Project: s41467-024-45240-z | Function: Run.STACAS (r)

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**DESCRIPTION:** Main STACAS algorithm orchestrating semi-supervised integration of single-cell transcriptomics data. Coordinates anchor finding, sample tree construction, and data integration with optional cell type guidance.

**KEY TERMS:** {
  "anchor.features": "integration features",
  "k.anchor": "number of neighbors for anchor finding",
  "k.score": "number of neighbors for scoring",
  "alpha": "weighting parameter for anchor scores",
  "cell.labels": "prior cell type information"
}

**CONTEXT CODE:** #' then integrated through pairwise integration. Each query is then mapped to
#' the integrated reference.
#' @param max.seed.objects Number of objects to use as seeds to
#' build the integration tree. Automatically chooses the largest max.seed.objects datasets;
#' the remaining datasets will be added sequentially to the reference.
#' @param min.sample.size Minimum number of cells per sample. Objects with fewer
#' than this number of cells are not integrated.
#' @param anchor.features Can be either: \itemize{
#'   \item{A numeric value. This will call \code{Seurat::SelectIntegrationFeatures} to identify \code{anchor.features}
#'       genes for anchor finding.}
#'   \item{A pre-calculated vector of integration features to be used for anchor search.}}
#' @param genesBlockList  If \code{anchor.features} is numeric, \code{genesBlockList} optionally takes a list of vectors of
#'     gene names. These genes will be removed from the integration features. If set to "default",
#'     STACAS uses its internal list \code{data("genes.blocklist")}.
#'     This is useful to mitigate effect of genes associated with technical artifacts or batch effects
#'     (e.g. mitochondrial, heat-shock response). 
#' @param dims The number of dimensions used for PCA reduction
#' @param k.anchor The number of neighbors to use for identifying anchors
#' @param k.score The number of neighbors to use for scoring anchors
#' @param k.weight Number of neighbors for local anchor weighting. Set \code{k.weight="max"} to disable local weighting
#' @param alpha Weight on rPCA distance for rescoring (between 0 and 1).
#' @param anchor.coverage Center of logistic function, based on quantile value of rPCA distance distribution
#' @param correction.scale Scale factor for logistic function (multiplied by SD of rPCA distance distribution)
#' @param cell.labels A metadata column name, storing cell type annotations. These will be taken into account
#' for semi-supervised alignment (optional). Cells annotated as NA or NULL will not be penalized in semi-supervised
#' alignment
#' @param label.confidence How much you trust the provided cell labels (from 0 to 1).
#' @param scale.data Whether to rescale expression data before PCA reduction.
#' @param hclust.method Clustering method for integration tree (single, complete, average, ward) 
#' @param seed Random seed for probabilistic anchor acceptance
#' @param verbose Print all output
#' @return Returns a \code{Seurat} object with a new integrated Assay. Also, centered, scaled variable features data are returned in the scale.data slot, and the pca of these batch-corrected scale data in the pca `reduction` slot 
#'
#' @examples
#' data(sampleObj)
#' library(Seurat)
#' obj.list <- SplitObject(sampleObj, split.by="donor")
#' integrated <- Run.STACAS(obj.list, min.sample.size=10, k.score=5, dims=3)
#' @import Seurat
#' @import SeuratObject
#' @export

#' @importFrom SeuratObject Assays DefaultAssay
  #' @importFrom Seurat FindIntegrationAnchors IntegrateData SplitObject TransferData
  #' @importFrom SeuratObject CountDepth NormalizeData FindVariableFeatures ScaleData RunPCA
  #' @importFrom Matrix t
  #' @importFrom stats median sd quantile hclust
  #' @importFrom utils data
  #' @importFrom methods is

Run.STACAS <- function (
    object.list = NULL,
    assay = NULL,
    new.assay.name = "integrated",
    reference = NULL,
    max.seed.objects = 10,
    min.sample.size = 100,
    anchor.features = 1000,
    genesBlockList = "default",
    dims = 30,
    k.anchor = 5,
    k.score = 30,
    k.weight = 100,
    alpha = 0.8,
    anchor.coverage = 0.5,
    correction.scale = 2,
    cell.labels = NULL,
    label.confidence = 1,
    scale.data = FALSE,
    hclust.method = c("single", "complete", "ward.D2", "average"),
    seed = 123,
    verbose = FALSE
) {
  stop("Function implementation has been masked")
  # Basic input validation ----------------------------------------------------
  if (is.null(object.list)) {
    stop("object.list must be provided and must be a list of objects.")
  }
  if (!is.list(object.list)) {
    stop("object.list must be a list.")
  }
  if (length(object.list) < 2L) {
    stop("object.list must contain at least two objects for integration.")
  }

  # Match clustering method argument
  hclust.method <- match.arg(hclust.method)

  # Normalize and validate integer-like arguments
  to_int <- function(x, name) {
    if (!is.numeric(x) || length(x) != 1 || is.na(x)) {
      stop(sprintf("%s must be a single numeric value.", name))
    }
    xi <- as.integer(x)
    if (xi < 1L) stop(sprintf("%s must be >= 1.", name))
    xi
  }
  max.seed.objects <- to_int(max.seed.objects, "max.seed.objects")
  min.sample.size <- to_int(min.sample.size, "min.sample.size")
  dims <- to_int(dims, "dims")
  k.anchor <- to_int(k.anchor, "k.anchor")
  k.score <- to_int(k.score, "k.score")
  k.weight <- to_int(k.weight, "k.weight")

  # Numeric parameter ranges
  if (!is.numeric(alpha) || alpha < 0 || alpha > 1) stop("alpha must be between 0 and 1.")
  if (!is.numeric(anchor.coverage) || anchor.coverage < 0 || anchor.coverage > 1)
    stop("anchor.coverage must be between 0 and 1.")
  if (!is.numeric(label.confidence) || label.confidence < 0 || label.confidence > 1)
    stop("label.confidence must be between 0 and 1.")
  if (!is.numeric(correction.scale) || length(correction.scale) != 1 || correction.scale <= 0)
    stop("correction.scale must be a positive number.")
  if (!is.logical(scale.data) || length(scale.data) != 1) stop("scale.data must be a single logical value.")
  if (!is.logical(verbose) || length(verbose) != 1) stop("verbose must be a single logical value.")

  # Handle seed
  if (!is.null(seed)) {
    if (!is.numeric(seed) || length(seed) != 1) stop("seed must be a single numeric value or NULL.")
    set.seed(as.integer(seed))
  }

  # Names for object.list
  obj_names <- names(object.list)
  if (is.null(obj_names) || any(obj_names == "")) {
    obj_names <- paste0("Sample", seq_along(object.list))
    names(object.list) <- obj_names
  }

  # Determine number of cells per object robustly ------------------------------
  get_ncells <- function(obj) {
    # Try common patterns without importing packages
    n <- NA_integer_
    try({
      # Seurat objects: try meta.data slot
      if (isS4(obj) && "meta.data" %in% slotNames(obj)) {
        md <- try(slot(obj, "meta.data"), silent = TRUE)
        if (!inherits(md, "try-error") && !is.null(md)) {
          n <- as.integer(NROW(md))
        }
      }
    }, silent = TRUE)
    if (is.na(n)) {
      # matrices / dgCMatrix
      if (is.matrix(obj) || inherits(obj, "dgCMatrix")) {
        n <- as.integer(ncol(obj))
      } else if (is.data.frame(obj)) {
        n <- as.integer(ncol(obj))
      } else {
        # fallback: try colnames or length of first dimension
        cl <- try(colnames(obj), silent = TRUE)


Complete the function implementation replacing "stop("Not implemented")".