Volcano Plot

Generate a volcano plot based on differential expression analysis results.

Usage

VolcanoPlot(
  srt,
  group.by = NULL,
  test.use = "wilcox",
  res = NULL,
  DE_threshold = "avg_log2FC > 0 & p_val_adj < 0.05",
  x_metric = "diff_pct",
  palette = "RdBu",
  palcolor = NULL,
  pt.size = 1,
  pt.alpha = 1,
  cols.highlight = "black",
  sizes.highlight = 1,
  alpha.highlight = 1,
  stroke.highlight = 0.5,
  nlabel = 5,
  features_label = NULL,
  label.fg = "black",
  label.bg = "white",
  label.bg.r = 0.1,
  label.size = 4,
  aspect.ratio = NULL,
  xlab = x_metric,
  ylab = "-log10(p-adjust)",
  theme_use = "theme_scop",
  theme_args = list(),
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  threshold_method = c("rectangular", "hyperbolic"),
  hyperbola_c = 6,
  annotate_enrichment = FALSE,
  enrich_from = c("Enrichment", "GSEA", "GSVA"),
  enrich_db = NULL,
  enrich_terms = NULL,
  enrich_top_terms = 3,
  enrich_padj_cutoff = 0.05,
  enrich_gsva_score_cutoff = NULL,
  gsva_method = NULL,
  enrich_nlabel = 15
)

Arguments

srt: An object of class Seurat containing the results of differential expression analysis.
group.by: Name of one or more meta.data columns to group (color) cells by.
test.use: A character string specifying the type of statistical test to use. Default is "wilcox".
res: A data.frame or data.table with differential expression results. When res is provided, srt will be ignored. The data.frame must contain columns: gene, group1 (factor or character), avg_log2FC, p_val_adj, and optionally pct.1 and pct.2 for calculating diff_pct.
DE_threshold: A character string specifying the threshold for differential expression (used to highlight significant genes in all plot types). Default is "avg_log2FC > 0 & p_val_adj < 0.05".
x_metric: A character string specifying the metric to use for the x-axis (only for volcano plot). Default is "diff_pct".
threshold_method: Volcano significance threshold method. Options are "rectangular" (legacy DE_threshold) or "hyperbolic" (|log2FC * -log10(padj)| > c). Default is "rectangular".
hyperbola_c: Numeric cutoff c for hyperbolic volcano threshold. Default is 6.
annotate_enrichment: Whether to annotate enrichment-hit genes on volcano plots. Enrichment results are read from existing results in srt@tools only. Default is FALSE.
enrich_from: Character vector specifying enrichment result source(s) to annotate. Options are "Enrichment", "GSEA", "GSVA". Default is c("Enrichment", "GSEA", "GSVA").
enrich_db: Optional database filter for enrichment annotation, e.g. "GO_BP" or "KEGG". Default is NULL.
enrich_terms: Optional whitelist of enrichment term IDs or names for annotation. Default is NULL.
enrich_top_terms: Number of top enriched terms selected per source/group/database. Default is 3.
enrich_padj_cutoff: Adjusted p-value cutoff for "Enrichment" and "GSEA" annotation. Default is 0.05.
enrich_gsva_score_cutoff: Optional absolute GSVA score cutoff for "GSVA" annotation. Default is NULL.
gsva_method: Optional GSVA method filter (e.g. "gsva" or "ssgsea") when multiple GSVA tool slots exist. Default is NULL.
enrich_nlabel: Maximum number of enrichment-derived labels added per group. Labels from features_label are always retained. Default is 15.
palette: Color palette name. Available palettes can be found in thisplot::show_palettes. Default is "RdBu".
palcolor: Custom colors used to create a color palette. Default is NULL.
pt.size: The size of the points. Default is 1.
pt.alpha: The transparency of the data points. Default is 1.
cols.highlight: A character string specifying the color for highlighted points. Default is "black".
sizes.highlight: The size of the highlighted points. Default is 1.
alpha.highlight: The transparency of the highlighted points. Default is 1.
stroke.highlight: The stroke width for the highlighted points. Default is 0.5.
nlabel: An integer value specifying the number of labeled points per group. Default is 5.
features_label: A character vector specifying the feature labels to plot. Default is NULL.
label.fg: A character string specifying the color for the labels' foreground. Default is "black".
label.bg: A character string specifying the color for the labels' background. Default is "white".
label.bg.r: The radius of the rounding of the labels' background. Default is 0.1.
label.size: The size of the labels. Default is 4.
aspect.ratio: Aspect ratio of the panel. Default is NULL.
xlab: A character string specifying the x-axis label.
ylab: A character string specifying the y-axis label.
theme_use: Theme to use for the plot. Default is "theme_scop".
theme_args: A list of additional arguments to pass to the theme function. Default is list().
combine: Whether to combine multiple plots into one. Default is TRUE.
nrow: Number of rows for combined plots. Default is NULL.
ncol: Number of columns for combined plots. Default is NULL.
byrow: Whether to fill plots by row. Default is TRUE.

