Function to visualize differential TF regulon activity between two groups of cells based on regulon scores. The plot shows the average log fold-change (logFC) for positive and negative regulon scores in a scatter plot, with the points colored by their associated module and optionally labeled with the most differentially active regulons.
Usage
PlotDifferentialRegulons(
seurat_obj,
dregs,
n_label = 10,
logfc_thresh = 0.1,
lm = TRUE,
wgcna_name = NULL
)Arguments
- seurat_obj
A Seurat object that contains the WGCNA experiment and TF regulon scores.
- dregs
Dataframe output from the
FindDifferentialRegulonsfunction containing differential regulon results.- n_label
Integer specifying the number of top up- and down-regulated regulons to label on the plot. If 'all', all significant regulons will be labeled. Default = 10.
- logfc_thresh
Numeric threshold for labeling and annotating regulons based on logFC values. Default = 0.1.
- lm
Logical indicating whether to plot a linear regression line for the logFC values of positive vs. negative regulons. Default = TRUE.
- wgcna_name
The name of the WGCNA experiment in the
seurat_obj@miscslot. Default is the active WGCNA experiment.
Details
This function generates a scatter plot where each point represents a TF regulon, with the x-axis showing the average log fold-change for positive regulon scores and the y-axis showing the average log fold-change for negative regulon scores. Points are colored by their module assignment, and the size of the points reflects the module eigengene correlation (kME) value of the TF.
Differentially expressed regulons can be highlighted, and the most significantly up- and down-regulated regulons can be labeled. Additional options allow adding a linear regression line and controlling label density based on logFC thresholds.