SCP-cellqc.R

#' Run doublet-calling with scDblFinder
#'
#' This function performs doublet-calling using the scDblFinder package on a Seurat object.
#'
#' @param srt A Seurat object.
#' @param assay The name of the assay to be used for doublet-calling. Default is "RNA".
#' @param db_rate The expected doublet rate. Default is calculated as ncol(srt) / 1000 * 0.01.
#' @param ... Additional arguments to be passed to scDblFinder::scDblFinder function.
#'
#' @examples
#' data("pancreas_sub")
#' pancreas_sub <- db_scDblFinder(pancreas_sub)
#' CellDimPlot(pancreas_sub, reduction = "umap", group.by = "db.scDblFinder_class")
#' FeatureDimPlot(pancreas_sub, reduction = "umap", features = "db.scDblFinder_score")
#' @importFrom Seurat as.SingleCellExperiment
#' @export
db_scDblFinder <- function(srt, assay = "RNA", db_rate = ncol(srt) / 1000 * 0.01, ...) {
  if (!inherits(srt, "Seurat")) {
    stop("'srt' is not a Seurat object.")
  }
  status <- check_DataType(srt, slot = "counts", assay = assay)
  if (status != "raw_counts") {
    stop("Data type is not raw counts!")
  }
  check_R("scDblFinder")
  sce <- as.SingleCellExperiment(srt, assay = assay)
  sce <- scDblFinder::scDblFinder(sce, dbr = db_rate, verbose = FALSE, ...)
  srt[["db.scDblFinder_score"]] <- sce[["scDblFinder.score"]]
  srt[["db.scDblFinder_class"]] <- sce[["scDblFinder.class"]]
  return(srt)
}

#' Run doublet-calling with scds
#'
#' This function performs doublet-calling using the scds package on a Seurat object.
#'
#' @param srt A Seurat object.
#' @param assay The name of the assay to be used for doublet-calling. Default is "RNA".
#' @param db_rate The expected doublet rate. Default is calculated as ncol(srt) / 1000 * 0.01.
#' @param method The method to be used for doublet-calling. Options are "hybrid", "cxds", or "bcds".
#' @param ... Additional arguments to be passed to scds::cxds_bcds_hybrid function.
#'
#' @examples
#' data("pancreas_sub")
#' pancreas_sub <- db_scds(pancreas_sub, method = "hybrid")
#' CellDimPlot(pancreas_sub, reduction = "umap", group.by = "db.scds_hybrid_class")
#' FeatureDimPlot(pancreas_sub, reduction = "umap", features = "db.scds_hybrid_score")
#' @importFrom Seurat as.SingleCellExperiment
#' @export
db_scds <- function(srt, assay = "RNA", db_rate = ncol(srt) / 1000 * 0.01, method = c("hybrid", "cxds", "bcds"), ...) {
  if (!inherits(srt, "Seurat")) {
    stop("'srt' is not a Seurat object.")
  }
  status <- check_DataType(srt, slot = "counts", assay = assay)
  if (status != "raw_counts") {
    stop("Data type is not raw counts!")
  }
  check_R("scds")
  method <- match.arg(method)
  sce <- as.SingleCellExperiment(srt, assay = assay)
  sce <- scds::cxds_bcds_hybrid(sce, ...)
  srt[["db.scds_cxds_score"]] <- sce[["cxds_score"]]
  srt[["db.scds_bcds_score"]] <- sce[["bcds_score"]]
  srt[["db.scds_hybrid_score"]] <- sce[["hybrid_score"]]
  ntop <- ceiling(db_rate * ncol(sce))
  db_qc <- names(sort(srt[[paste0("db.scds_", method, "_score"), drop = TRUE]], decreasing = TRUE)[1:ntop])
  srt[[paste0("db.scds_", method, "_class")]] <- "singlet"
  srt[[paste0("db.scds_", method, "_class")]][db_qc, ] <- "doublet"
  return(srt)
}

