DEtest Manhattan Plot

Draw a Manhattan-style plot of differential expression results by cell type.

Usage

DEtestManhattanPlot(
  srt,
  group.by = NULL,
  test.use = "wilcox",
  res = NULL,
  DE_threshold = "avg_log2FC > 0 & p_val_adj < 0.05",
  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,
  palette = "RdBu",
  palcolor = NULL,
  theme_use = "theme_scop",
  theme_args = list(),
  manhattan.bg = "white",
  jitter_width = 0.5,
  jitter_height = 0.4,
  aspect.ratio = NULL,
  xlab = NULL,
  ylab = NULL
)

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".
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.
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.
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().
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.
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.

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-03-20 08:17:30] Start standard scop workflow...
#> ℹ [2026-03-20 08:17:31] Checking a list of <Seurat>...
#> ! [2026-03-20 08:17:31] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-03-20 08:17:31] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-03-20 08:17:33] Perform `Seurat::FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-03-20 08:17:33] Use the separate HVF from `srt_list`
#> ℹ [2026-03-20 08:17:33] Number of available HVF: 2000
#> ℹ [2026-03-20 08:17:33] Finished check
#> ℹ [2026-03-20 08:17:34] Perform `Seurat::ScaleData()`
#> ℹ [2026-03-20 08:17:34] Perform pca linear dimension reduction
#> ℹ [2026-03-20 08:17:35] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-03-20 08:17:35] Reorder clusters...
#> ℹ [2026-03-20 08:17:35] Perform umap nonlinear dimension reduction
#> ℹ [2026-03-20 08:17:35] Perform umap nonlinear dimension reduction using Standardpca (1:50)
#> ℹ [2026-03-20 08:17:38] Perform umap nonlinear dimension reduction using Standardpca (1:50)
#> ✔ [2026-03-20 08:17:41] Run scop standard workflow completed
pancreas_sub <- RunDEtest(
  pancreas_sub,
  group.by = "CellType",
  only.pos = FALSE
)
#> ℹ [2026-03-20 08:17:41] Data type is log-normalized
#> ℹ [2026-03-20 08:17:41] Start differential expression test
#> ℹ [2026-03-20 08:17:41] Find all markers(wilcox) among [1] 5 groups...
#> ℹ [2026-03-20 08:17:41] Using 1 core
#> ⠙ [2026-03-20 08:17:41] Running for Ductal [1/5] ■■■■■■■                       …
#> ✔ [2026-03-20 08:17:41] Completed 5 tasks in 1s
#> 
#> ℹ [2026-03-20 08:17:41] Building results
#> ✔ [2026-03-20 08:17:42] Differential expression test completed
DEtestManhattanPlot(
  pancreas_sub,
  group.by = "CellType"
)

Usage

Arguments

See also

Examples