Perform Hotelling's T-squared Test on PCA Scores for Single-cell RNA-seq Data

Computes Hotelling's T-squared test statistic and p-values for each specified cell type based on PCA-projected data from query and reference datasets.

calculateHotellingPValue(
  query_data,
  reference_data,
  query_cell_type_col,
  ref_cell_type_col,
  cell_types = NULL,
  pc_subset = 1:5,
  n_permutation = 500,
  assay_name = "logcounts",
  max_cells = 2500
)

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

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

ref_cell_type_col

character. 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 to perform for p-value calculation. Default is 500.

assay_name

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

max_cells

Maximum number of cells to retain. If the object has fewer cells, it is returned unchanged. Default is 2500.

Value

A named numeric vector of p-values from Hotelling's T-squared test for each cell type.

Details

This function calculates Hotelling's T-squared statistic for comparing multivariate means between reference and query datasets, projected onto a subset of principal components (PCs). It performs a permutation test to obtain p-values for each cell type specified.

References

Hotelling, H. (1931). "The generalization of Student's ratio". *Annals of Mathematical Statistics*. 2 (3): 360–378. doi:10.1214/aoms/1177732979.

Author

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

Examples

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

# Get the p-values
p_values <- calculateHotellingPValue(query_data = query_data,
                                     reference_data = reference_data,
                                     query_cell_type_col = "SingleR_annotation",
                                     ref_cell_type_col = "expert_annotation",
                                     pc_subset = 1:10)
round(p_values, 5)
#>          CD4          CD8 B_and_plasma      Myeloid 
#>        0.000        0.000        0.244        0.364