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Visualize top transcription factors (TFs) from ChEA3 results

Source: R/visualizeRank.R
visualizeRank.Rd

Create a bar plot of the most significant transcription factors from a ChEA3 result table. The y-axis can be based on FDR, FET p-value, or Score (for integrated results). Bars are ordered by rank (Rank = 1 at the top).

Usage

visualizeRank(
  df_result,
  y_metric = c("auto", "FDR", "FET p-value", "Score"),
  fdr_threshold = 0.05,
  p_threshold = 0.05,
  query_name = "myGeneList",
  title_plot = "rChEA3 results (transcription factor enrichment analysis)",
  top_n = 10,
  fill_color = "#7AAACE"
)

Arguments

df_result

A ChEA3 result data frame. Must contain at least columns:

  • TF (transcription factor symbol),

  • Rank (integer rank). Optionally: FDR, FET p-value, Score.

y_metric

Character; which metric to use on the y-axis. One of:

  • "auto" (default): use FDR if present, otherwise FET p-value, otherwise Score.

  • "FDR": use FDR (requires FDR column).

  • "FET p-value": use Fisher’s exact test p-value (requires FET p-value column).

  • "Score": use Score (used in integrated meanRank/topRank).

fdr_threshold

Numeric; FDR cutoff for significance (default 0.05). Used only if y_metric = "FDR".

p_threshold

Numeric; p-value cutoff for significance (default 0.05). Used only if y_metric = "FET p-value".

query_name

Character; name of the input gene set, shown in the subtitle (default myGeneList)

title_plot

Character; main plot title (default rChEA3 results (transcription factor enrichment analysis))

top_n

Integer; number of TFs to display (default 10). The subtitle reports the total number of significant TFs, while only the top_n by rank are plotted.

fill_color

Character; fill color of the bars (default "#7AAACE").

Value

A ggplot object.

Details

The plot subtitle automatically reports the number of significant TFs (after filtering by fdr_threshold or p_threshold when applicable), while top_n controls how many of those TFs are displayed. For integrated collections (Mean Rank and Top Rank), the subtitle shows a descriptive label instead of individual library names.

Examples

# \donttest{
# Example with integrated meanRank results
genes <- c("STAT3", "RELA", "MYC", "FOXO1", "TP53")
results <- queryChEA3(genes, verbose = FALSE)
meanRank_res <- results[["Integrated--meanRank"]]
visualizeRank(meanRank_res, y_metric = "Score", top_n = 15)


# Example with ChIP-seq results (FET p-value-based)
chip_res <- results[["ENCODE--ChIP-seq"]]
visualizeRank(chip_res, y_metric = "FET p-value")

# }