Visualize DEG dynamic trends by Mfuzz cluster

Visualizes dynamic expression trends of differentially expressed genes (DEGs) grouped by Mfuzz clustering results stored in `rowData(se)`. The function summarizes gene expression across conditions, optionally scales expression per gene (z-score), and displays cluster-level average trends with optional single-gene trajectories.

Usage

plot_deg_trends(
  se,
  assay_name = "conc",
  group_by = "condition",
  scale_by_gene = TRUE,
  show_all_genes = TRUE,
  show_ci = FALSE,
  ci_level = 0.95
)

Arguments

se

A SummarizedExperiment object. Must contain:

• An assay specified by assay_name

• A column in colData(se) specified by group_by

• Columns gene, gene_group, and type in rowData(se)

assay_name

Character. Name of assay to visualize. Default is "conc".

group_by

Character. Column name in colData(se) used to define condition or time ordering. Default is "condition".

scale_by_gene

Logical. If TRUE, expression values are scaled per gene (z-score across conditions). Recommended for dynamic pattern visualization. Default is TRUE.

show_all_genes

Logical. If TRUE, individual gene trajectories are shown as semi-transparent grey lines. Default is TRUE.

show_ci

Logical. If TRUE, add 95% confidence intervals for the cluster-level mean trend at each condition. Default is FALSE.

ci_level

Numeric. Confidence level used when show_ci = TRUE. Default is 0.95.

Value

A ggplot2 object showing dynamic expression trends for each DEG cluster.

Details

The function performs the following steps:

1. Filters genes labeled as type == "DEGs" in rowData(se).

2. Computes mean expression per gene per condition.

3. Optionally standardizes expression per gene.

4. Computes cluster-level mean trends and optional confidence intervals.

5. Generates a faceted line plot by gene_group.

This function assumes that Mfuzz clustering results have already been computed and stored in rowData(se)$gene_group.

Examples

if (FALSE) { # \dontrun{
  p <- plot_deg_trends(se)
  print(p)
} # }