` = "CD4 T",
`CD8 Naive` = "CD8 T",
`CD8 TCM` = "CD8 T",
`CD8 TEM` = "CD8 T",
cDC1 = "DC",
cDC2 = "DC",
dnT = "unobserved",
Eryth = "RBC",
gdT = "gd T",
HSPC = "unobserved",
ILC = "unobserved",
MAIT = "unobserved",
NK = "NK",
NK_CD56bright = "NK",
pDC = "pDC",
Plasmablast = "PB",
Platelet = "Platelet",
`Treg Memory` = "CD4 T",
`Treg Naive` = "CD4 T"
)
return(prepare_dataset_output(data, binning_function, sce))
}
prepare_10x_sorted = function(cache_path, n_genes = NA, n_sample = NA, sce = FALSE) {
data = AnnotatedPBMC::get_10x_sorted(cache_path)
if (!is.na(n_genes)) {
genes = read.csv(file.path("data/", "genes.csv"))[, 2][1:n_genes]
data = data[genes, ]
}
removed_labels = c()
attr(data, "removed_labels") = removed_labels
data = data[, uniform_sample(data$cell_type, ifelse(is.na(n_sample), ncol(data), n_sample))]
binning_function = c(
ASDC = "unobserved",
`B intermediate` = "CD19+ B cells",
`B memory` = "CD19+ B cells",
`B naive` = "CD19+ B cells",
`CD14 Mono` = "CD14+ Monocytes",
`CD16 Mono` = "unobserved",
`CD4 CTL` = "unobserved",
`CD4 Naive` = "CD4+/CD45RA+/CD25- Naive T cells",
`CD4 TCM` = "CD4+/CD45RO+ Memory T Cells",
`CD4 TEM` = "CD4+/CD45RO+ Memory T Cells",
`CD8 Naive` = "CD8+/CD45RA+ Naive Cytotoxic T Cells",
`CD8 TCM` = "unobserved",
`CD8 TEM` = "unobserved",
cDC1 = "unobserved",
cDC2 = "unobserved",
dnT = "unobserved",
Eryth = "unobserved",
gdT = "unobserved",
HSPC = "CD34+ Cells",
ILC = "unobserved",
MAIT = "unobserved",
NK = "CD56+ Natural Killer Cells",
NK_CD56bright = "CD56+ Natural Killer Cells",
pDC = "unobserved",
Plasmablast = "CD19+ B cells",
Platelet = "unobserved",
`Treg Memory` = "CD4+/CD25+ Regulatory T Cells",
`Treg Naive` = "CD4+/CD25+ Regulatory T Cells"
)
return(prepare_dataset_output(data, binning_function, sce))
}
prepare_10x_pbmc_10k = function(cache_path, n_genes = NA, n_sample = NA, sce = FALSE) {
data = AnnotatedPBMC::get_10x_pbmc_10k(cache_path)
if (!is.na(n_genes)) {
genes = read.csv(file.path("data/", "genes.csv"))[, 2][1:n_genes]
data = data[genes, ]
}
data$cell_type = data$cell_type_2
removed_labels = "intermediate monocyte"
data = data[, data$cell_type != removed_labels]
attr(data, "removed_labels") = removed_labels
data = data[, uniform_sample(data$cell_type, ifelse(is.na(n_sample), ncol(data), n_sample))]
binning_function = c(
ASDC = "unobserved",
`B intermediate` = "B",
`B memory` = "B",
`B naive` = "B",
`CD14 Mono` = "classical monocyte",
`CD16 Mono` = "unobserved",
`CD4 CTL` = "unobserved",
`CD4 Naive` = "naive CD4",
`CD4 TCM` = "memory CD4",
`CD4 TEM` = "memory CD4",
`CD8 Naive` = "naive CD8",
`CD8 TCM` = "memory CD8",
`CD8 TEM` = "memory CD8",
cDC1 = "unobserved",
cDC2 = "unobserved",
dnT = "unobserved",
Eryth = "unobserved",
gdT = "unobserved",
HSPC = "unobserved",
ILC = "unobserved",
MAIT = "unobserved",
NK = "CD16+ NK",
NK_CD56bright = "CD16- NK",
pDC = "unobserved",
Plasmablast = "B",
Platelet = "unobserved",
`Treg Memory` = "Treg",
`Treg Naive` = "Treg"
)
