Plot gene expression distribution from overall and cell type-specific perspective

This function generates density plots to visualize the distribution of gene expression values for a specific gene across the overall dataset and within a specified cell type.

plotMarkerExpression(
  reference_data,
  query_data,
  ref_cell_type_col,
  query_cell_type_col,
  cell_type,
  gene_name,
  assay_name = "logcounts"
)

Arguments

reference_data: A SingleCellExperiment object containing numeric expression matrix for the reference cells.
query_data: A SingleCellExperiment object containing numeric expression matrix for the query cells.
ref_cell_type_col: The column name in the colData of reference_data that identifies the cell types.
query_cell_type_col: The column name in the colData of query_data that identifies the cell types.
cell_type: A vector of cell type cell_types to plot (e.g., c("T-cell", "B-cell")).
gene_name: The gene name for which the distribution is to be visualized.
assay_name: Name of the assay on which to perform computations. Default is "logcounts".

Value

A gtable object containing two arranged density plots as grobs. The first plot shows the overall gene expression distribution, and the second plot displays the cell type-specific expression distribution.

Details

This function generates density plots to compare the distribution of a specific marker gene between reference and query datasets. The aim is to inspect the alignment of gene expression levels as a surrogate for dataset similarity. Similar distributions suggest a good alignment, while differences may indicate discrepancies or incompatibilities between the datasets. To make the gene expression scales comparable between the datasets, the gene expression values are transformed using z-rank normalization. This transformation ranks the expression values and then scales the ranks to have a mean of 0 and a standard deviation of 1, which helps in standardizing the distributions for comparison.

Author

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

Examples

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

# Note: Users can use SingleR or any other method to obtain the cell type annotations.
plotMarkerExpression(reference_data = reference_data, 
                     query_data = query_data, 
                     ref_cell_type_col = "expert_annotation", 
                     query_cell_type_col = "SingleR_annotation", 
                     gene_name = "VPREB3", 
                     cell_type = "B_and_plasma")