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SCP-analysis.R
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#' Gene ID conversion function using biomart
#'
#' This function can convert different gene ID types within one species or bewteen two species using biomart service.
#'
#' @param geneID A vector of the geneID character.
#' @param geneID_from_IDtype ID type of the geneID. e.g. "symbol", "ensembl_id", "entrez_id"
#' @param geneID_to_IDtype ID type to convert to. e.g. "symbol", "ensembl_id", "entrez_id"
#' @param species_from Latin names for animals of the input geneID.
#' @param species_to Latin names for animals of the output geneID. e.g. "Homo_sapiens","Mus_musculus"
#' @param Ensembl_version Ensembl database version. If NULL, use the current release version.
#' @param attempts Number of attempts to connect with the biomart service.
#' @param mirror Specify an Ensembl mirror to connect to. The valid options here are 'www', 'uswest', 'useast', 'asia'.
#'
#' @return A list.
#'
#' @examples
#' res <- GeneConvert(
#' geneID = c("CDK1", "MKI67", "TOP2A", "AURKA", "CTCF"),
#' geneID_from_IDtype = "symbol",
#' geneID_to_IDtype = "entrez_id",
#' species_from = "Homo_sapiens",
#' species_to = "Mus_musculus",
#' Ensembl_version = 103
#' )
#' str(res)
#'
#' # Convert the human genes to mouse homologs and replace the raw counts in a Seurat object.
#' data("pancreas_sub")
#' counts <- pancreas_sub@assays$RNA@counts
#' res <- GeneConvert(
#' geneID = rownames(counts),
#' geneID_from_IDtype = "symbol",
#' geneID_to_IDtype = "symbol",
#' species_from = "Mus_musculus",
#' species_to = "Homo_sapiens",
#' Ensembl_version = 103
#' )
#' homologs_counts <- counts[res$geneID_expand[, "from_geneID"], ]
#' rownames(homologs_counts) <- res$geneID_expand[, "symbol"]
#' homologs_counts <- aggregate(homologs_counts, by = list(row.names(homologs_counts)), FUN = sum)
#' rownames(homologs_counts) <- homologs_counts[, 1]
#' homologs_counts <- as(as.matrix(homologs_counts[, -1]), "dgCMatrix")
#' homologs_counts
#'
#' @importFrom reshape2 dcast melt
#' @importFrom R.cache loadCache saveCache
#' @importFrom biomaRt listEnsemblArchives useMart listDatasets useDataset getBM listAttributes useEnsembl
#' @export
#'
GeneConvert <- function(geneID, geneID_from_IDtype = "symbol", geneID_to_IDtype = "entrez_id",
species_from = "Homo_sapiens", species_to = NULL,
Ensembl_version = 103, attempts = 5, mirror = NULL) {
if (requireNamespace("httr", quietly = TRUE)) {
httr::set_config(httr::config(ssl_verifypeer = FALSE, ssl_verifyhost = FALSE))
}
if (missing(geneID)) {
stop("'geneID' must be provided.")
}
if (is.null(species_to)) {
species_to <- species_from
}
if (is.null(Ensembl_version)) {
Ensembl_version <- "current_release"
}
species_from_split <- unlist(strsplit(species_from, split = "_"))
species_to_split <- unlist(strsplit(species_to, split = "_"))
species_from_simp <- paste0(tolower(substring(species_from_split[1], 1, 1)), species_from_split[2])
species_to_simp <- paste0(tolower(substring(species_to_split[1], 1, 1)), species_to_split[2])
geneID_from_IDtype <- sapply(geneID_from_IDtype, tolower)
geneID_to_IDtype <- sapply(unique(geneID_to_IDtype), tolower)
if ("symbol" %in% geneID_from_IDtype) {
geneID_from_IDtype <- geneID_from_IDtype[geneID_from_IDtype != "symbol"]
geneID_from_IDtype <- c("ensembl_symbol", "entrez_symbol", "uniprot_symbol", "wiki_symbol", geneID_from_IDtype)
}
from_IDtype <- sapply(geneID_from_IDtype, function(x) {
switch(x,
"ensembl_symbol" = "external_gene_name",
"ensembl_id" = "ensembl_gene_id",
"entrez_symbol" = "entrezgene_accession",
"entrez_id" = "entrezgene_id",
"uniprot_symbol" = "uniprot_gn_symbol",
"wiki_symbol" = "wikigene_name"
)
})
names(from_IDtype) <- geneID_from_IDtype
to_IDtype <- sapply(geneID_to_IDtype, function(x) {
switch(x,
"symbol" = "external_gene_name",
"ensembl_symbol" = "external_gene_name",
"entrez_symbol" = "external_gene_name",
"ensembl_id" = "ensembl_gene_id",
"entrez_id" = "entrezgene_id"
)
})
if (species_from != species_to && all(geneID_to_IDtype %in% c("symbol", "ensembl_id"))) {
to_IDtype <- sapply(to_IDtype, function(x) {
switch(x,
"external_gene_name" = "associated_gene_name",
"ensembl_gene_id" = "ensembl_gene"
)
})
to_attr <- paste(species_to_simp, to_IDtype, sep = "_homolog_")
names(to_attr) <- geneID_to_IDtype
} else {
to_attr <- to_IDtype
names(to_attr) <- geneID_to_IDtype
}
message("Connect to the Ensembl archives...")
archives <- NULL
ntry <- 0
while (is.null(archives)) {
ntry <- ntry + 1
archives <- tryCatch(expr = {
listEnsemblArchives()
}, error = function(error) {
message(error)
message("Get errors when connecting with EnsemblArchives...\nRetrying...")
