Feature statistical plots

Usage

FeatureStatPlot(
  object,
  stat.by,
  group.by = NULL,
  split.by = NULL,
  bg.by = NULL,
  plot.by = c("group", "feature"),
  fill.by = c("group", "feature", "expression"),
  cells = NULL,
  layer = "data",
  assay = NULL,
  keep_empty = FALSE,
  individual = FALSE,
  plot_type = c("violin", "box", "bar", "dot", "col"),
  palette = "Chinese",
  palcolor = NULL,
  alpha = 1,
  bg_palette = "Chinese",
  bg_palcolor = NULL,
  bg_alpha = 0.2,
  add_box = FALSE,
  box_color = "black",
  box_width = 0.1,
  box_ptsize = 2,
  add_point = FALSE,
  pt.color = "grey30",
  pt.size = NULL,
  pt.alpha = 1,
  jitter.width = 0.4,
  jitter.height = 0.1,
  add_trend = FALSE,
  trend_color = "black",
  trend_linewidth = 1,
  trend_ptsize = 2,
  add_stat = c("none", "mean", "median"),
  stat_color = "black",
  stat_size = 1,
  stat_stroke = 1,
  stat_shape = 25,
  add_line = NULL,
  line_color = "red",
  line_size = 1,
  line_type = 1,
  cells.highlight = NULL,
  cols.highlight = "red",
  sizes.highlight = 1,
  alpha.highlight = 1,
  calculate_coexp = FALSE,
  same.y.lims = FALSE,
  y.min = NULL,
  y.max = NULL,
  y.trans = "identity",
  y.nbreaks = 5,
  sort = FALSE,
  stack = FALSE,
  flip = FALSE,
  comparisons = NULL,
  ref_group = NULL,
  auto_comparison = FALSE,
  pairwise_method = "wilcox.test",
  multiplegroup_comparisons = FALSE,
  multiple_method = "kruskal.test",
  sig_label = c("p.signif", "p.format"),
  sig_labelsize = 3.5,
  aspect.ratio = NULL,
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = "Expression level",
  legend.position = "right",
  legend.direction = "vertical",
  legend.title = NULL,
  theme_use = "theme_scop",
  theme_args = list(),
  grid_major = TRUE,
  grid_major_colour = "grey80",
  grid_major_linetype = 2,
  grid_major_linewidth = 0.3,
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  force = FALSE,
  seed = 11,
  ...,
  x_text_angle = 45,
  verbose = TRUE,
  srt = NULL
)

Arguments

object: A Seurat object.
stat.by: Features to plot.
group.by: Metadata column(s) used to color cells.
split.by: Metadata column to facet by.
bg.by: Metadata column used as background color.
plot.by: "group" or "feature".
fill.by: "group", "feature", or "expression".
cells: Cell names to include.
layer: Assay layer to use.
assay: Assay to use. NULL uses the default assay.
keep_empty: Keep empty factor levels.
individual: One plot per group.
plot_type: "violin", "box", "bar", "dot", or "col".
palette, palcolor: Palette name (thisplot::show_palettes) or custom colors.
alpha: Plot transparency.
bg_palette, bg_palcolor, bg_alpha: Background palette and transparency.
add_box, box_color, box_width, box_ptsize: Overlay boxplot.
add_point, pt.color, jitter.width, jitter.height: Overlay jittered points.
pt.size, pt.alpha: Point size and transparency. pt.size = NULL scales with sqrt(n) (minimum 0.3). Rasterized points keep at least a two-pixel radius at raster.dpi = c(512, 512) and scale with raster.dpi.
add_trend, trend_color, trend_linewidth, trend_ptsize: Overlay trend line.
add_stat, stat_color, stat_size, stat_stroke, stat_shape: Summary statistic ("none", "mean", or "median").
add_line, line_color, line_size, line_type: Horizontal line at this y-intercept.
cells.highlight: Cells to highlight and their appearance. TRUE highlights all cells.
cols.highlight, sizes.highlight, alpha.highlight: Highlighted cells.
calculate_coexp: Plot the geometric mean of stat.by.
same.y.lims: Share y-axis limits across panels.
y.min, y.max: Y-axis limits. Character values like "q5" use that quantile.
y.trans, y.nbreaks: Y-axis transform ("identity" or "log2") and breaks.
sort: Sort groups on the x-axis: FALSE, TRUE/"increasing", or "decreasing".
stack: Stack plots vertically.
flip: Flip x and y.
comparisons, ref_group: Pairwise comparisons (length-2 name or index vectors).
auto_comparison: Compare the highest-median group (or ref_group) against the others. Requires split.by = NULL.
pairwise_method, multiplegroup_comparisons, multiple_method: Comparison tests.
sig_label, sig_labelsize: Significance labels ("p.signif" or "p.format").
aspect.ratio: Panel aspect ratio.
title: Plot title. NULL hides the title for merged/single panels. When multiple lineages are plotted and title is NULL, each panel is titled with its lineage column.
subtitle: Plot subtitle.
xlab: Plot labels.
ylab: Y-axis label.
legend.position, legend.direction, legend.title: Legend placement ("none", "left", "right", "bottom", "top"), direction, and title. legend.title = NULL uses the group name.
theme_use, theme_args: Theme name or function, plus extra theme arguments.
grid_major, grid_major_colour, grid_major_linetype, grid_major_linewidth: Major panel grid lines.
combine, nrow, ncol, byrow: Combine plots with patchwork. combine = FALSE returns a list of ggplots.
force: Draw even when more than 50 stat.by features are requested.
seed: Random seed.
...: Additional arguments passed to the plotting helpers.
x_text_angle: Rotation of x-axis labels.
verbose: Whether to print messages.
srt: Deprecated alias for object; supply exactly one of the two. It will be removed in scop 1.0.0.

