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Volcano Plot

Source: R/VolcanoPlot.R
VolcanoPlot.Rd

Generate a volcano plot based on differential expression analysis results.

Usage

VolcanoPlot(
  srt,
  group.by = NULL,
  test.use = "wilcox",
  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
)

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".

DE_threshold

A character string specifying the threshold for differential expression. Default is "avg_log2FC > 0 & p_val_adj < 0.05".

x_metric

A character string specifying the metric to use for the x-axis. Default is "diff_pct".

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. Default is the value of x_metric.

ylab

A character string specifying the y-axis label. Default is "-log10(p-adjust)".

theme_use

Theme used. Can be a character string or a theme function. Default is "theme_scop".

theme_args

Other arguments passed to the theme_use. Default is list().

combine

Combine plots into a single patchwork object. If FALSE, return a list of ggplot objects.

nrow

Number of rows in the combined plot. Default is NULL, which means determined automatically based on the number of plots.

ncol

Number of columns in the combined plot. Default is NULL, which means determined automatically based on the number of plots.

byrow

Whether to arrange the plots by row in the combined plot. Default is TRUE.

See also

RunDEtest

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-01-27 08:36:01] Start standard scop workflow...
#> ℹ [2026-01-27 08:36:02] Checking a list of <Seurat>...
#> ! [2026-01-27 08:36:02] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-01-27 08:36:02] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 08:36:04] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 08:36:04] Use the separate HVF from srt_list
#> ℹ [2026-01-27 08:36:04] Number of available HVF: 2000
#> ℹ [2026-01-27 08:36:05] Finished check
#> ℹ [2026-01-27 08:36:05] Perform `Seurat::ScaleData()`
#> ℹ [2026-01-27 08:36:05] Perform pca linear dimension reduction
#> ℹ [2026-01-27 08:36:06] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-01-27 08:36:06] Reorder clusters...
#> ℹ [2026-01-27 08:36:06] Perform umap nonlinear dimension reduction
#> ℹ [2026-01-27 08:36:06] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2026-01-27 08:36:11] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ✔ [2026-01-27 08:36:15] Run scop standard workflow completed
pancreas_sub <- RunDEtest(
  pancreas_sub,
  group.by = "CellType"
)
#> ℹ [2026-01-27 08:36:16] Data type is log-normalized
#> ℹ [2026-01-27 08:36:16] Start differential expression test
#> ℹ [2026-01-27 08:36:16] Find all markers(wilcox) among [1] 5 groups...
#> ℹ [2026-01-27 08:36:16] Using 1 core
#> ⠙ [2026-01-27 08:36:16] Running for Ductal [1/5] ■■■■■■■                       …
#> ⠹ [2026-01-27 08:36:16] Running for Ngn3-high-EP [2/5] ■■■■■■■■■■■■■           …
#> ✔ [2026-01-27 08:36:16] Completed 5 tasks in 798ms
#> 
#> ℹ [2026-01-27 08:36:16] Building results
#> ✔ [2026-01-27 08:36:17] 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
)