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Combine a Volcano Plot with a gt Table of Genes

Source: R/gene_table.R
genes_table.Rd

Takes a ggplot object (typically from ggvolc) and a data frame of gene details and produces a combined layout using patchwork. The gene table is rendered as a polished gt table with color-coded significance and formatted numeric columns.

Usage

genes_table(
  plot_obj,
  data2,
  top_n = NULL,
  sig_col = c("padj", "pvalue"),
  dir = c("both", "up", "down", "each"),
  p_value = 0.05,
  table_height = 1
)

Arguments

plot_obj

A ggplot object, typically the output of ggvolc.

data2

A data frame containing gene details. Columns are auto-detected in the same way as ggvolc (DESeq2, edgeR, or limma conventions). Required columns after mapping: genes, log2FoldChange, pvalue. Optional: baseMean, padj. When top_n is supplied you can pass the full DE table here (e.g. all_genes) and the most significant genes are selected automatically.

top_n

Optional integer. When supplied, the top_n most significant genes (smallest sig_col, among genes with sig_col < p_value) are selected from data2 for the table. When NULL (default) every row of data2 is shown, so passing a pre-filtered set such as attention_genes behaves as before.

sig_col

Column used to rank significance when top_n is set. Either "padj" (adjusted p-value / FDR, the default) or "pvalue". Falls back to "pvalue" when "padj" is the default but no adjusted-p column is present.

dir

Direction to draw the top_n genes from. One of "both" (top N over all significant genes, the default), "up" (top N upregulated), "down" (top N downregulated), or "each" (top N up and top N down, up to 2 * top_n rows). Ignored when top_n is NULL.

p_value

Significance threshold used for color-coding the p-value column, and for filtering eligible genes when top_n is set. Default is 0.05.

table_height

Relative height of the table panel (plot panel is always 3). Default is 1.

Value

A patchwork object that can be further composed or saved with ggplot2::ggsave().

Examples

# Load example datasets
data(all_genes)
data(attention_genes)

# Create a volcano plot and combine it with a gt gene table
p <- ggvolc(all_genes, attention_genes, add_seg = TRUE)
genes_table(p, attention_genes)


# Auto-select the 10 most significant genes from the full table
p2 <- ggvolc(all_genes, label_top = 10)
genes_table(p2, all_genes, top_n = 10)


# Top 8 of each direction (up to 16 rows)
genes_table(p2, all_genes, top_n = 8, dir = "each")