Scatter plot: QC stats vs Cell Type Annotation Scores

Creates a scatter plot to visualize the relationship between QC stats (e.g., library size) and cell type annotation scores for one or more cell types.

plotQCvsAnnotation(
  se_object,
  cell_type_col,
  cell_types = NULL,
  qc_col,
  score_col
)

Arguments

se_object

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

cell_type_col

The column name in the colData of se_object that contains the cell type labels.

cell_types

A vector of cell type labels to plot (e.g., c("T-cell", "B-cell")). Defaults to NULL, which will include all the cells.

qc_col

A column name in the colData of se_object that contains the QC stats of interest.

score_col

The column name in the colData of se_object that contains the cell type annotation scores.

Value

A ggplot object displaying a scatter plot of QC stats vs annotation scores, where each point represents a cell, color-coded by its cell type.

Details

This function generates a scatter plot to explore the relationship between various quality control (QC) statistics, such as library size and mitochondrial percentage, and cell type annotation scores. By examining these relationships, users can assess whether specific QC metrics, systematically influence the confidence in cell type annotations, which is essential for ensuring reliable cell type annotation.

Examples

# Load data
data("qc_data")

p1 <- plotQCvsAnnotation(se_object = qc_data,
                         cell_type_col = "SingleR_annotation",
                         cell_types = NULL,
                         qc_col = "total",
                         score_col = "annotation_scores")
p1 + ggplot2::xlab("Library Size")