Compute Wasserstein Distance Distributions Between Query and Reference Datasets

This function calculates distributions of Wasserstein distances between reference-reference pairs and reference-query pairs for each specified cell type, after projecting them into a shared PCA space. It then computes the probability of superiority to assess whether reference-query distances tend to be larger than reference-reference distances.

This function generates ridge plots comparing reference-reference and reference-query Wasserstein distance distributions for each cell type, along with the probability of superiority metric.

calculateWassersteinDistance(
  query_data,
  reference_data,
  ref_cell_type_col,
  query_cell_type_col,
  cell_types = NULL,
  pc_subset = 1:10,
  n_resamples = 300,
  assay_name = "logcounts"
)

# S3 method for class 'calculateWassersteinDistanceObject'
plot(x, cell_types = NULL, ...)

Arguments

query_data: A SingleCellExperiment object containing a numeric expression matrix for the query cells.
reference_data: A SingleCellExperiment object with a numeric expression matrix for the reference cells.
ref_cell_type_col: The column name in the colData of reference_data that identifies cell types.
query_cell_type_col: The column name in the colData of query_data that identifies cell types.
cell_types: A character vector specifying which cell types to plot. If NULL, all cell types are plotted.
pc_subset: A numeric vector specifying which principal components to use. Default is 1:10.
n_resamples: An integer specifying the number of resamples to generate each distribution. Default is 300.
assay_name: The name of the assay to use for computations. Default is "logcounts".
x: A list object containing the Wasserstein distance results from the calculateWassersteinDistance function.
...: Additional arguments for future extensions.

Value

A list with the following components:

ref_ref_dist: A named list of numeric vectors containing Wasserstein distances computed from resampled pairs within the reference dataset for each cell type.
ref_query_dist: A named list of numeric vectors containing Wasserstein distances between reference and query datasets for each cell type.
probability_superiority: A named numeric vector showing the probability that ref-query distances are larger than ref-ref distances for each cell type.
cell_types: A character vector containing the cell types analyzed.

A ggplot2 object representing the comparison of Wasserstein distance distributions.

Details

The function projects the query dataset onto the PCA space defined by the reference dataset. For each cell type, it computes two distributions: (1) Wasserstein distances between randomly sampled pairs within the reference dataset (null distribution), and (2) Wasserstein distances between reference and query dataset samples. It then calculates the probability of superiority, which represents the probability that a randomly selected ref-query distance is larger than a randomly selected ref-ref distance.

The function creates faceted ridge plots showing two clearly separated density curves for each cell type: one for the reference-reference distribution (null) and one for the reference-query distribution. The probability of superiority is displayed for each comparison, representing the probability that a ref-query distance exceeds a ref-ref distance.

References

Schuhmacher, D., Bernhard, S., & Book, M. (2019). "A Review of Approximate Transport in Machine Learning". In Journal of Machine Learning Research (Vol. 20, No. 117, pp. 1-61).

Author

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

Examples

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

# Compute Wasserstein distance distributions for all cell types
wasserstein_data <- calculateWassersteinDistance(query_data = query_data,
                                                 reference_data = reference_data,
                                                 query_cell_type_col = "expert_annotation",
                                                 ref_cell_type_col = "expert_annotation",
                                                 pc_subset = 1:10,
                                                 n_resamples = 100)
plot(wasserstein_data)
#> Picking joint bandwidth of 0.0143
#> Picking joint bandwidth of 0.0339
#> Picking joint bandwidth of 0.0444
#> Picking joint bandwidth of 0.0677