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Visualization of gene sets or pathway scores on dimensional reduction plot

Source: R/plotGeneSetScores.R
plotGeneSetScores.Rd

Plot gene sets or pathway scores on PCA, TSNE, or UMAP. Single cells are color-coded by scores of gene sets or pathways.

Usage

plotGeneSetScores(
  sce_object,
  cell_type_col,
  method = c("PCA", "TSNE", "UMAP"),
  score_col,
  pc_subset = 1:5,
  cell_types = NULL,
  max_cells = 2000
)

Arguments

sce_object

An object of class SingleCellExperiment containing numeric expression matrix and other metadata. It can be either a reference or query dataset.

cell_type_col

The column name in the colData of sce_object that identifies the cell types.

method

A character string indicating the method for visualization ("PCA", "TSNE", or "UMAP").

score_col

A character string representing the name of the score_col (score) in the colData(sce_object) to plot.

pc_subset

An optional vector specifying the principal components (PCs) to include in the plot if method = "PCA". Default is 1:5.

cell_types

A character vector specifying the cell types to include in the plot. If NULL, all cell types are included.

max_cells

Maximum number of cells to retain. If the object has fewer cells, it is returned unchanged. Default is 2000.

Value

A ggplot2 object representing the gene set scores plotted on the specified reduced dimensions.

Details

This function plots gene set scores on reduced dimensions such as PCA, t-SNE, or UMAP. It extracts the reduced dimensions from the provided SingleCellExperiment object. Gene set scores are visualized as a scatter plot with colors indicating the scores. For PCA, the function automatically includes the percentage of variance explained in the plot's legend.

Author

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

Examples

# Load data
data("query_data")

# Plot gene set scores on PCA
plotGeneSetScores(sce_object = query_data,
                  method = "PCA",
                  score_col = "gene_set_scores",
                  pc_subset = 1:5,
                  cell_types = "CD8",
                  cell_type_col = "SingleR_annotation")