Run hitype_assign for a Seurat object

Usage

RunHitype(object, ...)

# Default S3 method
RunHitype(object, ...)

# S3 method for class 'Seurat'
RunHitype(
  object,
  gs = NULL,
  fallback = "Unknown",
  threshold = NULL,
  level_weights = function(l) 1/(10^(l - 1)),
  make_unique = FALSE,
  norm = "sqrt",
  use_sensitivity = TRUE,
  layer = "data",
  assay = NULL,
  scaled = FALSE,
  ident = NULL,
  ...
)

Arguments

object: Seurat object
...: Additional arguments passed to the specific method.
gs: The gene list prepared by gs_prepare
fallback: A fallback cell type if no cell type is assigned
threshold: Confidence threshold as top1/top2 score ratio, passed to hitype_assign(). NULL (default) means no filtering.
level_weights: The weights for each level of the hierarchy to calculate the final cell type score It should be either a numeric vector of length equal to the number of levels or a single numeric value to be used for all levels It can also be a function that takes the levels as input and returns a numeric vectors as the weights.
make_unique: Whether to make the cell type names unique
norm: The normalization method for hitype_score, passed through as-is. One of "sqrt", "weight", "none". "weight" is recommended when scoring with learned weights.
use_sensitivity: Whether to weight markers by their sensitivity in hitype_score. FALSE is recommended when scoring with learned weights.
layer: The layer to use for GetAssayData
assay: The assay to use for GetAssayData
scaled: Whether the data from GetAssayData is scaled
ident: The identity column to use majority voting to assign cell types to clusters If NULL, return cell-level assignments for each cell. If "ident", return cluster(identity)-level assignments for each cluster. if a character, return cluster-level assignments for each cluster based on the specified column in the metadata.

Value

The Seurat object with the cell types (named hitype) added to the metadata