Sys.sleep(1)
return(NULL)
})
if (is.null(archives) && ntry >= attempts) {
stop("Stop connecting...")
}
}
Ensembl_version <- as.character(Ensembl_version)
if (Ensembl_version == "current_release") {
url <- archives[which(archives$current_release == "*"), "url"]
version <- as.character(archives[which(archives$current_release == "*"), "version"])
message("Using the ", Ensembl_version, "(", version, ")", " version of biomart...")
} else if (Ensembl_version %in% archives$version) {
url <- archives[which(archives$version == Ensembl_version), "url"]
version <- as.character(archives[which(archives$version == Ensembl_version), "version"])
message("Using the ", version, " version of biomart...")
} else {
stop("Ensembl_version is invalid. Must be one of current_release,", paste0(archives$version, collapse = ","))
}
message("Connect to the biomart...")
mart <- NULL
ntry <- 0
while (is.null(mart)) {
ntry <- ntry + 1
if (!is.null(mirror)) {
mart <- tryCatch(
expr = {
useEnsembl("ensembl", mirror = mirror)
},
error = function(error) {
message(error)
message("Get errors when connecting with mirror...\nRetrying...")
Sys.sleep(1)
return(NULL)
}
)
}
if (is.null(mart)) {
mart <- tryCatch(
expr = {
useMart("ensembl", host = url)
},
error = function(error) {
message(error)
message("Get errors when connecting with ensembl mart...\nRetrying...")
Sys.sleep(1)
return(NULL)
}
)
}
if (is.null(mart) && ntry >= attempts) {
stop("Stop connecting...")
}
}
Datasets <- listDatasets(mart)
dataset <- paste0(species_from_simp, "_gene_ensembl")
if (!dataset %in% Datasets$dataset) {
warning(paste0("Can not find the dataset for the species: ", species_from, " (", dataset, ")"), immediate. = TRUE)
return(list(geneID_res = NULL, geneID_collapse = NULL, geneID_expand = NULL, Ensembl_version = version, Datasets = Datasets, Attributes = NULL))
}
message("Connect to the dataset ", dataset, " ...")
mart1 <- NULL
ntry <- 0
while (is.null(mart1)) {
ntry <- ntry + 1
mart1 <- tryCatch(
expr = {
useDataset(dataset = dataset, mart = mart)
},
error = function(error) {
message(error)
message("Get errors when connecting with ensembl mart...\nRetrying...")
Sys.sleep(1)
return(NULL)
}
)
if (is.null(mart1) && ntry >= attempts) {
stop("Stop connecting...")
}
}
if (species_from != species_to && any(!geneID_to_IDtype %in% c("symbol", "ensembl_id"))) {
dataset2 <- paste0(species_to_simp, "_gene_ensembl")
if (!dataset2 %in% Datasets$dataset) {
warning(paste0("Can not find the dataset for the species: ", species_from, " (", dataset2, ")"), immediate. = TRUE)
return(list(geneID_res = NULL, geneID_collapse = NULL, geneID_expand = NULL, Ensembl_version = version, Datasets = Datasets, Attributes = NULL))
}
message("Connect to the dataset ", dataset2, " ...")
mart2 <- NULL
ntry <- 0
while (is.null(mart2)) {
ntry <- ntry + 1
mart2 <- tryCatch(
expr = {
useDataset(dataset = dataset2, mart = mart)
},
error = function(error) {
message(error)
message("Get errors when connecting with ensembl mart...\nRetrying...")
Sys.sleep(1)
return(NULL)
}
)
if (is.null(mart2) && ntry >= attempts) {
stop("Stop connecting...")
}
}
}
Attributes <- listAttributes(mart1)
from_IDtype <- from_IDtype[from_IDtype %in% Attributes[, "name"]]
geneID_res_list <- list()
total <- length(geneID)
geneID_res <- NULL
if (any(!to_attr %in% Attributes$name)) {
to_attr_drop <- to_attr[!to_attr %in% Attributes$name]
to_attr <- to_attr[to_attr %in% Attributes$name]
warning(paste0("Can not find the attributes for the species ", species_from, ": ", paste(to_attr_drop, collapse = ", ")), immediate. = TRUE)
if (length(to_attr) == 0) {
warning("No attribute found for the species ", species_from, ". Please check the 'Attributes' in the result.", immediate. = TRUE)
return(list(geneID_res = NULL, geneID_collapse = NULL, geneID_expand = NULL, Ensembl_version = version, Datasets = Datasets, Attributes = Attributes))
}
}
message("Match and convert the geneID...\n")
if (species_from != species_to) {
for (from_attr in from_IDtype) {
if (length(geneID) > 0) {
geneID_res1 <- getBM(
mart = mart1,
attributes = c(from_attr, "ensembl_gene_id"),
filters = from_attr,
values = list(geneID)
)
geneID_res1 <- geneID_res1[, c(from_attr, "ensembl_gene_id"), drop = FALSE]
geneID_res1 <- geneID_res1[geneID_res1[, from_attr] %in% geneID, ]
if (nrow(geneID_res1) == 0) {
next
}
colnames(geneID_res1) <- c("from_geneID", "ensembl_gene_id_tmp")
from_name <- geneID_from_IDtype[which(from_IDtype == from_attr)]
geneID_res1[, "from_IDtype"] <- from_name