return(prepare_dataset_output(data, binning_function, sce))
}
prepare_10x_pbmc_5k_v3 = function(cache_path, n_genes = NA, n_sample = NA, sce = FALSE) {
data = AnnotatedPBMC::get_10x_pbmc_5k_v3(cache_path)
if (!is.na(n_genes)) {
genes = read.csv(file.path("data/", "genes.csv"))[, 2][1:n_genes]
data = data[genes, ]
}
data$cell_type = data$cell_type_2
removed_labels = "intermediate monocyte"
data = data[, data$cell_type != removed_labels]
attr(data, "removed_labels") = removed_labels
data = data[, uniform_sample(data$cell_type, ifelse(is.na(n_sample), ncol(data), n_sample))]
binning_function = c(
ASDC = "DCs",
`B intermediate` = "unobserved",
`B memory` = "memory B",
`B naive` = "naive B",
`CD14 Mono` = "classical monocyte",
`CD16 Mono` = "non-classical CD16+ monocyte",
`CD4 CTL` = "unobserved",
`CD4 Naive` = "naive CD4",
`CD4 TCM` = "memory CD4",
`CD4 TEM` = "memory CD4",
`CD8 Naive` = "naive CD8",
`CD8 TCM` = "memory CD8",
`CD8 TEM` = "memory CD8",
cDC1 = "DCs",
cDC2 = "DCs",
dnT = "unobserved",
Eryth = "unobserved",
gdT = "unobserved",
HSPC = "unobserved",
ILC = "unobserved",
MAIT = "unobserved",
NK = "CD16+ NK",
NK_CD56bright = "CD16- NK",
pDC = "DCs",
Plasmablast = "unobserved",
Platelet = "unobserved",
`Treg Memory` = "Treg",
`Treg Naive` = "Treg"
)
return(prepare_dataset_output(data, binning_function, sce))
}
prepare_su_2020 = function(cache_path, n_genes = NA, n_sample = NA, sce = FALSE) {
data = AnnotatedPBMC::get_su_2020(cache_path)
if (!is.na(n_genes)) {
genes = read.csv(file.path("data/", "genes.csv"))[, 2][1:n_genes]
data = data[genes, ]
}
data$cell_type = data$cell_type_2
removed_labels = "intermediate monocyte"
data = data[, data$cell_type != removed_labels]
attr(data, "removed_labels") = removed_labels
data = data[, uniform_sample(data$cell_type, ifelse(is.na(n_sample), ncol(data), n_sample))]
binning_function = c(
ASDC = "myeloid DC",
`B intermediate` = "unobserved",
`B memory` = "memory B",
`B naive` = "naive B",
`CD14 Mono` = "classical monocyte",
`CD16 Mono` = "non-classical CD16+ monocyte",
`CD4 CTL` = "unobserved",
`CD4 Naive` = "naive CD4",
`CD4 TCM` = "memory CD4",
`CD4 TEM` = "memory CD4",
`CD8 Naive` = "naive CD8",
`CD8 TCM` = "memory CD8",
`CD8 TEM` = "memory CD8",
cDC1 = "myeloid DC",
cDC2 = "myeloid DC",
dnT = "unobserved",
Eryth = "unobserved",
gdT = "unobserved",
HSPC = "unobserved",
ILC = "unobserved",
MAIT = "unobserved",
NK = "CD16+ NK",
NK_CD56bright = "CD16- NK",
pDC = "plasmacytoid DC",
Plasmablast = "unobserved",
Platelet = "unobserved",
`Treg Memory` = "Treg",
`Treg Naive` = "Treg"
)
return(prepare_dataset_output(data, binning_function, sce))
}
prepare_ding_2019 = function(cache_path, n_genes = NA, n_sample = NA, sce = FALSE) {
data = AnnotatedPBMC::get_ding_2019(cache_path)
if (!is.na(n_genes)) {
genes = read.csv(file.path("data/", "genes.csv"))[, 2][1:n_genes]
data = data[genes, ]
}
removed_labels = "Megakaryocyte"
data = data[, grepl("10x", data$method) & data$cell_type != removed_labels]
