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

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**DESCRIPTION:** Core anchor finding algorithm implementing reciprocal PCA approach with distance-weighted scoring and semi-supervised filtering. Central to STACAS methodology for identifying biologically equivalent cells across batches.

**KEY TERMS:** {
  "dims": "principal component dimensions",
  "alpha": "rPCA distance weighting parameter",
  "anchor.coverage": "anchor filtering threshold",
  "correction.scale": "batch correction scaling factor",
  "label.confidence": "cell type label confidence level"
}

**CONTEXT CODE:** #' @param assay A vector containing the assay to use for each Seurat object in object.list.
#' If not specified, uses the default assay.
#' @param reference A vector specifying the object/s to be used as a reference
#' during integration. If NULL (default), all pairwise anchors are found (no
#' reference/s). If not NULL, the corresponding objects in \code{object.list}
#' will be used as references. When using a set of specified references, anchors
#' are first found between each query and each reference. The references are
#' then integrated through pairwise integration. Each query is then mapped to
#' the integrated reference.
#' @param min.sample.size Minimum number of cells per sample. Objects with fewer
#' than this number of cells are not integrated.
#' @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 anchor.features Can be either: \itemize{
#'   \item{A numeric value. This will call \code{FindVariableFeatures.STACAS} to identify \code{anchor.features}
#'       that are consistently variable across datasets}
#'   \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 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). Note that not all cells need to be annotated - please set
#' unannotated cells as NA or 'unknown' for this column. Cells with NA or 'unknown' cell labels 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 seed Random seed for probabilistic anchor acceptance
#' @param verbose Print all output
#' @return Returns an AnchorSet object, which can be passed to \code{IntegrateData.STACAS}
#'
#' @examples
#' data(sampleObj)
#' library(Seurat)
#' obj.list <- SplitObject(sampleObj, split.by="donor")
#' anchors <- FindAnchors.STACAS(obj.list, min.sample.size=10, k.score=5, dims=3)
#' @import Seurat
#' @import SeuratObject
#' @importFrom pbapply pblapply
#' @export

FindAnchors.STACAS <- function (
  object.list = NULL,
  assay = NULL,
  reference = NULL,
  min.sample.size = 100,
  max.seed.objects = 10,
  anchor.features = 1000,
  genesBlockList = "default",
  dims = 30,
  k.anchor = 5,
  k.score = 30,
  alpha=0.8,
  anchor.coverage = 0.5,
  correction.scale = 2,
  cell.labels = NULL,
  label.confidence = 1,
  scale.data = FALSE,
  seed = 123,
  verbose = TRUE
) {
  stop("Function implementation has been masked")
  # Basic checks
  if (is.null(object.list) || !is.list(object.list) || length(object.list) < 2) {
    stop("object.list must be a list of at least two Seurat objects.")
  }
  if (!requireNamespace("Seurat", quietly = TRUE)) {
    stop("Package 'Seurat' is required but not installed.")
  }
  # preserve original names or create them
  orig_names <- names(object.list)
  if (is.null(orig_names)) {
    orig_names <- as.character(seq_along(object.list))
    names(object.list) <- orig_names
  }
  # Determine assay vector
  if (is.null(assay)) {
    assay_vec <- vapply(object.list, Seurat::DefaultAssay, FUN.VALUE = character(1))
  } else if (length(assay) == 1) {
    assay_vec <- rep(assay, length(object.list))
  } else {
    if (length(assay) != length(object.list)) {
      stop("If 'assay' is provided as a vector, its length must match object.list.")
    }
    assay_vec <- assay
  }
  # Filter objects by min.sample.size
  cell_counts <- vapply(object.list, function(x) {
    # try ncol, fallback to number of cells in meta.data
    nc <- tryCatch(ncol(x), error = function(e) NA_integer_)
    if (is.na(nc)) {
      if (!is.null(x@meta.data)) {
        nc <- nrow(x@meta.data)
      } else {
        nc <- 0L
      }
    }
    as.integer(nc)
  }, integer(1))
  keep_idx <- which(cell_counts >= min.sample.size)
  if (length(keep_idx) < 2) {
    stop("Less than two datasets remain after filtering by min.sample.size.")
  }
  if (length(keep_idx) < length(object.list) && verbose) {
    message(sprintf("Dropping %d dataset(s) with fewer than %d cells.",
                    length(object.list) - length(keep_idx), min.sample.size))
  }
  object.list.filtered <- object.list[keep_idx]
  assay_vec.filtered <- assay_vec[keep_idx]
  names(object.list.filtered) <- orig_names[keep_idx]
  # Process reference argument: map to indices in filtered list if provided
  reference_arg <- NULL
  if (!is.null(reference)) {
    # If numeric, map to original names then to filtered indices
    if (is.numeric(reference)) {
      ref_names <- orig_names[reference]
      reference_arg <- which(names(object.list.filtered) %in% ref_names)
    } else if (is.character(reference)) {
      reference_arg <- which(names(object.list.filtered) %in% reference)
    } else {
      warning("reference format not recognized; ignoring and using NULL (all pairwise anchors).")
      reference_arg <- NULL
    }
    if (length(reference_arg) == 0) {
      warning("No requested reference datasets remain after filtering; proceeding with reference = NULL.")
      reference_arg <- NULL
    }
  }
  # Determine integration features
  if (is.numeric(anchor.features) && length(anchor.features) == 1) {
    # Use Seurat::SelectIntegrationFeatures if available
    if (isTRUE(verbose)) message("Selecting integration features...")
    integration.features <- tryCatch({
      Seurat::SelectIntegrationFeatures(object.list = object.list.filtered, nfeatures = anchor.features)
    }, error = function(e) {
      # fallback: call FindVariableFeatures on each and take intersection/union heuristic
      if (isTRUE(verbose)) message("SelectIntegrationFeatures not available or failed; falling back to per-object variable features.")
      vf_list <- lapply(seq_along(object.list.filtered), function(i) {
        obj <- object.list.filtered[[i]]
        as.character(tryCatch({
          Seurat::FindVariableFeatures(obj, assay = assay_vec.filtered[i], nfeatures = anchor.features, verbose = FALSE)


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