Examples

data(pancreas_sub)
pancreas_sub <- RunStandardWorkflow(pancreas_sub)
#> ℹ [2026-10-11 22:53:58] Start standard processing workflow...
#> ℹ [2026-10-11 22:53:58] Checking a list of <Seurat>...
#> ! [2026-10-11 22:53:58] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-10-11 22:53:58] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 22:53:58] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 22:53:58] Use the separate HVF from `srt_list`
#> ℹ [2026-10-11 22:53:58] Number of available HVF: 2000
#> ℹ [2026-10-11 22:53:58] Finished check
#> ℹ [2026-10-11 22:53:58] Perform `ScaleData()`
#> ℹ [2026-10-11 22:53:58] Perform pca linear dimension reduction
#> ℹ [2026-10-11 22:53:59] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-10-11 22:53:59] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-10-11 22:53:59] Reorder clusters...
#> ℹ [2026-10-11 22:53:59] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-10-11 22:53:59] Perform umap nonlinear dimension reduction
#> ✔ [2026-10-11 22:54:05] Standard processing workflow completed
FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType"
)


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType"
) |> thisplot::panel_fix(height = 1, width = 2)


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  plot_type = "box"
)


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  plot_type = "bar"
)


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  plot_type = "dot"
)

FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  plot_type = "col"
)


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  add_box = TRUE
)


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  add_point = TRUE
)

FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  add_trend = TRUE
)


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  add_stat = "mean"
)


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  add_line = 0.2,
  line_type = 2
)


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  split.by = "Phase"
)
#> ! [2026-10-11 22:54:14] Removed 10 groups with < 2 observations for violin plot: "sp-S-gp-Beta", "sp-G2M-gp-Beta", "sp-S-gp-Pre-endocrine", "sp-G2M-gp-Pre-endocrine", "sp-S-gp-Alpha", "sp-G2M-gp-Alpha", "sp-S-gp-Epsilon", "sp-G2M-gp-Epsilon", "sp-S-gp-Delta", and "sp-G2M-gp-Delta"
#> ! [2026-10-11 22:54:14] Removed 10 groups with < 2 observations for violin plot: "sp-S-gp-Beta", "sp-G2M-gp-Beta", "sp-S-gp-Pre-endocrine", "sp-G2M-gp-Pre-endocrine", "sp-S-gp-Alpha", "sp-G2M-gp-Alpha", "sp-S-gp-Epsilon", "sp-G2M-gp-Epsilon", "sp-S-gp-Delta", and "sp-G2M-gp-Delta"


