R/plotMarkerExpression.R
plotMarkerExpression.Rd
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(
query_data,
reference_data,
ref_cell_type_col,
query_cell_type_col,
cell_type,
gene_name,
assay_name = "logcounts",
normalization = c("z_score", "min_max", "rank", "none"),
max_cells_query = NULL,
max_cells_ref = NULL
)
A SingleCellExperiment
object containing numeric expression matrix for the query cells.
A SingleCellExperiment
object containing numeric expression matrix for the reference cells.
The column name in the colData
of reference_data
that identifies the cell types.
The column name in the colData
of query_data
that identifies the cell types.
A cell type to plot (e.g., c("T-cell", "B-cell")).
The gene name for which the distribution is to be visualized.
Name of the assay on which to perform computations. Default is "logcounts".
Method for normalizing expression values. Options: "z_score" (default), "min_max", "rank", "none".
Maximum number of query cells to retain after cell type filtering. If NULL, no downsampling of query cells is performed. Default is NULL.
Maximum number of reference cells to retain after cell type filtering. If NULL, no downsampling of reference cells is performed. Default is NULL.
A ggplot object containing density plots comparing reference and query distributions.
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.
Multiple normalization options are available: - "z_score": Standard z-score normalization within each dataset - "min_max": Min-max scaling to [0,1] range within each dataset - "rank": Maps values to quantile ranks (0-100 scale) - "none": No transformation (preserves original scale differences)
# 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 = c("expert_annotation", "SingleR_annotation")[1],
gene_name = "CD8A",
cell_type = "CD4",
normalization = "z_score")