#' Run doublet-calling with Scrublet
#'
#' This function performs doublet-calling using the scrublet(python) package on a Seurat object.
#'
#' @param srt A Seurat object.
#' @param assay The name of the assay to be used for doublet-calling. Default is "RNA".
#' @param db_rate The expected doublet rate. Default is calculated as ncol(srt) / 1000 * 0.01.
#' @param ... Additional arguments to be passed to scrublet.Scrublet function.
#'
#' @examples
#' data("pancreas_sub")
#' pancreas_sub <- db_Scrublet(pancreas_sub)
#' CellDimPlot(pancreas_sub, reduction = "umap", group.by = "db.Scrublet_class")
#' FeatureDimPlot(pancreas_sub, reduction = "umap", features = "db.Scrublet_score")
#' @importFrom reticulate import
#' @importFrom Seurat GetAssayData
#' @export
db_Scrublet <- function(srt, assay = "RNA", db_rate = ncol(srt) / 1000 * 0.01, ...) {
  if (!inherits(srt, "Seurat")) {
    stop("'srt' is not a Seurat object.")
  }
  status <- check_DataType(srt, slot = "counts", assay = assay)
  if (status != "raw_counts") {
    stop("Data type is not raw counts!")
  }
  check_Python("scrublet")
  scr <- import("scrublet")
  raw_counts <- t(as_matrix(GetAssayData(object = srt, assay = assay, slot = "counts")))
  scrub <- scr$Scrublet(raw_counts, expected_doublet_rate = db_rate, ...)
  res <- scrub$scrub_doublets()
  doublet_scores <- res[[1]]
  predicted_doublets <- res[[2]]

  srt[["db.Scrublet_score"]] <- doublet_scores
  srt[["db.Scrublet_class"]] <- sapply(predicted_doublets, function(i) {
    switch(as.character(i),
      "FALSE" = "singlet",
      "TRUE" = "doublet"
    )
  })
  return(srt)
}

#' Run doublet-calling with DoubletDetection
#'
#' This function performs doublet-calling using the doubletdetection(python) package on a Seurat object.
#'
#' @param srt A Seurat object.
#' @param assay The name of the assay to be used for doublet-calling. Default is "RNA".
#' @param db_rate The expected doublet rate. Default is calculated as ncol(srt) / 1000 * 0.01.
#' @param ... Additional arguments to be passed to doubletdetection.BoostClassifier function.
#'
#' @examples
#' data("pancreas_sub")
#' pancreas_sub <- db_DoubletDetection(pancreas_sub)
#' CellDimPlot(pancreas_sub, reduction = "umap", group.by = "db.DoubletDetection_class")
#' FeatureDimPlot(pancreas_sub, reduction = "umap", features = "db.DoubletDetection_score")
#' @importFrom reticulate import
#' @importFrom Seurat GetAssayData
#' @export
db_DoubletDetection <- function(srt, assay = "RNA", db_rate = ncol(srt) / 1000 * 0.01, ...) {
  if (!inherits(srt, "Seurat")) {
    stop("'srt' is not a Seurat object.")
  }
  status <- check_DataType(srt, slot = "counts", assay = assay)
  if (status != "raw_counts") {
    stop("Data type is not raw counts!")
  }
  check_Python("doubletdetection")
  doubletdetection <- import("doubletdetection")
  counts <- GetAssayData(object = srt, assay = assay, slot = "counts")
  clf <- doubletdetection$BoostClassifier(
    n_iters = as.integer(5),
    standard_scaling = TRUE,
    ...
  )
  labels <- clf$fit(Matrix::t(counts))$predict()
  scores <- clf$doublet_score()

  srt[["db.DoubletDetection_score"]] <- scores
  srt[["db.DoubletDetection_class"]] <- sapply(labels, function(i) {
    switch(as.character(i),
      "0" = "singlet",
      "1" = "doublet"
    )
  })
  return(srt)
}

