Apply Chi-squared normalization to a Seurat object — NormalizeChiSquared • scSLIDE

This function applies Chi-squared normalization to a (sample-level) Seurat object. It calculates the expected density (count of cells) for each landmark-sample pair assuming random distribution, then normalizes observed counts using: (observed - expected) / √(expected). The resulting values show how much each landmark's density deviates from the expected baseline—density values. Positive values indicate higher-than-expected density, negative values indicate lower-than-expected expression.

Usage

NormalizeChiSquared(object, ...)

# Default S3 method
NormalizeChiSquared(object = NULL, verbose = TRUE, ...)

# S3 method for class 'Assay'
NormalizeChiSquared(object, verbose = TRUE, ...)

# S3 method for class 'StdAssay'
NormalizeChiSquared(
  object,
  layer = "counts",
  save = "data",
  verbose = TRUE,
  ...
)

# S3 method for class 'Seurat'
NormalizeChiSquared(object, assay = NULL, verbose = TRUE, ...)

Arguments

object: a count matrix (a landmark-sample density matrix), Assay, StdAssay, or Seurat object
...: Additional arguments
verbose: display progress bar for normalization procedure
layer: Layer to use (for StdAssay objects)
save: Layer to save results to (for StdAssay objects)
assay: Name of assay to use (for Seurat objects)

Value

Normalized object of the same class as input

Examples

if (FALSE) { # \dontrun{
# Normalize a sample-level Seurat object
sample_obj <- NormalizeChiSquared(sample_obj)
} # }