Visualize DEGs

Visualize bulk differentially expressed genes

Usage

VizBulkDEGs(
  degs,
  plot_type = c("volcano", "violin", "box", "bar", "ridge", "heatmap", "cor"),
  order_by = "desc(abs(log2FC))",
  genes = 10,
  outprefix = NULL,
  devpars = list(res = 100),
  more_formats = c(),
  save_code = FALSE,
  show_row_names = TRUE,
  show_column_names = TRUE,
  ...
)

Arguments

degs

DEGs from RunDEGAnalysis

plot_type

Type of plot to generate One of 'volcano', 'violin', 'box', 'bar', 'ridge', 'dim', 'heatmap', 'dot'

order_by

An expression in string to order the genes

genes

Number of genes genes to visualize (based on the 'order_by' expression) Or an expression in string to filter the genes (passed by dplyr::filter) Only works when plot_type is not a volcano plot

outprefix

Prefix of the output file

devpars

List of parameters to save the plot

more_formats

Additional formats to save the plot in addition to 'png'

save_code

Whether to save the code to reproduce the plot

show_row_names

Whether to show row names in the heatmap

show_column_names

Whether to show column names in the heatmap

...

Additional arguments to pass to the plot function

• For 'volcano', additional arguments to pass to 'scplotter::VolcanoPlot'

• For 'violin', 'box', 'bar', 'ridge', 'dim', 'heatmap', 'dot', additional arguments to pass to 'scplotter::FeatureStatPlot'

Value

A ggplot object if 'outprefix' is NULL, otherwise, save the plot to the output directory

Examples

# \donttest{
set.seed(8525)
data = matrix(rnbinom(1000, mu = 5, size = 1), nrow = 100, ncol = 10)
rownames(data) <- paste0("Gene", 1:100)
colnames(data) <- paste0("Sample", 1:10)
meta <- data.frame(
   Sample = colnames(data),
   Condition = rep(c("Control", "Treatment"), each = 5)
)

degs <- RunDEGAnalysis(data, meta = meta,
 group_by = "Condition", ident_1 = "Treatment", tool = "edger")
#> INFO    [2025-08-12 23:11:10] Running differential gene expression analysis using edgeR...
VizBulkDEGs(degs, plot_type = "volcano", legend.position = "top", y_cutoff = 0.25)

VizBulkDEGs(degs, plot_type = "violin", genes = 2)

VizBulkDEGs(degs, plot_type = "violin", stack = TRUE, genes = 2)

VizBulkDEGs(degs, plot_type = "box", genes = 2)

VizBulkDEGs(degs, plot_type = "bar", genes = 2, x_text_angle = 90)

VizBulkDEGs(degs, plot_type = "ridge", genes = 2)
#> Picking joint bandwidth of 2.34
#> Picking joint bandwidth of 0.935
#> Picking joint bandwidth of 2.34
#> Picking joint bandwidth of 0.935

VizBulkDEGs(degs, plot_type = "heatmap", genes = 5)

# }