if (all(geneID_to_IDtype %in% c("symbol", "ensembl_id"))) {
geneID_res2 <- getBM(
mart = mart1,
attributes = unique(c("ensembl_gene_id", to_attr)),
filters = "ensembl_gene_id",
values = list(geneID_res1[, "ensembl_gene_id_tmp"])
)
geneID_res2 <- geneID_res2[, unique(c("ensembl_gene_id", to_attr))]
geneID_res2 <- geneID_res2[geneID_res2[, "ensembl_gene_id"] %in% geneID_res1[, "ensembl_gene_id_tmp"], ]
if (nrow(geneID_res2) == 0) {
next
}
geneID_res2[, "ensembl_gene_id_tmp"] <- geneID_res2[, "ensembl_gene_id"]
geneID_res2 <- geneID_res2[, c("ensembl_gene_id_tmp", to_attr)]
geneID_res2 <- unique(reshape2::melt(geneID_res2, id.vars = "ensembl_gene_id_tmp", variable.name = "to_IDtype", value.name = "to_geneID"))
geneID_res2$to_IDtype <- setNames(names(to_attr), nm = to_attr)[geneID_res2$to_IDtype]
geneID_res_merge <- merge(x = geneID_res1, y = geneID_res2, by = "ensembl_gene_id_tmp")
} else {
homolog_ensembl_gene <- paste(species_to_simp, "ensembl_gene", sep = "_homolog_")
geneID_res2 <- getBM(
mart = mart1,
attributes = c("ensembl_gene_id", homolog_ensembl_gene),
filters = "ensembl_gene_id",
values = list(geneID_res1[, "ensembl_gene_id_tmp"])
)
geneID_res2 <- geneID_res2[, c("ensembl_gene_id", homolog_ensembl_gene), drop = FALSE]
geneID_res2 <- geneID_res2[geneID_res2[, "ensembl_gene_id"] %in% geneID_res1[, "ensembl_gene_id_tmp"], ]
if (nrow(geneID_res2) == 0) {
next
}
colnames(geneID_res2) <- c("ensembl_gene_id_tmp", homolog_ensembl_gene)
geneID_res3 <- getBM(
mart = mart2,
attributes = unique(c("ensembl_gene_id", to_attr)),
filters = "ensembl_gene_id",
values = list(geneID_res2[, homolog_ensembl_gene])
)
geneID_res3 <- geneID_res3[, unique(c("ensembl_gene_id", to_attr)), drop = FALSE]
geneID_res3 <- geneID_res3[geneID_res3[, "ensembl_gene_id"] %in% geneID_res2[, homolog_ensembl_gene], ]
if (nrow(geneID_res3) == 0) {
next
}
geneID_res3[, "ensembl_gene_id_tmp2"] <- geneID_res3[, "ensembl_gene_id"]
geneID_res3 <- geneID_res3[, c("ensembl_gene_id_tmp2", to_attr)]
geneID_res3 <- unique(reshape2::melt(geneID_res3, id.vars = "ensembl_gene_id_tmp2", variable.name = "to_IDtype", value.name = "to_geneID"))
colnames(geneID_res3)[1] <- homolog_ensembl_gene
geneID_res3$to_IDtype <- setNames(names(to_attr), nm = to_attr)[geneID_res3$to_IDtype]
geneID_res_merge <- Reduce(merge, list(geneID_res1, geneID_res2, geneID_res3))
}
geneID_res_merge[geneID_res_merge == ""] <- NA
geneID_res_list[[from_attr]] <- geneID_res_merge[, c("from_IDtype", "from_geneID", "to_IDtype", "to_geneID")]
ismap <- geneID %in% geneID_res_list[[from_attr]][, "from_geneID"]
message(paste(sum(ismap), "genes mapped with", from_name))
geneID <- geneID[!ismap]
}
}
} else {
for (from_attr in from_IDtype) {
if (length(geneID) > 0) {
geneID_res1 <- getBM(
mart = mart1,
attributes = unique(c("ensembl_gene_id", from_attr, to_attr)),
filters = from_attr,
values = list(geneID)
)
geneID_res1 <- geneID_res1[, unique(c("ensembl_gene_id", from_attr, to_attr)), drop = FALSE]
geneID_res1 <- geneID_res1[geneID_res1[, from_attr] %in% geneID, ]
if (nrow(geneID_res1) == 0) {
next
}
geneID_res1[, "ensembl_gene_id_tmp"] <- geneID_res1[, "ensembl_gene_id"]
geneID_res1_from <- unique(geneID_res1[, c("ensembl_gene_id_tmp", from_attr)])
colnames(geneID_res1_from) <- c("ensembl_gene_id_tmp", "from_geneID")
from_name <- geneID_from_IDtype[which(from_IDtype == from_attr)]
geneID_res1_from[, "from_IDtype"] <- from_name
geneID_res1_to <- unique(geneID_res1[, c("ensembl_gene_id_tmp", to_attr)])
geneID_res1_to <- unique(reshape2::melt(geneID_res1_to, id.vars = "ensembl_gene_id_tmp", variable.name = "to_IDtype", value.name = "to_geneID"))
geneID_res1_to$to_IDtype <- setNames(names(to_attr), nm = to_attr)[geneID_res1_to$to_IDtype]
geneID_res_merge <- merge(x = geneID_res1_from, y = geneID_res1_to, by = "ensembl_gene_id_tmp")
geneID_res_merge[geneID_res_merge == ""] <- NA
geneID_res_list[[from_attr]] <- geneID_res_merge[, c("from_IDtype", "from_geneID", "to_IDtype", "to_geneID")]
ismap <- geneID %in% geneID_res_list[[from_attr]][, "from_geneID"]
message(paste(sum(ismap), "genes mapped with", from_name))
geneID <- geneID[!ismap]
}
}
}
message(
paste0(
paste0(rep("=", 30), collapse = ""), "\n",
total - length(geneID), " genes mapped\n",
length(geneID), " genes unmapped"
), "\n",
paste0(rep("=", 30), collapse = ""), "\n"
)
geneID_res <- unique(do.call(rbind, geneID_res_list))
if (is.null(geneID_res) || nrow(geneID_res) == 0 || all(is.na(geneID_res[["to_geneID"]]))) {
warning(paste0("None of the gene IDs were converted"), immediate. = TRUE)