#' Run doublet-calling for single cell RNA-seq data.
#'
#' Identification of heterotypic (or neotypic) doublets in single-cell RNAseq data.
#'
#' @param srt A Seurat object.
#' @param assay The name of the assay to be used for doublet-calling. Default is "RNA".
#' @param db_method Doublet-calling methods used. Can be one of \code{scDblFinder}, \code{Scrublet}, \code{DoubletDetection}, \code{scds_cxds}, \code{scds_bcds}, \code{scds_hybrid}
#' @param db_rate The expected doublet rate. By default this is assumed to be 1\% per thousand cells captured (so 4\% among 4000 thousand cells), which is appropriate for 10x datasets.
#' @param ... Arguments passed to the corresponding doublet-calling method.
#'
#' @return Returns Seurat object with the doublet prediction results and prediction scores stored in the meta.data slot.
#'
#' @examples
#' data("pancreas_sub")
#' pancreas_sub <- RunDoubletCalling(pancreas_sub, db_method = "scDblFinder")
#' CellDimPlot(pancreas_sub, reduction = "umap", group.by = "db.scDblFinder_class")
#' FeatureDimPlot(pancreas_sub, reduction = "umap", features = "db.scDblFinder_score")
#' @export
#'
RunDoubletCalling <- function(srt, assay = "RNA", db_method = "scDblFinder", db_rate = ncol(srt) / 1000 * 0.01, ...) {
  if (!inherits(srt, "Seurat")) {
    stop("'srt' is not a Seurat object.")
  }
  status <- check_DataType(srt, slot = "counts", assay = assay)
  if (status != "raw_counts") {
    stop("Data type is not raw counts!")
  }
  if (db_method %in% c("scDblFinder", "Scrublet", "DoubletDetection", "scds_cxds", "scds_bcds", "scds_hybrid")) {
    methods <- unlist(strsplit(db_method, "_"))
    method1 <- methods[1]
    method2 <- methods[2]
    if (is.na(method2)) {
      args1 <- mget(names(formals()), sys.frame(sys.nframe()))
      args2 <- as.list(match.call())
    } else {
      args1 <- c(mget(names(formals()), sys.frame(sys.nframe())), method = method2)
      args2 <- c(as.list(match.call()), method = method2)
    }
    for (n in names(args2)) {
      args1[[n]] <- args2[[n]]
    }
    args1 <- args1[!names(args1) %in% c("db_method", "...")]
    tryCatch(expr = {
      srt <- do.call(
        what = paste0("db_", method1),
        args = args1
      )
    }, error = function(e) {
      message(e)
    })
    return(srt)
  } else {
    stop(paste(db_method, "is not a suppoted doublet-calling method!"))
  }
}

#' Detect outliers using MAD(Median Absolute Deviation) method
#'
#' This function detects outliers in a numeric vector using the MAD (Median Absolute Deviation) method. It calculates the median and the MAD, and determines the boundaries for outliers based on the median and the selected number of MADs.
#'
#' @param x a numeric vector.
#' @param nmads the number of median absolute deviations (MADs) from the median to define the boundaries for outliers. The default value is 2.5.
#' @param constant a constant factor to convert the MAD to a standard deviation. The default value is 1.4826, which is consistent with the MAD of a normal distribution.
#' @param type the type of outliers to detect. Available options are "both" (default), "lower", or "higher". If set to "both", it detects both lower and higher outliers. If set to "lower", it detects only lower outliers. If set to "higher", it detects only higher outliers.
#'
#' @importFrom stats mad
#' @return A numeric vector of indices indicating the positions of outliers in \code{x}.
#' @examples
#' x <- c(1, 2, 3, 4, 5, 100)
#' isOutlier(x) # returns 6
#'
#' x <- c(3, 4, 5, NA, 6, 7)
#' isOutlier(x, nmads = 1.5, type = "lower") # returns 4
#'
#' x <- c(10, 20, NA, 15, 35)
#' isOutlier(x, nmads = 2, type = "higher") # returns 3, 5
#' @export
isOutlier <- function(x, nmads = 2.5, constant = 1.4826, type = c("both", "lower", "higher")) {
  type <- match.arg(type, c("both", "lower", "higher"))
  mad <- mad(x, constant = constant, na.rm = TRUE)
  upper <- median(x, na.rm = TRUE) + nmads * mad
  lower <- median(x, na.rm = TRUE) - nmads * mad
  if (type == "both") {
    out <- which(x > upper | x < lower)
  }
  if (type == "lower") {
    out <- which(x < lower)
  }
  if (type == "higher") {
    out <- which(x > upper)
  }
  out <- c(which(is.na(x)), out)
  return(out)
}

