Rank markers for clusters, cell subsets, or individual cells

Identifies and ranks positive and negative marker genes for a specified set of cells, which may correspond to clusters, sub-clusters, arbitrary cell subsets, or even single cells. Marker ranking is based on expression-weighted centered scaled ranks (EWCSR), with Gini-based specificity assessment, and noise suppression.

Usage

markoCell(
  data,
  assay = "RNA",
  layer = "counts",
  norm_assay = "RNA",
  norm_layer = "data",
  cluster_labels = NULL,
  desired_clusters = NULL,
  desired_cells = NULL,
  log1p = TRUE,
  remove_quiescent_cells = TRUE,
  high_quantile = 0.25,
  low_quantile = 0.25,
  subset_to_HVG = FALSE,
  hvg_selection.method = c("vst", "mean.var.plot", "dispersion"),
  hvg_var_thresh = 1,
  gini_thresh = 0.5,
  noise_feature_thresh = 4,
  random_marker_thresh = 5,
  num_threads = -1,
  seed = 9999,
  verbose = TRUE
)

Arguments

data: Either a Seurat object or a numeric matrix with features (genes) as rows and cells as columns. Recommended to provide at least library-size normalized data when subset_to_HVG = TRUE.
assay: Character; assay used for marker ranking.
layer: Character; assay layer used for marker ranking. May be normalized.
norm_assay: Character; assay containing a normalized layer used for HVG detection.
norm_layer: Character; normalized layer used for HVG detection.
cluster_labels: Optional; column name in data@meta.data containing cluster labels, or a character vector of cluster labels with length equal to the number of cells in data. Required if desired_clusters is specified.
desired_clusters: Optional; character vector of cluster labels for which markers are ranked. Required if desired_cells is not specified.
desired_cells: Optional; named list of character vectors specifying cell names for each subset. Required if desired_clusters is not specified.
log1p: Logical; whether to apply log1p transformation to the input data. It is recommended to set this argument to TRUE (default) if the data is not already on a log scale.
remove_quiescent_cells: Logical; whether to remove quiescent cells prior to marker ranking.
high_quantile: Numeric; quantile threshold defining highly positive EWCSR values.
low_quantile: Numeric; quantile threshold defining highly negative EWCSR values.
subset_to_HVG: Logical; whether to restrict analysis to highly variable genes (HVGs).
hvg_selection.method: Character; HVG selection strategy. One of "vst", "mean.var.plot", or "dispersion".
hvg_var_thresh: Numeric; variance threshold for selecting HVGs.
gini_thresh: Numeric; Gini coefficient threshold for detecting non-specific markers.
noise_feature_thresh: Integer; features expressed in fewer than this number of cells are considered noise.
random_marker_thresh: Integer; markers detected in fewer than this number of cells are discarded.
num_threads: Integer; number of threads to use. Default -1 uses all available cores.
seed: Integer; random seed for reproducibility.
verbose: Logical; whether to display progress messages.

Value

An object of class "MarkoCell" containing ranked marker tables and associated statistics for each requested cell set.

Details

When subset_to_HVG = TRUE, highly variable genes are detected using the normalized assay and layer specified by norm_assay and norm_layer. Gini-based filtering is applied to identify global (non-specific) versus specific markers.

Examples

if (FALSE) { # \dontrun{
mc <- markoCell(
  data = seurat_obj,
  cluster_labels = "seurat_clusters",
  desired_clusters = c("0", "1")
)
} # }