return(list(geneID_res = NULL, geneID_collapse = NULL, geneID_expand = NULL, Datasets = Datasets, Attributes = Attributes))
}
geneID_res_stat <- by(geneID_res, list(geneID_res[["from_geneID"]], geneID_res[["to_IDtype"]]), function(x) {
data.frame(
from_IDtype = unique(x[["from_IDtype"]]),
from_geneID = unique(x[["from_geneID"]]),
to_IDtype = unique(x[["to_IDtype"]]),
to_geneID = I(list(unique(x[["to_geneID"]][!x[["to_geneID"]] %in% c("", NA)])))
)
})
geneID_collapse <- do.call(rbind, geneID_res_stat)
geneID_collapse <- unique(as.data.frame(geneID_collapse[, c("from_geneID", "to_IDtype", "to_geneID")]))
geneID_collapse <- geneID_collapse[sapply(geneID_collapse$to_geneID, length) > 0, ]
geneID_collapse <- reshape2::dcast(geneID_collapse, formula = from_geneID ~ to_IDtype, value.var = "to_geneID")
rownames(geneID_collapse) <- geneID_collapse[, "from_geneID"]
geneID_expand <- unnest(data = geneID_collapse, cols = colnames(geneID_collapse)[sapply(geneID_collapse, class) == "list"], keep_empty = FALSE)
return(list(geneID_res = geneID_res, geneID_collapse = geneID_collapse, geneID_expand = geneID_expand, Ensembl_version = version, Datasets = Datasets, Attributes = Attributes, geneID_unmapped = geneID))
}
#' Prefetch cycle gene
#'
#' Based on the human cell cycle genes, the cell cycle genes of the corresponding species were captured by homologous gene conversion.
#'
#' @inheritParams GeneConvert
#' @param species Latin names for animals,i.e., "Homo_sapiens", "Mus_musculus"
#' @param use_cached_gene Whether to use previously cached cell cycle gene conversion results for the species.
#'
#' @return A list of S-phase and G2M-phase genes.
#'
#' @examples
#' ccgenes <- CC_GenePrefetch("Homo_sapiens")
#' str(ccgenes)
#' ccgenes <- CC_GenePrefetch("Mus_musculus")
#' str(ccgenes)
#' @importFrom R.cache loadCache saveCache
#' @export
CC_GenePrefetch <- function(species = "Homo_sapiens", Ensembl_version = 103, mirror = NULL, attempts = 5, use_cached_gene = TRUE) {
cc_S_genes <- Seurat::cc.genes.updated.2019$s.genes
cc_G2M_genes <- Seurat::cc.genes.updated.2019$g2m.genes
res <- NULL
if (species != "Homo_sapiens") {
if (isTRUE(use_cached_gene)) {
res <- loadCache(key = list(species))
}
if (is.null(res)) {
res <- GeneConvert(
geneID = unique(c(cc_S_genes, cc_G2M_genes)),
geneID_from_IDtype = "symbol",
geneID_to_IDtype = "symbol",
species_from = "Homo_sapiens",
species_to = species,
Ensembl_version = Ensembl_version,
attempts = attempts,
mirror = mirror
)
saveCache(res, key = list(species))
} else {
message("Using cached conversion results for ", species)
}
genes <- res[["geneID_collapse"]]
cc_S_genes <- unlist(genes[cc_S_genes[cc_S_genes %in% rownames(genes)], "symbol"])
cc_G2M_genes <- unlist(genes[cc_G2M_genes[cc_G2M_genes %in% rownames(genes)], "symbol"])
}
return(list(
res = res,
cc_S_genes = cc_S_genes,
cc_G2M_genes = cc_G2M_genes
))
}
LengthCheck <- function(values, cutoff = 0) {
return(vapply(X = values, FUN = function(x) {
return(length(x = x) > cutoff)
}, FUN.VALUE = logical(1)))
}
#' @importFrom Seurat UpdateSymbolList CaseMatch
#' @importFrom SeuratObject DefaultAssay GetAssayData CheckGC
#' @importFrom BiocParallel bplapply bpaggregate
#' @importFrom stats rnorm
AddModuleScore2 <- function(object, slot = "data", features, pool = NULL, nbin = 24, ctrl = 100,
k = FALSE, assay = NULL, name = "Cluster", seed = 1, search = FALSE,
BPPARAM = BiocParallel::bpparam(), ...) {
if (!is.null(x = seed)) {
set.seed(seed = seed)
}
assay.old <- DefaultAssay(object = object)
assay <- assay %||% assay.old
DefaultAssay(object = object) <- assay
assay.data <- GetAssayData(object = object, slot = slot)
features.old <- features
if (k) {
.NotYetUsed(arg = "k")
features <- list()
for (i in as.numeric(x = names(x = table([email protected][[1]]$cluster)))) {
features[[i]] <- names(x = which(x = [email protected][[1]]$cluster ==
i))
}
cluster.length <- length(x = features)
} else {
if (is.null(x = features)) {
stop("Missing input feature list")
}
features <- lapply(X = features, FUN = function(x) {
missing.features <- setdiff(x = x, y = rownames(x = object))
if (length(x = missing.features) > 0) {
warning("The following features are not present in the object: ",
paste(missing.features, collapse = ", "),
ifelse(test = search, yes = ", attempting to find updated synonyms",
no = ", not searching for symbol synonyms"
),
call. = FALSE, immediate. = TRUE
)
if (search) {
tryCatch(expr = {
updated.features <- UpdateSymbolList(symbols = missing.features, ...)