#' Run cell-level quality control for single cell RNA-seq data.
#'
#' This function handles multiple quality control methods for single-cell RNA-seq data.
#'
#' @inheritParams RunDoubletCalling
#' @param split.by Name of the sample variable to split the Seurat object. Default is NULL.
#' @param return_filtered Logical indicating whether to return a cell-filtered Seurat object. Default is FALSE.
#' @param qc_metrics A character vector specifying the quality control metrics to be applied. Default is
#'   `c("doublets", "outlier", "umi", "gene", "mito", "ribo", "ribo_mito_ratio", "species")`.
#' @param outlier_threshold A character vector specifying the outlier threshold. Default is
#'   `c("log10_nCount:lower:2.5", "log10_nCount:higher:5", "log10_nFeature:lower:2.5", "log10_nFeature:higher:5", "featurecount_dist:lower:2.5")`. See \link[scuttle]{isOutlier}.
#' @param db_coefficient The coefficient used to calculate the doublet rate. Default is 0.01. Doublet rate is calculated as`ncol(srt) / 1000 * db_coefficient`
#' @param outlier_n Minimum number of outlier metrics that meet the conditions for determining outlier cells. Default is 1.
#' @param UMI_threshold UMI number threshold. Cells that exceed this threshold will be considered as kept. Default is 3000.
#' @param gene_threshold Gene number threshold. Cells that exceed this threshold will be considered as kept. Default is 1000.
#' @param mito_threshold Percentage of UMI counts of mitochondrial genes. Cells that exceed this threshold will be considered as discarded. Default is 20.
#' @param mito_pattern Regex patterns to match the mitochondrial genes. Default is `c("MT-", "Mt-", "mt-")`.
#' @param mito_gene A defined mitochondrial genes. If features provided, will ignore the \code{mito_pattern} matching. Default is \code{NULL}.
#' @param ribo_threshold Percentage of UMI counts of ribosomal genes. Cells that exceed this threshold will be considered as discarded. Default is 50.
#' @param ribo_pattern Regex patterns to match the ribosomal genes. Default is `c("RP[SL]\\d+\\w{0,1}\\d*$", "Rp[sl]\\d+\\w{0,1}\\d*$", "rp[sl]\\d+\\w{0,1}\\d*$")`.
#' @param ribo_gene A defined ribosomal genes. If features provided, will ignore the \code{ribo_pattern} matching. Default is \code{NULL}.
#' @param ribo_mito_ratio_range A numeric vector specifying the range of ribosomal/mitochondrial gene expression ratios for ribo_mito_ratio outlier cells. Default is c(1, Inf).
#' @param species Species used as the suffix of the QC metrics. The first is the species of interest. Default is \code{NULL}.
#' @param species_gene_prefix Species gene prefix used to calculate QC metrics for each species. Default is \code{NULL}.
#' @param species_percent Percentage of UMI counts of the first species. Cells that exceed this threshold will be considered as kept. Default is 95.
#' @param seed Set a random seed. Default is 11.
#'
#' @return Returns Seurat object with the QC results stored in the meta.data slot.
#'
#' @examples
#' data("pancreas_sub")
#' pancreas_sub <- RunCellQC(pancreas_sub)
#' CellStatPlot(
#'   srt = pancreas_sub,
#'   stat.by = c(
#'     "db_qc", "outlier_qc", "umi_qc", "gene_qc",
#'     "mito_qc", "ribo_qc", "ribo_mito_ratio_qc", "species_qc"
#'   ),
#'   plot_type = "upset", stat_level = "Fail"
#' )
#' table(pancreas_sub$CellQC)
#'
#' data("ifnb_sub")
#' ifnb_sub <- RunCellQC(ifnb_sub, split.by = "stim", UMI_threshold = 1000, gene_threshold = 550)
#' CellStatPlot(
#'   srt = ifnb_sub,
#'   stat.by = c(
#'     "db_qc", "outlier_qc", "umi_qc", "gene_qc",
#'     "mito_qc", "ribo_qc", "ribo_mito_ratio_qc", "species_qc"
#'   ),
#'   plot_type = "upset", stat_level = "Fail"
#' )
#' table(ifnb_sub$CellQC)
#' @importFrom Seurat Assays as.SingleCellExperiment PercentageFeatureSet WhichCells SplitObject AddMetaData
#' @importFrom stats loess predict aggregate
#' @importFrom Matrix colSums t
#' @export
#'
RunCellQC <- function(srt, assay = "RNA", split.by = NULL, return_filtered = FALSE,
                      qc_metrics = c("doublets", "outlier", "umi", "gene", "mito", "ribo", "ribo_mito_ratio", "species"),
                      db_method = "scDblFinder", db_rate = NULL, db_coefficient = 0.01,
                      outlier_threshold = c(
                        "log10_nCount:lower:2.5",
                        "log10_nCount:higher:5",
                        "log10_nFeature:lower:2.5",
                        "log10_nFeature:higher:5",
                        "featurecount_dist:lower:2.5"
                      ), outlier_n = 1,
                      UMI_threshold = 3000, gene_threshold = 1000,
                      mito_threshold = 20, mito_pattern = c("MT-", "Mt-", "mt-"), mito_gene = NULL,
                      ribo_threshold = 50, ribo_pattern = c("RP[SL]\\d+\\w{0,1}\\d*$", "Rp[sl]\\d+\\w{0,1}\\d*$", "rp[sl]\\d+\\w{0,1}\\d*$"), ribo_gene = NULL,
                      ribo_mito_ratio_range = c(1, Inf),
                      species = NULL, species_gene_prefix = NULL, species_percent = 95,
                      seed = 11) {
  set.seed(seed)

