Make Milo Neighborhoods from Seurat Object

Constructs Milo neighborhoods from a Seurat object for differential abundance analysis.

Usage

make_nhoods(
  seu,
  assay = "RNA",
  k = 30,
  d = 30,
  prop = 0.1,
  variable = NULL,
  samples = NULL,
  covariate = NULL,
  plot = TRUE
)

Arguments

seu: A Seurat object containing single-cell RNA-seq data.
assay: Character string specifying the assay to use from the Seurat object. Default is "RNA".
k: Integer specifying the number of nearest neighbors to compute in the k-NN graph. Default is 30.
d: Integer specifying the number of dimensions to use from the PCA reduction. Default is 30.
prop: Numeric value specifying the proportion of cells to include in each neighborhood. Default is 0.1.
variable: Character string specifying the column in metadata by which to measure differential abundance.
samples: Character string specifying the column in metadata representing samples or a primary covariate.
covariate: Character string specifying the column in metadata representing batch or a secondary covariate.
plot: Logical indicating whether to plot the neighborhood size histogram. Default is TRUE.

Value

A list containing:

milo: A Milo object with neighborhoods constructed.
design: A data frame representing the design matrix for differential testing.

Details

This function converts a Seurat object into a SingleCellExperiment object, constructs Milo neighborhoods, counts cells within neighborhoods, and prepares a design matrix for differential abundance testing.

Examples

# Example usage:
# result <- make_nhoods(seu, variable="condition", samples="sample_id", covariate="batch")