names(x = updated.features) <- missing.features
for (miss in names(x = updated.features)) {
index <- which(x == miss)
x[index] <- updated.features[miss]
}
}, error = function(...) {
warning("Could not reach HGNC's gene names database",
call. = FALSE, immediate. = TRUE
)
})
missing.features <- setdiff(x = x, y = rownames(x = object))
if (length(x = missing.features) > 0) {
warning("The following features are still not present in the object: ",
paste(missing.features, collapse = ", "),
call. = FALSE, immediate. = TRUE
)
}
}
}
return(intersect(x = x, y = rownames(x = object)))
})
cluster.length <- length(x = features)
}
if (!all(LengthCheck(values = features))) {
warning(paste(
"Could not find enough features in the object from the following feature lists:",
paste(names(x = which(x = !LengthCheck(values = features)))),
"Attempting to match case..."
))
features <- lapply(X = features.old, FUN = CaseMatch, match = rownames(x = object))
}
if (!all(LengthCheck(values = features))) {
stop(paste(
"The following feature lists do not have enough features present in the object:",
paste(names(x = which(x = !LengthCheck(values = features)))),
"exiting..."
))
}
pool <- pool %||% rownames(x = object)
data.avg <- Matrix::rowMeans(x = assay.data[pool, ])
data.avg <- data.avg[order(data.avg)]
data.cut <- ggplot2::cut_number(x = data.avg + rnorm(n = length(data.avg)) / 1e+30, n = nbin, labels = FALSE, right = FALSE)
names(x = data.cut) <- names(x = data.avg)
scores <- bplapply(1:cluster.length, function(i, features, data.cut, assay.data, ctrl) {
features.use <- features[[i]]
# ctrl.use <- data.cut[which(data.cut %in% data.cut[features.use])]
# ctrl.use <- names(sample(ctrl.use, size = min(ctrl * length(features.use), length(ctrl.use)), replace = FALSE))
ctrl.use <- unlist(lapply(1:length(features.use), function(j) {
data.cut[which(data.cut == data.cut[features.use[j]])]
}))
ctrl.use <- names(sample(ctrl.use, size = min(ctrl * length(features.use), length(ctrl.use)), replace = FALSE))
ctrl.scores_i <- Matrix::colMeans(x = assay.data[ctrl.use, , drop = FALSE])
features.scores_i <- Matrix::colMeans(x = assay.data[features.use, , drop = FALSE])
return(list(ctrl.scores_i, features.scores_i))
}, BPPARAM = BPPARAM, features = features, data.cut = data.cut, assay.data = assay.data, ctrl = ctrl)
ctrl.scores <- do.call(rbind, lapply(scores, function(x) x[[1]]))
features.scores <- do.call(rbind, lapply(scores, function(x) x[[2]]))
# features_collapse <- bplapply(1:cluster.length, function(i) {
# features.use <- features[[i]]
# # ctrl.use <- data.cut[which(data.cut %in% data.cut[features.use])]
# # ctrl.use <- names(sample(ctrl.use, size = min(ctrl * length(features.use), length(ctrl.use)), replace = FALSE))
# ctrl.use <- unlist(lapply(1:length(features.use), function(j) {
# data.cut[which(data.cut == data.cut[features.use[j]])]
# }))
# ctrl.use <- names(sample(ctrl.use, size = min(ctrl * length(features.use), length(ctrl.use)), replace = FALSE))
# return(list(features.use, ctrl.use))
# }, BPPARAM = BPPARAM)
# features.scores <- bpaggregate(
# x = as.matrix(assay.data[unlist(lapply(features_collapse, function(x) x[[1]])), ]),
# by = list(unlist(lapply(1:length(features_collapse), function(x) rep(x, length(features_collapse[[x]][[1]]))))),
# FUN = mean,
# BPPARAM = BPPARAM
# )
# features.scores <- features.scores[,-1]
# ctrl.scores <- bpaggregate(
# x = as.matrix(assay.data[unlist(lapply(features_collapse, function(x) x[[2]])), ]),
# by = list(unlist(lapply(1:length(features_collapse), function(x) rep(x, length(features_collapse[[x]][[2]]))))),
# FUN = mean,
# BPPARAM = BPPARAM
# )
# ctrl.scores <- ctrl.scores[,-1]
features.scores.use <- features.scores - ctrl.scores
rownames(x = features.scores.use) <- paste0(name, 1:cluster.length)
features.scores.use <- as.data.frame(x = t(x = features.scores.use))
rownames(x = features.scores.use) <- colnames(x = object)
object[[colnames(x = features.scores.use)]] <- features.scores.use
CheckGC()
DefaultAssay(object = object) <- assay.old
return(object)
}
#' CellScoring
#'
#' @param srt
#' @param features
#' @param method
#' @param classification
#' @param name
#' @param slot
#' @param assay
#' @param new_assay
#' @param ...