  if (!inherits(srt, "Seurat")) {
    stop("'srt' is not a Seurat object.")
  }
  if (!isTRUE(assay %in% Seurat::Assays(srt))) {
    stop("srt does not contain '", assay, "' assay.")
  }
  if (length(species) != length(species_gene_prefix)) {
    stop("'species_gene_prefix' must be the same length as 'species'.")
  }
  if (length(species) == 0) {
    species <- species_gene_prefix <- NULL
  }
  status <- check_DataType(srt, slot = "counts", assay = assay)
  if (status != "raw_counts") {
    warning("Data type is not raw counts!", immediate. = TRUE)
  }
  if (!paste0("nCount_", assay) %in% colnames(srt@meta.data)) {
    srt@meta.data[[paste0("nCount_", assay)]] <- colSums(srt[[assay]]@counts)
  }
  if (!paste0("nFeature_", assay) %in% colnames(srt@meta.data)) {
    srt@meta.data[[paste0("nFeature_", assay)]] <- colSums(srt[[assay]]@counts > 0)
  }
  srt_raw <- srt
  if (!is.null(split.by)) {
    srtList <- SplitObject(srt, split.by = split.by)
  } else {
    srtList <- list(srt)
  }

  for (i in seq_along(srtList)) {
    srt <- srtList[[i]]
    if (!is.null(split.by)) {
      cat("===", srt@meta.data[[split.by]][1], "===\n")
    }
    ntotal <- ncol(srt)

    db_qc <- c()
    if ("doublets" %in% qc_metrics) {
      if (!is.null(db_method)) {
        if (is.null(db_rate)) {
          db_rate <- ncol(srt) / 1000 * db_coefficient
        }
        if (db_rate >= 1) {
          stop("The db_rate is equal to or greater than 1!")
        }
        for (dbm in db_method) {
          srt <- RunDoubletCalling(srt = srt, db_method = dbm, db_rate = db_rate)
          db_qc <- unique(c(db_qc, colnames(srt)[srt[[paste0("db.", dbm, "_class"), drop = TRUE]] == "doublet"]))
        }
      }
    }

