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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 = "Paired",
  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
)

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".

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 "Paired".

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 [2026-02-11 03:16:03] Start standard scop workflow...
#> ℹ [2026-02-11 03:16:04] Checking a list of <Seurat>...
#> ! [2026-02-11 03:16:04] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-02-11 03:16:04] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2026-02-11 03:16:06] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2026-02-11 03:16:06] Use the separate HVF from srt_list
#> ℹ [2026-02-11 03:16:06] Number of available HVF: 2000
#> ℹ [2026-02-11 03:16:07] Finished check
#> ℹ [2026-02-11 03:16:07] Perform `Seurat::ScaleData()`
#> ℹ [2026-02-11 03:16:07] Perform pca linear dimension reduction
#> ℹ [2026-02-11 03:16:08] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-02-11 03:16:08] Reorder clusters...
#> ℹ [2026-02-11 03:16:08] Perform umap nonlinear dimension reduction
#> ℹ [2026-02-11 03:16:08] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2026-02-11 03:16:11] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ✔ [2026-02-11 03:16:14] Run scop standard workflow completed
pancreas_sub <- RunDEtest(
  pancreas_sub,
  group.by = "CellType",
  only.pos = FALSE
)
#> ℹ [2026-02-11 03:16:15] Data type is log-normalized
#> ℹ [2026-02-11 03:16:15] Start differential expression test
#> ℹ [2026-02-11 03:16:15] Find all markers(wilcox) among [1] 5 groups...
#> ℹ [2026-02-11 03:16:15] Using 1 core
#> ⠙ [2026-02-11 03:16:15] Running for Ductal [1/5] ■■■■■■■                       …
#> ✔ [2026-02-11 03:16:15] Completed 5 tasks in 875ms
#> 
#> ℹ [2026-02-11 03:16:15] Building results
#> ✔ [2026-02-11 03:16:16] 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
)