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  split.by = "Phase",
  add_box = TRUE,
  add_trend = TRUE
)
#> ! [2026-10-11 22:54:14] Removed 10 groups with < 2 observations for violin plot: "sp-S-gp-Beta", "sp-G2M-gp-Beta", "sp-S-gp-Pre-endocrine", "sp-G2M-gp-Pre-endocrine", "sp-S-gp-Alpha", "sp-G2M-gp-Alpha", "sp-S-gp-Epsilon", "sp-G2M-gp-Epsilon", "sp-S-gp-Delta", and "sp-G2M-gp-Delta"
#> ! [2026-10-11 22:54:15] Removed 10 groups with < 2 observations for violin plot: "sp-S-gp-Beta", "sp-G2M-gp-Beta", "sp-S-gp-Pre-endocrine", "sp-G2M-gp-Pre-endocrine", "sp-S-gp-Alpha", "sp-G2M-gp-Alpha", "sp-S-gp-Epsilon", "sp-G2M-gp-Epsilon", "sp-S-gp-Delta", and "sp-G2M-gp-Delta"


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  split.by = "Phase",
  comparisons = TRUE
)
#> ! [2026-10-11 22:54:16] Removed 10 groups with < 2 observations for violin plot: "sp-S-gp-Beta", "sp-G2M-gp-Beta", "sp-S-gp-Pre-endocrine", "sp-G2M-gp-Pre-endocrine", "sp-S-gp-Alpha", "sp-G2M-gp-Alpha", "sp-S-gp-Epsilon", "sp-G2M-gp-Epsilon", "sp-S-gp-Delta", and "sp-G2M-gp-Delta"
#> ℹ [2026-10-11 22:54:16] Detected more than 2 groups. Use "kruskal.test" for comparison
#> ! [2026-10-11 22:54:17] Removed 10 groups with < 2 observations for violin plot: "sp-S-gp-Beta", "sp-G2M-gp-Beta", "sp-S-gp-Pre-endocrine", "sp-G2M-gp-Pre-endocrine", "sp-S-gp-Alpha", "sp-G2M-gp-Alpha", "sp-S-gp-Epsilon", "sp-G2M-gp-Epsilon", "sp-S-gp-Delta", and "sp-G2M-gp-Delta"
#> ℹ [2026-10-11 22:54:17] Detected more than 2 groups. Use "kruskal.test" for comparison


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("Rbp4", "Pyy"),
  group.by = "SubCellType",
  fill.by = "expression",
  palette = "Blues",
  same.y.lims = TRUE
)


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("Rbp4", "Pyy"),
  group.by = "SubCellType",
  multiplegroup_comparisons = TRUE
)


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("Rbp4", "Pyy"),
  group.by = "SubCellType",
  auto_comparison = TRUE,
  sig_label = "p.signif"
)
#> `stat_compare_means()` with `comparisons` displays *unadjusted* p-values (no correction for multiple comparisons).
#> ℹ For p-values adjusted for multiple comparisons, use `geom_pwc()`, or `stat_pvalue_manual()` together with `compare_means(..., p.adjust.method = )`.
#> This message is displayed once per session.


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("Rbp4", "Pyy"),
  group.by = "SubCellType",
  comparisons = list(c("Alpha", "Beta"), c("Alpha", "Delta"))
)


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("Rbp4", "Pyy"),
  group.by = "SubCellType",
  comparisons = list(c("Alpha", "Beta"), c("Alpha", "Delta")),
  sig_label = "p.format"
)


FeatureStatPlot(
  pancreas_sub,
  stat.by = "Fev",
  group.by = "SubCellType",
  split.by = "Phase",
  comparisons = TRUE
) + FeatureStatPlot(
  pancreas_sub,
  stat.by = "Fev",
  group.by = "SubCellType",
  split.by = "Phase",
  comparisons = TRUE,
  y.max = 5
)
#> ! [2026-10-11 22:54:24] Removed 10 groups with < 2 observations for violin plot: "sp-S-gp-Beta", "sp-G2M-gp-Beta", "sp-S-gp-Pre-endocrine", "sp-G2M-gp-Pre-endocrine", "sp-S-gp-Alpha", "sp-G2M-gp-Alpha", "sp-S-gp-Epsilon", "sp-G2M-gp-Epsilon", "sp-S-gp-Delta", and "sp-G2M-gp-Delta"
#> ℹ [2026-10-11 22:54:24] Detected more than 2 groups. Use "kruskal.test" for comparison
#> ! [2026-10-11 22:54:24] Removed 10 groups with < 2 observations for violin plot: "sp-S-gp-Beta", "sp-G2M-gp-Beta", "sp-S-gp-Pre-endocrine", "sp-G2M-gp-Pre-endocrine", "sp-S-gp-Alpha", "sp-G2M-gp-Alpha", "sp-S-gp-Epsilon", "sp-G2M-gp-Epsilon", "sp-S-gp-Delta", and "sp-G2M-gp-Delta"
#> ℹ [2026-10-11 22:54:24] Detected more than 2 groups. Use "kruskal.test" for comparison


