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Differential Expression Test Plot

Source: R/DEtestPlot.R
DEtestPlot.Rd

Differential Expression Test Plot

Usage

DEtestPlot(
  srt,
  group.by = NULL,
  test.use = "wilcox",
  res = NULL,
  plot_type = c("volcano", "manhattan", "ring"),
  DE_threshold = "avg_log2FC > 0 & p_val_adj < 0.05",
  x_metric = "diff_pct",
  y_metric = c("p_val_adj", "p_val"),
  x_order = c("gene", "index"),
  palette = "RdBu",
  palcolor = NULL,
  group_palette = "Chinese",
  group_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 = NULL,
  ylab = NULL,
  theme_use = "theme_scop",
  theme_args = list(),
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  manhattan.bg = "white",
  jitter_width = 0.5,
  jitter_height = 0.4,
  tile_height = 0.3,
  tile_gap = 0.1,
  ring_segments = TRUE,
  seed = 11,
  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.

plot_type

Type of plot to create. Options: "volcano", "manhattan", or "ring". Default is "volcano".

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.

y_metric

A character string specifying the metric to use for the y-axis (only for Manhattan plot, not used currently). Options: "p_val" or "p_val_adj". Default is "p_val_adj".

x_order

A character string specifying how to order genes on x-axis (only for Manhattan plot, not used currently). Options: "gene" (alphabetical by gene name) or "index" (by data order). Default is "gene".

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.

group_palette

Palette for cell types (groups) in Manhattan plot. Default is "Chinese".

group_palcolor

Custom colors for cell types (groups) in Manhattan plot. 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.

manhattan.bg

Background color for Manhattan plot. Default is "white".

jitter_width

Horizontal jitter range for points in Manhattan plot. Default is 0.5.

jitter_height

Vertical jitter range for points in Manhattan plot. Default is 0.4.

tile_height

Height of the cell-type track in ring plot. Default is 0.3.

tile_gap

Gap between the track and nudged points in ring plot. Default is 0.1.

ring_segments

Whether to draw segment lines between cell types in ring plot. Default is TRUE.

seed

Random seed for jitter in ring plot. Default is 11.

See also

RunDEtest, VolcanoPlot, DEtestManhattanPlot, DEtestRingPlot

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-04-26 01:00:40] Start standard processing workflow...
#> ℹ [2026-04-26 01:00:41] Checking a list of <Seurat>...
#> ! [2026-04-26 01:00:41] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-04-26 01:00:41] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-04-26 01:00:43] Perform `Seurat::FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-04-26 01:00:43] Use the separate HVF from `srt_list`
#> ℹ [2026-04-26 01:00:43] Number of available HVF: 2000
#> ℹ [2026-04-26 01:00:43] Finished check
#> ℹ [2026-04-26 01:00:44] Perform `Seurat::ScaleData()`
#> ℹ [2026-04-26 01:00:44] Perform pca linear dimension reduction
#> ℹ [2026-04-26 01:00:44] Use stored estimated dimensions 1:20 for Standardpca
#> ℹ [2026-04-26 01:00:45] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-04-26 01:00:45] Reorder clusters...
#> ℹ [2026-04-26 01:00:45] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-04-26 01:00:45] Perform umap nonlinear dimension reduction
#> ℹ [2026-04-26 01:00:45] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ℹ [2026-04-26 01:00:49] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ✔ [2026-04-26 01:00:53] Standard processing workflow completed
pancreas_sub <- RunDEtest(
  pancreas_sub,
  group.by = "CellType",
  only.pos = FALSE
)
#> ℹ [2026-04-26 01:00:53] Data type is log-normalized
#> ℹ [2026-04-26 01:00:53] Start differential expression test
#> ℹ [2026-04-26 01:00:53] Find all markers(wilcox) among [1] 5 groups...
#> ℹ [2026-04-26 01:00:53] Using 1 core
#> ⠙ [2026-04-26 01:00:53] Running for Ductal [1/5] ■■          20% | ETA:  1s
#> ✔ [2026-04-26 01:00:53] Completed 5 tasks in 1s
#> 
#> ℹ [2026-04-26 01:00:53] Building results
#> ✔ [2026-04-26 01:00:54] Differential expression test completed

DEtestPlot(
  pancreas_sub,
  group.by = "CellType",
  plot_type = "volcano",
  ncol = 2
)


DEtestPlot(
  pancreas_sub,
  group.by = "CellType",
  plot_type = "manhattan"
)


DEtestPlot(
  pancreas_sub,
  group.by = "CellType",
  plot_type = "ring"
)


de_results1 <- pancreas_sub@tools$DEtest_CellType$AllMarkers_wilcox
DEtestPlot(
  res = de_results1,
  plot_type = "volcano",
  ncol = 2
)


de_results2 <- Seurat::FindMarkers(
  pancreas_sub,
  group.by = "CellType",
  ident.1 = "Ductal",
  ident.2 = "Endocrine"
)
DEtestPlot(
  res = de_results2,
  plot_type = "volcano"
)


de_results3 <- Seurat::FindAllMarkers(
  pancreas_sub,
  group.by = "CellType"
)
#> Calculating cluster Ductal
#> Calculating cluster Ngn3-high-EP
#> Calculating cluster Endocrine
#> Calculating cluster Ngn3-low-EP
#> Calculating cluster Pre-endocrine
DEtestPlot(
  res = de_results3,
  plot_type = "volcano",
  ncol = 2
)