    outlier_qc <- c()
    for (n in 1:length(species)) {
      if (n == 0) {
        break
      }
      sp <- species[n]
      prefix <- species_gene_prefix[n]
      sp_genes <- rownames(srt[[assay]])[grep(pattern = paste0("^", prefix), x = rownames(srt[[assay]]))]
      nCount <- srt[[paste0(c(paste0("nCount_", assay), sp), collapse = ".")]] <- colSums(srt[[assay]]@counts[sp_genes, ])
      nFeature <- srt[[paste0(c(paste0("nFeature_", assay), sp), collapse = ".")]] <- colSums(srt[[assay]]@counts[sp_genes, ] > 0)
      percent.mito <- srt[[paste0(c("percent.mito", sp), collapse = ".")]] <- PercentageFeatureSet(object = srt, assay = assay, pattern = paste0("(", paste0("^", prefix, "-*", mito_pattern), ")", collapse = "|"), features = mito_gene)[[1]]
      percent.ribo <- srt[[paste0(c("percent.ribo", sp), collapse = ".")]] <- PercentageFeatureSet(object = srt, assay = assay, pattern = paste0("(", paste0("^", prefix, "-*", ribo_pattern), ")", collapse = "|"), features = ribo_gene)[[1]]
      percent.genome <- srt[[paste0(c("percent.genome", sp), collapse = ".")]] <- PercentageFeatureSet(object = srt, assay = assay, pattern = paste0("^", prefix))[[1]]
      ribo.mito.ratio <- srt[[paste0(c("percent.ribo", sp), collapse = "."), drop = TRUE]] / srt[[paste0(c("percent.mito", sp), collapse = "."), drop = TRUE]]
      ribo.mito.ratio[is.na(ribo.mito.ratio)] <- 1
      srt[[paste0(c("ribo.mito.ratio", sp), collapse = ".")]] <- ribo.mito.ratio

      if (n == 1) {
        if ("outlier" %in% qc_metrics) {
          # "percent.top_20:higher:5"
          # countx <- as(srt[[assay]]@counts[sp_genes, ], "sparseMatrix")
          # agg <- aggregate(x = countx@x, by = list(rep(colnames(countx), diff(countx@p))), FUN = function(x) {
          #   sum(head(x, 20))
          # })
          # rownames(agg) <- agg[[1]]
          # percent.top_20 <- srt[[paste0(c("percent.top_20", sp), collapse = ".")]] <- agg[colnames(srt), "x"]

          log10_nFeature <- srt[[paste0(c(paste0("log10_nFeature_", assay), sp), collapse = ".")]] <- log10(nFeature)
          log10_nCount <- srt[[paste0(c(paste0("log10_nCount_", assay), sp), collapse = ".")]] <- log10(nCount)
          log10_nCount[is.infinite(log10_nCount)] <- NA
          log10_nFeature[is.infinite(log10_nFeature)] <- NA
          mod <- loess(log10_nFeature ~ log10_nCount)
          pred <- predict(mod, newdata = data.frame(log10_nCount = log10_nCount))
          featurecount_dist <- srt[[paste0(c("featurecount_dist", sp), collapse = ".")]] <- log10_nFeature - pred

          # df <- data.frame(cell = colnames(srt), ribo = srt$percent.ribo.Homo_sapiens, y = log10_nFeature, x = log10_nCount, pred = pred, featurecount_dist = featurecount_dist)
          # lower_df <- subset(df, featurecount_dist < median(df$featurecount_dist) - 2.5 * mad(df$featurecount_dist))
          # higher_df <- subset(df, featurecount_dist > median(df$featurecount_dist) + 2.5 * mad(df$featurecount_dist))
          # ggplot(df) +
          #   geom_point(aes(x = x, y = y, color = featurecount_dist)) +
          #   scale_color_gradientn(colors = c("green", "white", "orange"), values = scales::rescale(c(min(df$featurecount_dist), 0, max(df$featurecount_dist)))) +
          #   geom_point(data = lower_df, aes(x = x, y = y), shape = 21, fill = "transparent", color = "blue") +
          #   geom_point(data = higher_df, aes(x = x, y = y), shape = 21, fill = "transparent", color = "red") +
          #   geom_line(aes(x = x, y = pred), color = "black")+
          #   theme(panel.background = element_rect(fill = "grey"))
          # nrow(lower_df)

          var <- sapply(strsplit(outlier_threshold, ":"), function(x) x[[1]])
          var_valid <- var %in% colnames(srt@meta.data) | sapply(var, FUN = function(x) exists(x, where = environment()))
          if (any(!var_valid)) {
            stop("Variable ", paste0(names(var_valid)[!var_valid], collapse = ","), " is not found in the srt object.")
          }
          outlier <- lapply(strsplit(outlier_threshold, ":"), function(m) {
            colnames(srt)[isOutlier(get(m[1]), nmads = as.numeric(m[3]), type = m[2])]
          })
          names(outlier) <- outlier_threshold
          # print(unlist(lapply(outlier, length)))
          outlier_tb <- table(unlist(outlier))
          outlier_qc <- c(outlier_qc, names(outlier_tb)[outlier_tb >= outlier_n])
          for (nm in names(outlier)) {
            srt[[make.names(nm)]] <- colnames(srt) %in% outlier[[nm]]
          }
        }
      }
    }

