Step: Significant Variables Boxplot

Create boxplots for the most significant variables from DEA analysis using glyvis::plot_boxplot(). The function selects the top n_top variables with the lowest adjusted p-values from the DEA results and plots their expression values grouped by sample groups.

Usage

step_sig_boxplot(
  on = "sig_exp",
  n_top = 25,
  panel_width = 1.5,
  panel_height = 1.2,
  min_width = 5,
  min_height = 3,
  max_width = 14,
  max_height = 12,
  ...
)

Arguments

on: Name of the experiment data in ctx$data to plot. One of "sig_exp", "sig_trait_exp", "sig_motif_exp". Default is "sig_exp".
n_top: Number of top significant variables to plot. Must be between 1 and 25 (inclusive). Default is 25.
panel_width: Width of each panel in inches. Default is 1.5.
panel_height: Height of each panel in inches. Default is 1.2.
min_width: Minimum plot width in inches. Default is 5.
min_height: Minimum plot height in inches. Default is 3.
max_width: Maximum plot width in inches. Default is 14.
max_height: Maximum plot height in inches. Default is 12.
...: Additional arguments passed to glyvis::plot_boxplot().

Value

A glysmith_step object.

Details

This step requires a DEA step to be run first (e.g., step_dea_limma(), step_dea_ttest(), step_dea_wilcox(), step_dea_anova(), or step_dea_kruskal()). The number of variables is limited to a maximum of 25, as enforced by glyvis::plot_boxplot().

Data required:

Depends on on parameter:
- sig_exp (default): Significant experiment from DEA
- sig_trait_exp: Significant trait experiment from DTA
- sig_motif_exp: Significant motif experiment from DMA

Plots generated:

sig_boxplot: A boxplot of significant variables (if on = "sig_exp")
sig_trait_boxplot: A boxplot of significant traits (if on = "sig_trait_exp")
sig_motif_boxplot: A boxplot of significant motifs (if on = "sig_motif_exp")

AI Prompt

This section is for AI in inquire_blueprint() only.

Include this step after DEA steps to visualize the significant variables.
This step is particularly useful for understanding the expression patterns of the most differentially expressed features across groups.

Examples