Calculate Cramer Test P-Values for Two-Sample Comparison of Multivariate ECDFs

This function performs the Cramer test for comparing multivariate empirical cumulative distribution functions (ECDFs) between two samples.

calculateCramerPValue(
  query_data,
  reference_data,
  query_cell_type_col,
  ref_cell_type_col,
  cell_types = NULL,
  pc_subset = 1:5,
  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.

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 Cramer 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 the Cramer test for each cell type using the cramer.test function in the cramer package.

References

Baringhaus, L., & Franz, C. (2004). "On a new multivariate two-sample test". Journal of Multivariate Analysis, 88(1), 190-206.

Examples

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

# Plot the PC data (with query data)
cramer_test <- calculateCramerPValue(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", "B_and_plasma", "Myeloid"),
                                     pc_subset = 1:5)
cramer_test
#> B_and_plasma          CD4          CD8      Myeloid 
#>   0.11388611   0.08891109   0.00000000   0.92007992