    umi_qc <- gene_qc <- mito_qc <- ribo_qc <- ribo_mito_ratio_qc <- species_qc <- c()
    if ("umi" %in% qc_metrics) {
      umi_qc <- colnames(srt)[which(srt[[paste0(c(paste0("nCount_", assay), species[1]), collapse = "."), drop = TRUE]] < UMI_threshold)]
    }
    if ("gene" %in% qc_metrics) {
      gene_qc <- colnames(srt)[which(srt[[paste0(c(paste0("nFeature_", assay), species[1]), collapse = "."), drop = TRUE]] < gene_threshold)]
    }
    if ("mito" %in% qc_metrics) {
      mito_qc <- colnames(srt)[which(srt[[paste0(c("percent.mito", species[1]), collapse = "."), drop = TRUE]] > mito_threshold)]
    }
    if ("ribo" %in% qc_metrics) {
      ribo_qc <- colnames(srt)[which(srt[[paste0(c("percent.ribo", species[1]), collapse = "."), drop = TRUE]] > ribo_threshold)]
    }
    if ("ribo_mito_ratio" %in% qc_metrics) {
      ribo_mito_ratio_qc <- colnames(srt)[which(srt[[paste0(c("ribo.mito.ratio", species[1]), collapse = "."), drop = TRUE]] < ribo_mito_ratio_range[1] | srt[[paste0(c("ribo.mito.ratio", species[1]), collapse = "."), drop = TRUE]] > ribo_mito_ratio_range[2])]
    }
    if ("species" %in% qc_metrics) {
      species_qc <- colnames(srt)[which(srt[[paste0(c("percent.genome", species[1]), collapse = "."), drop = TRUE]] < species_percent)]
    }

    CellQC <- unique(c(db_qc, outlier_qc, umi_qc, gene_qc, mito_qc, ribo_qc, ribo_mito_ratio_qc, species_qc))
    cat(">>>", "Total cells:", ntotal, "\n")
    cat(">>>", "Cells which are filtered out:", length(CellQC), "\n")
    cat("...", length(db_qc), "potential doublets", "\n")
    cat("...", length(outlier_qc), "outlier cells", "\n")
    cat("...", length(umi_qc), "low-UMI cells", "\n")
    cat("...", length(gene_qc), "low-gene cells", "\n")
    cat("...", length(mito_qc), "high-mito cells", "\n")
    cat("...", length(ribo_qc), "high-ribo cells", "\n")
    cat("...", length(ribo_mito_ratio_qc), "ribo_mito_ratio outlier cells", "\n")
    cat("...", length(species_qc), "species-contaminated cells", "\n")
    cat(">>>", "Remained cells after filtering:", ntotal - length(CellQC), "\n")

    qc_nm <- c("db_qc", "outlier_qc", "umi_qc", "gene_qc", "mito_qc", "ribo_qc", "ribo_mito_ratio_qc", "species_qc", "CellQC")
    for (qc in qc_nm) {
      srt[[qc]] <- ifelse(colnames(srt) %in% get(qc), "Fail", "Pass")
      srt[[qc]] <- factor(srt[[qc, drop = TRUE]], levels = c("Pass", "Fail"))
    }

    if (return_filtered) {
      srt <- srt[, srt$CellQC == "Pass"]
      srt@meta.data[, intersect(qc_nm, colnames(srt@meta.data))] <- NULL
    }
    srtList[[i]] <- srt
  }
  cells <- unlist(lapply(srtList, colnames))
  srt_raw <- srt_raw[, cells]
  meta.data <- do.call(rbind.data.frame, unname(lapply(srtList, function(x) x@meta.data)))
  srt_raw <- AddMetaData(srt_raw, metadata = meta.data)
  return(srt_raw)
}