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-04-26 02:31:40] Start standard processing workflow...
#> ℹ [2026-04-26 02:31:40] Checking a list of <Seurat>...
#> ! [2026-04-26 02:31:41] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-04-26 02:31:41] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-04-26 02:31:43] Perform `Seurat::FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-04-26 02:31:44] Use the separate HVF from `srt_list`
#> ℹ [2026-04-26 02:31:44] Number of available HVF: 2000
#> ℹ [2026-04-26 02:31:44] Finished check
#> ℹ [2026-04-26 02:31:44] Perform `Seurat::ScaleData()`
#> ℹ [2026-04-26 02:31:45] Perform pca linear dimension reduction
#> ℹ [2026-04-26 02:31:45] Use stored estimated dimensions 1:20 for Standardpca
#> ℹ [2026-04-26 02:31:46] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-04-26 02:31:46] Reorder clusters...
#> ℹ [2026-04-26 02:31:46] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-04-26 02:31:46] Perform umap nonlinear dimension reduction
#> ℹ [2026-04-26 02:31:46] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ℹ [2026-04-26 02:31:52] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ✔ [2026-04-26 02:31:57] Standard processing workflow completed
pancreas_sub <- RunDEtest(
  pancreas_sub,
  group.by = "CellType"
)
#> ℹ [2026-04-26 02:31:58] Data type is log-normalized
#> ℹ [2026-04-26 02:31:58] Start differential expression test
#> ℹ [2026-04-26 02:31:58] Find all markers(wilcox) among [1] 5 groups...
#> ℹ [2026-04-26 02:31:58] Using 1 core
#> ⠙ [2026-04-26 02:31:58] Running for Ductal [1/5] ■■          20% | ETA:  1s
#> ✔ [2026-04-26 02:31:58] Completed 5 tasks in 876ms
#> 
#> ℹ [2026-04-26 02:31:58] Building results
#> ✔ [2026-04-26 02:31:59] Differential expression test completed
VolcanoPlot(
  pancreas_sub,
  group.by = "CellType",
  ncol = 2
)


VolcanoPlot(
  pancreas_sub,
  group.by = "CellType",
  DE_threshold = "abs(diff_pct) > 0.3 & p_val_adj < 0.05",
  ncol = 2
)


VolcanoPlot(
  pancreas_sub,
  group.by = "CellType",
  x_metric = "avg_log2FC",
  ncol = 2
)


if (FALSE) { # \dontrun{
pancreas_sub <- RunEnrichment(
  pancreas_sub,
  group.by = "CellType",
  db = "GO_BP",
  species = "Mus_musculus"
)
VolcanoPlot(
  pancreas_sub,
  group.by = "CellType",
  threshold_method = "hyperbolic",
  hyperbola_c = 6,
  annotate_enrichment = TRUE,
  enrich_from = "Enrichment",
  enrich_db = "GO_BP",
  ncol = 2
)
} # }

Usage

Arguments

See also

Examples