#' @param split.by
#' @param IDtype
#' @param species
#' @param db
#' @param termnames
#' @param db_update
#' @param db_version
#' @param convert_species
#' @param Ensembl_version
#' @param mirror
#' @param minGSSize
#' @param maxGSSize
#' @param BPPARAM
#' @param progressbar
#' @param force
#' @param seed
#'
#' @examples
#' data("pancreas_sub")
#' ccgenes <- CC_GenePrefetch("Mus_musculus")
#' pancreas_sub <- CellScoring(
#' srt = pancreas_sub,
#' features = list(S = ccgenes$cc_S_genes, G2M = ccgenes$cc_G2M_genes),
#' method = "Seurat", name = "CC"
#' )
#' ClassDimPlot(pancreas_sub, "CC_classification")
#' ExpDimPlot(pancreas_sub, "CC_G2M")
#'
#' \dontrun{
#' data("panc8_sub")
#' panc8_sub <- Integration_SCP(panc8_sub,
#' batch = "tech", integration_method = "Seurat"
#' )
#' ClassDimPlot(panc8_sub, group.by = c("tech", "celltype"))
#'
#' panc8_sub <- CellScoring(
#' srt = panc8_sub, slot = "data", assay = "RNA",
#' db = "GO_BP", species = "Homo_sapiens",
#' minGSSize = 10, maxGSSize = 100,
#' method = "Seurat", name = "GO", new_assay = TRUE
#' )
#' panc8_sub <- Integration_SCP(panc8_sub,
#' assay = "GO",
#' batch = "tech", integration_method = "Seurat"
#' )
#' ClassDimPlot(panc8_sub, group.by = c("tech", "celltype"))
#'
#' pancreas_sub <- CellScoring(
#' srt = pancreas_sub, slot = "data", assay = "RNA",
#' db = "GO_BP", species = "Mus_musculus",
#' termnames = panc8_sub[["GO"]]@meta.features[, "termnames"],
#' method = "Seurat", name = "GO", new_assay = TRUE
#' )
#' pancreas_sub <- Standard_SCP(pancreas_sub, assay = "GO")
#' ClassDimPlot(pancreas_sub, "SubCellType")
#'
#' pancreas_sub[["tech"]] <- "Mouse"
#' panc_merge <- Integration_SCP(
#' srtList = list(panc8_sub, pancreas_sub),
#' assay = "GO",
#' batch = "tech", integration_method = "Seurat"
#' )
#' ClassDimPlot(panc_merge, group.by = c("tech", "celltype", "SubCellType", "Phase"))
#'
#' genenames <- make.unique(capitalize(rownames(panc8_sub[["RNA"]]), force_tolower = TRUE))
#' panc8_sub <- RenameFeatures(panc8_sub, newnames = genenames, assay = "RNA")
#' head(rownames(panc8_sub))
#' panc_merge <- Integration_SCP(
#' srtList = list(panc8_sub, pancreas_sub),
#' assay = "RNA",
#' batch = "tech", integration_method = "Seurat"
#' )
#' ClassDimPlot(panc_merge, group.by = c("tech", "celltype", "SubCellType", "Phase"))
#' }
#'
#' @importFrom Seurat AddModuleScore AddMetaData
#' @export
CellScoring <- function(srt, features = NULL, slot = "data", assay = "RNA", split.by = NULL,
IDtype = "symbol", species = "Homo_sapiens",
db = "GO_BP", termnames = NULL, db_update = FALSE, db_version = "latest", convert_species = FALSE,
Ensembl_version = 103, mirror = NULL, minGSSize = 10, maxGSSize = 500,
method = "Seurat", classification = TRUE, name = "", new_assay = FALSE,
BPPARAM = BiocParallel::bpparam(), progressbar = TRUE, force = FALSE, seed = 11, ...) {
set.seed(seed)
if (!method %in% c("Seurat", "AUCell", "UCell")) {
stop("method must be 'Seurat', 'AUCell'or 'UCell'.")
