Ridgeline Plot of Pairwise Distance Analysis

This function calculates pairwise distances or correlations between query and reference cells of a specified cell type and visualizes the results using ridgeline plots, displaying the density distribution for each comparison.

plotPairwiseDistancesDensity(
  query_data,
  reference_data,
  query_cell_type_col,
  ref_cell_type_col,
  cell_type_query,
  cell_type_ref,
  pc_subset = 1:5,
  distance_metric = c("correlation", "euclidean"),
  correlation_method = c("spearman", "pearson"),
  assay_name = "logcounts"
)

Arguments

query_data

A SingleCellExperiment containing the single-cell expression data and metadata.

reference_data

A SingleCellExperiment object containing the single-cell expression data and metadata.

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_type_query

The query cell type for which distances or correlations are calculated.

cell_type_ref

The reference cell type for which distances or correlations are calculated.

pc_subset

A numeric vector specifying which principal components to use in the analysis. Default is 1:5. If set to NULL, the assay data is used directly for computations without dimensionality reduction.

distance_metric

The distance metric to use for calculating pairwise distances, such as euclidean, manhattan, etc. Set to "correlation" to calculate correlation coefficients.

correlation_method

The correlation method to use when distance_metric is "correlation". Possible values are "pearson" and "spearman".

assay_name

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

Value

A ggplot2 object showing ridgeline plots of calculated distances or correlations.

Details

Designed for SingleCellExperiment objects, this function subsets data for specified cell types, computes pairwise distances or correlations, and visualizes these measurements through ridgeline plots. The plots help evaluate the consistency and differentiation of annotated cell types within single-cell datasets.

See also

calculateWassersteinDistance

Examples

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

# Example usage of the function
plotPairwiseDistancesDensity(query_data = query_data,
                             reference_data = reference_data,
                             query_cell_type_col = "SingleR_annotation",
                             ref_cell_type_col = "expert_annotation",
                             cell_type_query = "CD8",
                             cell_type_ref = "CD8",
                             pc_subset = 1:5,
                             distance_metric = "euclidean",
                             correlation_method = "pearson")