View source: R/visualizeRank.R
| visualizeRank | R Documentation |
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).
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"
)
df_result |
A ChEA3 result data frame. Must contain at least columns:
|
y_metric |
Character; which metric to use on the y-axis. One of:
|
fdr_threshold |
Numeric; FDR cutoff for significance (default |
p_threshold |
Numeric; p-value cutoff for significance (default |
query_name |
Character; name of the input gene set, shown in the subtitle (default |
title_plot |
Character; main plot title (default |
top_n |
Integer; number of TFs to display (default |
fill_color |
Character; fill color of the bars (default |
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.
A ggplot object.
# 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")
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.