}
if (slot == "counts") {
status <- check_DataType(srt, slot = "counts", assay = assay)
if (status != "raw_counts") {
warning("Data is not raw counts", immediate. = TRUE)
}
}
if (slot == "data") {
status <- check_DataType(srt, slot = "data", assay = assay)
if (status == "raw_counts") {
cat("Data is raw counts. Perform NormalizeData(LogNormalize) on the data ...\n", sep = "")
srt <- suppressWarnings(NormalizeData(object = srt, assay = assay, normalization.method = "LogNormalize", verbose = FALSE))
}
if (status == "raw_normalized_counts") {
cat("Data is normalized without log transformation. Perform NormalizeData(LogNormalize) on the data...\n", sep = "")
srt <- suppressWarnings(NormalizeData(object = srt, assay = assay, normalization.method = "LogNormalize", verbose = FALSE))
}
if (status == "unknown") {
warning("Can not determine whether data ", i, " is log-normalized...\n", immediate. = TRUE)
}
}
if (name == "" && isTRUE(new_assay)) {
stop("name must be specified when new_assay=TRUE")
}
if (is.null(features)) {
for (single_db in db) {
db_list <- PrepareDB(
species = species, db = single_db, db_update = db_update, db_version = db_version,
db_IDtypes = IDtype, convert_species = convert_species, Ensembl_version = Ensembl_version, mirror = mirror
)
TERM2GENE_tmp <- db_list[[species]][[single_db]][["TERM2GENE"]][, c("Term", IDtype)]
TERM2NAME_tmp <- db_list[[species]][[single_db]][["TERM2NAME"]]
dup <- duplicated(TERM2GENE_tmp)
na <- rowSums(is.na(TERM2GENE_tmp)) > 0
TERM2GENE_tmp <- TERM2GENE_tmp[!(dup | na), ]
TERM2NAME_tmp <- TERM2NAME_tmp[TERM2NAME_tmp[, "Term"] %in% TERM2GENE_tmp[, "Term"], ]
TERM2GENE_tmp <- unique(TERM2GENE_tmp)
TERM2NAME_tmp <- unique(TERM2NAME_tmp)
rownames(TERM2NAME_tmp) <- TERM2NAME_tmp[, "Term"]
features_tmp <- split(TERM2GENE_tmp[, IDtype], TERM2NAME_tmp[TERM2GENE_tmp[, "Term"], "Name"])
if (is.null(termnames)) {
GSSize <- sapply(features_tmp, length)
features_tmp <- features_tmp[GSSize >= minGSSize & GSSize <= maxGSSize]
} else {
if (length(intersect(termnames, names(features_tmp)) > 0)) {
features_tmp <- features_tmp[intersect(termnames, names(features_tmp))]
} else {
stop("None of termnames found in the db: ", single_db)
}
}
features <- c(features, features_tmp)
}
}
if (!is.list(features) || length(names(features)) == 0) {
stop("'features' must be a named list")
}
expressed <- names(which(rowSums(GetAssayData(srt, slot = slot, assay = assay) > 0) > 0))
features <- lapply(setNames(names(features), names(features)), function(x) features[[x]][features[[x]] %in% expressed])
filtered_none <- names(which(sapply(features, length) == 0))
if (length(filtered_none) > 0) {
warning("The following list of features were filtered because none of features were found in the srt assay:\n", paste0(filtered_none, collapse = ", "), immediate. = TRUE)
}
features <- features[!names(features) %in% filtered_none]
features_raw <- features
names(features) <- make.names(names(features))
message("Number of feature lists to be scored: ", length(features))
if ("progressbar" %in% names(BPPARAM)) {
BPPARAM[["progressbar"]] <- progressbar
}
time_start <- Sys.time()
cat(paste0("[", time_start, "] ", "Start CellScoring\n"))
message("Threads used: ", BPPARAM$workers)
if (!is.null(split.by)) {
split_list <- SplitObject(srt, split.by = split.by)
} else {
split_list <- list(srt)
}
scores_list <- list()
features_nm_list <- list()
for (i in seq_along(split_list)) {
srt_sp <- split_list[[i]]
if (method == "Seurat") {
## need to add a 'slot' parameter
srt_tmp <- AddModuleScore2(
srt_sp,
features = features,
name = name,
slot = slot,
assay = assay,
BPPARAM = BPPARAM,
...
)
if (name != "") {
scores <- srt_tmp[[paste0(name, seq_along(features))]]
} else {
scores <- srt_tmp[[paste0("X", seq_along(features))]]
}
features_nm <- features_raw
colnames(scores) <- make.names(paste(name, names(features_nm), sep = "_"))
} else if (method == "UCell") {
check_R("UCell")
srt_tmp <- UCell::AddModuleScore_UCell(
srt_sp,
features = features,
name = name,
slot = slot,
assay = assay,
BPPARAM = BPPARAM,
...
)
filtered <- names(features)[!paste0(names(features), name) %in% colnames([email protected])]
if (length(filtered) > 0) {
warning("The following list of features were filtered when scoring:\n", paste0(filtered, collapse = ", "), immediate. = TRUE)
}
features_keep <- features[!names(features) %in% filtered]
features_nm <- features_raw[!names(features) %in% filtered]
scores <- srt_tmp[[paste0(names(features_keep), name)]]
colnames(scores) <- make.names(paste(name, names(features_nm), sep = "_"))
} else if (method == "AUCell") {
check_R("AUCell")
CellRank <- AUCell::AUCell_buildRankings(as.matrix(GetAssayData(srt_sp, slot = slot, assay = assay)), BPPARAM = BPPARAM, plotStats = FALSE)
cells_AUC <- AUCell::AUCell_calcAUC(geneSets = features, rankings = CellRank, ...)
filtered <- names(features)[!names(features) %in% rownames(AUCell::getAUC(cells_AUC))]
if (length(filtered) > 0) {
warning("The following list of features were filtered when scoring:", paste0(filtered, collapse = ", "), immediate. = TRUE)
}
features_keep <- features[!names(features) %in% filtered]
features_nm <- features_raw[!names(features) %in% filtered]
scores <- as.data.frame(t(AUCell::getAUC(cells_AUC)))[, names(features_keep)]
colnames(scores) <- make.names(paste(name, names(features_nm), sep = "_"))
}
features_nm_list[[i]] <- setNames(object = names(features_nm), nm = colnames(scores))
scores_list[[i]] <- scores
}
features_used <- Reduce(intersect, lapply(scores_list, colnames))
features_nm_used <- Reduce(intersect, features_nm_list)
scores_mat <- do.call(rbind, lapply(scores_list, function(x) x[intersect(rownames(x), colnames(srt)), features_used, drop = FALSE]))
if (isTRUE(new_assay)) {
srt[[name]] <- CreateAssayObject(counts = t(as.matrix(scores_mat[colnames(srt), , drop = FALSE])))
srt[[name]] <- AddMetaData(object = srt[[name]], metadata = data.frame(termnames = features_nm_used[colnames(scores_mat)]))
} else {
srt <- AddMetaData(object = srt, metadata = scores_mat)
}
if (isTRUE(classification)) {
assignments <- apply(scores_mat, MARGIN = 1, FUN = function(x) {
if (all(x < 0)) {
return(NA)
} else {
if (length(which(x == max(x))) > 1) {
return(NA)
} else {
return(names(features)[which(x == max(x))])
}
}
})
srt[[paste0(name, "_classification")]] <- assignments[rownames(scores_mat)]
}
time_end <- Sys.time()
cat(paste0("[", time_end, "] ", "CellScoring done\n"))
cat("Elapsed time:", format(round(difftime(time_end, time_start), 2), format = "%Y-%m-%d %H:%M:%S"), "\n")
return(srt)
}
metap <- function(p, method = c("maximump", "minimump", "wilkinsonp", "meanp", "sump", "votep"), ...) {
method <- match.arg(method)
res <- do.call(method, args = list(p = p, ...))
