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Calculate Maximum Mean Discrepancy P-Values for Two-Sample Comparison

Source: R/calculateMMDPValue.R
calculateMMDPValue.Rd

This function performs the Maximum Mean Discrepancy (MMD) test for comparing distributions between two samples in PCA space using a custom implementation with permutation testing for better sensitivity.

Usage

calculateMMDPValue(
  query_data,
  reference_data,
  query_cell_type_col,
  ref_cell_type_col,
  cell_types = NULL,
  pc_subset = 1:5,
  n_permutation = 100,
  kernel_type = "gaussian",
  sigma = NULL,
  assay_name = "logcounts",
  max_cells_query = 5000,
  max_cells_ref = 5000
)

Arguments

query_data

A SingleCellExperiment object containing numeric expression matrix for the query cells.

reference_data

A SingleCellExperiment object containing numeric expression matrix for the reference cells.

query_cell_type_col

The column name in the colData of query_data that identifies the cell types.

ref_cell_type_col

The column name in the colData of reference_data that identifies the cell types.

cell_types

A character vector specifying the cell types to include in the plot. If NULL, all cell types are included.

pc_subset

A numeric vector specifying which principal components to include in the plot. Default is PC1 to PC5.

n_permutation

Number of permutations for p-value calculation. Default is 100.

kernel_type

Type of kernel to use. Options are "gaussian" (default) or "linear".

sigma

Bandwidth parameter for Gaussian kernel. If NULL, uses median heuristic.

assay_name

Name of the assay on which to perform computations. Default is "logcounts".

max_cells_query

Maximum number of query cells to retain after cell type filtering. If NULL, no downsampling of query cells is performed. Default is 5000.

max_cells_ref

Maximum number of reference cells to retain after cell type filtering. If NULL, no downsampling of reference cells is performed. Default is 5000.

Value

A named vector of p-values from the MMD test for each cell type.

Details

The function performs the following steps:

  1. Projects the data into the PCA space.

  2. Subsets the data to the specified cell types and principal components.

  3. Performs a custom MMD test with permutation-based p-values for each cell type.

References

Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., & Smola, A. (2012). "A kernel two-sample test". Journal of Machine Learning Research, 13(1), 723-773.

Author

Anthony Christidis, anthony-alexander_christidis@hms.harvard.edu

Examples

# Load data
data("reference_data")
data("query_data")

# Calculate MMD p-values (with query data)
mmd_test <- calculateMMDPValue(reference_data = reference_data,
                              query_data = query_data,
                              ref_cell_type_col = "expert_annotation",
                              query_cell_type_col = "SingleR_annotation",
                              cell_types = c("CD4", "CD8"),
                              pc_subset = 1:5,
                              n_permutation = 30)
mmd_test
#>        CD4        CD8 
#> 0.03225806 0.03225806