Plot Principal Components for Different Cell Types — plotCellTypePCA • scDiagnostics

This function plots the principal components for different cell types in the query and reference datasets.

plotCellTypePCA(
  query_data,
  reference_data,
  query_cell_type_col,
  ref_cell_type_col,
  cell_types = NULL,
  pc_subset = 1:5,
  assay_name = "logcounts"
)

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 1:5.
assay_name: Name of the assay on which to perform computations. Default is "logcounts".

Value

A ggplot object representing the boxplots of specified principal components for the given cell types and datasets.

Details

This function projects the query dataset onto the principal component space of the reference dataset and then plots the specified principal components for the specified cell types. It uses the `projectPCA` function to perform the projection and ggplot2 to create the plots.

Author

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

Examples

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

# Plot the PC data
pc_plot <- plotCellTypePCA(query_data = query_data, 
                           reference_data = reference_data,
                           cell_types = c("CD4", "CD8", "B_and_plasma", "Myeloid"),
                           query_cell_type_col = "expert_annotation", 
                           ref_cell_type_col = "expert_annotation", 
                           pc_subset = 1:5)
pc_plot