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("Rbp4", "Pyy"),
  group.by = "SubCellType",
  bg.by = "CellType",
  add_box = TRUE, stack = TRUE
)


FeatureStatPlot(
  pancreas_sub,
  stat.by = c(
    # Ductal
    "Sox9", "Anxa2", "Bicc1",
    # EPs
    "Neurog3", "Hes6",
    # Pre-endocrine
    "Fev", "Neurod1",
    # Endocrine
    "Rbp4", "Pyy",
    # Beta, Alpha, Delta, Epsilon
    "Ins1", "Gcg", "Sst", "Ghrl"
  ),
  legend.position = "top",
  legend.direction = "horizontal",
  group.by = "SubCellType",
  bg.by = "CellType",
  stack = TRUE
)


FeatureStatPlot(
  pancreas_sub,
  stat.by = c(
    # Ductal
    "Sox9", "Anxa2", "Bicc1",
    # EPs
    "Neurog3", "Hes6",
    # Pre-endocrine
    "Fev", "Neurod1",
    # Endocrine
    "Rbp4", "Pyy",
    # Beta, Alpha, Delta, Epsilon
    "Ins1", "Gcg", "Sst", "Ghrl"
  ),
  fill.by = "feature",
  plot_type = "box",
  group.by = "SubCellType",
  bg.by = "CellType", stack = TRUE, flip = TRUE
) |> thisplot::panel_fix_overall(
  width = 8, height = 5
)


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("Neurog3", "Rbp4", "Ins1"),
  group.by = "CellType",
  plot.by = "group"
)


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("Neurog3", "Rbp4", "Ins1"),
  group.by = "CellType",
  plot.by = "feature"
)
#> ℹ [2026-10-11 22:54:36] Setting `group.by` to "Features" as `plot.by` is set to "feature"


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("Neurog3", "Rbp4", "Ins1"),
  group.by = "CellType",
  plot.by = "feature",
  multiplegroup_comparisons = TRUE,
  sig_label = "p.format",
  sig_labelsize = 4
)
#> ℹ [2026-10-11 22:54:37] Setting `group.by` to "Features" as `plot.by` is set to "feature"


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("Neurog3", "Rbp4", "Ins1"),
  group.by = "CellType",
  plot.by = "feature",
  comparisons = list(
    c("Neurog3", "Rbp4"),
    c("Rbp4", "Ins1")
  ),
  stack = TRUE
)
#> ℹ [2026-10-11 22:54:40] Setting `group.by` to "Features" as `plot.by` is set to "feature"


FeatureStatPlot(pancreas_sub,
  stat.by = c(
    # Ductal
    "Sox9", "Anxa2", "Bicc1",
    # EPs
    "Neurog3", "Hes6",
    # Pre-endocrine
    "Fev", "Neurod1",
    # Endocrine
    "Rbp4", "Pyy",
    # Beta, Alpha, Delta, Epsilon
    "Ins1", "Gcg", "Sst", "Ghrl"
  ),
  group.by = "SubCellType",
  plot.by = "feature",
  stack = TRUE
)
#> ℹ [2026-10-11 22:54:43] Setting `group.by` to "Features" as `plot.by` is set to "feature"


data <- GetAssayData5(
  pancreas_sub,
  assay = "RNA",
  layer = "data"
)
pancreas_sub <- SeuratObject::SetAssayData(
  object = pancreas_sub,
  layer = "scale.data",
  assay = "RNA",
  new.data = data / Matrix::rowMeans(data)
)
FeatureStatPlot(
  pancreas_sub,
  stat.by = c("Neurog3", "Rbp4"),
  group.by = "CellType",
  layer = "scale.data",
  ylab = "FoldChange",
  same.y.lims = TRUE,
  y.max = 4
)

Feature statistical plots

Usage

Arguments

See also

Examples