attr(data, "removed_labels") = removed_labels
data = data[, uniform_sample(data$cell_type, ifelse(is.na(n_sample), ncol(data), n_sample))]
binning_function = c(
ASDC = "Dendritic cell",
`B intermediate` = "B cell",
`B memory` = "B cell",
`B naive` = "B cell",
`CD14 Mono` = "CD14+ monocyte",
`CD16 Mono` = "CD16+ monocyte",
`CD4 CTL` = "CD4+ T cell",
`CD4 Naive` = "CD4+ T cell",
`CD4 TCM` = "CD4+ T cell",
`CD4 TEM` = "CD4+ T cell",
`CD8 Naive` = "Cytotoxic T cell",
`CD8 TCM` = "Cytotoxic T cell",
`CD8 TEM` = "Cytotoxic T cell",
cDC1 = "Dendritic cell",
cDC2 = "Dendritic cell",
dnT = "unobserved",
Eryth = "unobserved",
gdT = "unobserved",
HSPC = "unobserved",
ILC = "unobserved",
MAIT = "unobserved",
NK = "Natural killer cell",
NK_CD56bright = "Natural killer cell",
pDC = "Plasmacytoid dendritic cell",
Plasmablast = "B cell",
Platelet = "unobserved",
`Treg Memory` = "CD4+ T cell",
`Treg Naive` = "CD4+ T cell"
)
return(prepare_dataset_output(data, binning_function, sce))
}
prepare_dataset_output = function(data, binning_function, sce) {
stopifnot(all(sort(names(binning_function)) == c("ASDC", "B intermediate", "B memory", "B naive", "CD14 Mono",
"CD16 Mono", "CD4 CTL", "CD4 Naive", "CD4 TCM", "CD4 TEM", "CD8 Naive",
"CD8 TCM", "CD8 TEM", "cDC1", "cDC2", "dnT", "Eryth", "gdT",
"HSPC", "ILC", "MAIT", "NK", "NK_CD56bright", "pDC", "Plasmablast",
"Platelet", "Treg Memory", "Treg Naive")))
stopifnot(length(setdiff(data$cell_type, binning_function)) == 0)
if(!(length(setdiff(binning_function, data$cell_type)) == 0 || all(setdiff(binning_function, data$cell_type) == "unobserved"))) warning("Some labels from binning function not observed")
if (sce) return(list(sce = data, binning_function = binning_function))
# binning_function[binning_function == "unobserved"] = names(binning_function)[binning_function == "unobserved"]
data = list(data) # split_data(data, data$dataset)
X_list = lapply(data, function(x) t(as.matrix(SingleCellExperiment::logcounts(x))))
Y_list = lapply(data, function(x) as.character(x$cell_type))
sample_list = lapply(data, function(x) as.character(x$dataset))
category_mapping = binning_function_to_category_mapping(binning_function)
return(list(Y_list = Y_list, X_list = X_list, categories = names(binning_function), category_mappings = replicate(length(Y_list), category_mapping, simplify = FALSE), inverse_category_mappings = replicate(length(Y_list), binning_function, simplify = FALSE), sample_list = sample_list))
}
uniform_sample = function(Y, n) {
indices = sample(1:length(Y), min(n, length(Y)))
return(indices)
}
split_data = function(sce, split_by) {
lapply(sort(unique(split_by)), function(x) sce[, split_by == x])
}
binning_function_to_category_mapping = function(binning_function) {
category_mapping = list()
for (label in unique(binning_function)) {
category_mapping[[label]] = names(binning_function)[which(binning_function == label)]
}
return(category_mapping)
}
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