return(res)
}
#' @importFrom stats pbeta
wilkinsonp <- function(p, r = 1, alpha = 0.05, log.p = FALSE) {
alpha <- ifelse(alpha > 1, alpha / 100, alpha)
stopifnot(alpha > 0, alpha < 1)
alpha <- ifelse(alpha > 0.5, 1 - alpha, alpha)
keep <- (p >= 0) & (p <= 1)
invalid <- sum(1L * keep) < 2
if (invalid) {
warning("Must have at least two valid p values")
res <- list(
p = NA_real_, pr = NA_real_, r = r, critp = NA_real_,
alpha = alpha, validp = p[keep]
)
} else {
pi <- p[keep]
k <- length(pi)
if (k != length(p)) {
warning("Some studies omitted")
}
if ((r < 1) | (r > k)) {
r <- 1
warning("Illegal r set to 1")
}
pi <- sort(pi)
pr <- pi[r]
res <- list(
p = pbeta(pr, r, k + 1 - r, log.p = log.p),
pr = pr, r = r, critp = qbeta(alpha, r, k + 1 - r),
alpha = alpha, validp = pi
)
}
res
}
maximump <- function(p, alpha = 0.05, log.p = FALSE) {
keep <- (p >= 0) & (p <= 1)
validp <- p[keep]
k <- length(validp)
res <- wilkinsonp(p, r = k, alpha, log.p)
res
}
minimump <- function(p, alpha = 0.05, log.p = FALSE) {
res <- wilkinsonp(p, r = 1, alpha, log.p)
res
}
#' @importFrom stats pnorm
meanp <- function(p) {
keep <- (p >= 0) & (p <= 1)
invalid <- sum(1L * keep) < 4
if (invalid) {
warning("Must have at least four valid p values")
res <- list(z = NA_real_, p = NA_real_, validp = p[keep])
} else {
pi <- mean(p[keep])
k <- length(p[keep])
z <- (0.5 - pi) * sqrt(12 * k)
if (k != length(p)) {
warning("Some studies omitted")
}
res <- list(
z = z, p = pnorm(z, lower.tail = FALSE),
validp = p[keep]
)
}
res
}
#' @importFrom stats pnorm
sump <- function(p) {
keep <- (p >= 0) & (p <= 1)
invalid <- sum(1L * keep) < 2
if (invalid) {
warning("Must have at least two valid p values")
res <- list(p = NA_real_, conservativep = NA_real_, validp = p[keep])
} else {
sigmap <- sum(p[keep])
k <- length(p[keep])
conservativep <- exp(k * log(sigmap) - lgamma(k + 1))
nterm <- floor(sigmap) + 1
denom <- lfactorial(k)
psum <- 0
terms <- vector("numeric", nterm)
for (i in 1:nterm) {
terms[i] <- lchoose(k, i - 1) + k * log(sigmap -
i + 1) - denom
pm <- 2 * (i %% 2) - 1
psum <- psum + pm * exp(terms[i])
}
if (k != length(p)) {
warning("Some studies omitted")
}
if (sigmap > 20) {
warning("Likely to be unreliable, check with another method")
}
res <- list(
p = psum, conservativep = conservativep,
validp = p[keep]
)
}
res
}
#' @importFrom stats binom.test
votep <- function(p, alpha = 0.5) {
alpha <- ifelse(alpha > 1, alpha / 100, alpha)
stopifnot(alpha > 0, alpha < 1)
keep <- (p >= 0) & (p <= 1)
alp <- vector("numeric", 2)
if (alpha <= 0.5) {
alp[1] <- alpha
alp[2] <- 1 - alpha
} else {
alp[2] <- alpha
alp[1] <- 1 - alpha
}
invalid <- sum(1L * keep) < 2
if (invalid) {
warning("Must have at least two valid p values")
res <- list(
p = NA_real_, pos = NA_integer_, neg = NA_integer_,
alpha = alpha, validp = p[keep]
)
} else {
pi <- p[keep]
k <- length(pi)
pos <- sum(1L * (pi < alp[1]))
neg <- sum(1L * (pi > alp[2]))
if (k != length(p)) {
warning("Some studies omitted")
}
if ((pos + neg) <= 0) {
warning("All p values are within specified limits of alpha")
p <- 1
} else {
p <- binom.test(pos, pos + neg, 0.5, alternative = "greater")$p.value
}
res <- list(
p = p, pos = pos, neg = neg, alpha = alpha,
validp = pi
)