This function generates a statistical plot for features.
Usage
FeatureStatPlot(
srt,
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 = "Paired",
palcolor = NULL,
alpha = 1,
bg_palette = "Paired",
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,
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",
theme_use = "theme_scop",
theme_args = list(),
combine = TRUE,
nrow = NULL,
ncol = NULL,
byrow = TRUE,
force = FALSE,
seed = 11
)Arguments
- srt
A Seurat object.
- stat.by
A character vector specifying the features to plot.
- group.by
A character vector specifying the groups to group by. Default is `NULL`.
- split.by
A character vector specifying the variable to split the plot by. Default is `NULL`.
- bg.by
A character vector specifying the variable to use as the background color. Default is `NULL`.
- plot.by
A character vector specifying how to plot the data, by group or feature. Possible values are `"group"` or `"feature"`. Default is `"group"`.
- fill.by
A string specifying what to fill the plot by. Possible values are `"group"`, `"feature"`, or `"expression"`. Default is `"group"`.
- cells
A character vector specifying the cells to include in the plot. Default is `NULL`.
- layer
A string specifying which layer of the Seurat object to use. Default is `"data"`.
- assay
A string specifying which assay to use. Default is `NULL`.
- keep_empty
Whether to keep empty levels in the plot. Default is `FALSE`.
- individual
Whether to create individual plots for each group. Default is `FALSE`.
- plot_type
A string specifying the type of plot to create. Possible values are `"violin"`, `"box"`, `"bar"`, `"dot"`, or `"col"`. Default is `"violin"`.
- palette
A string specifying the color palette to use for filling. Default is `"Paired"`.
- palcolor
A character vector specifying specific colors to use for filling. Default is `NULL`.
- alpha
The transparency of the plot. Default is `1`.
- bg_palette
A string specifying the color palette to use for the background. Default is `"Paired"`.
- bg_palcolor
A character vector specifying specific colors to use for the background. Default is `NULL`.
- bg_alpha
The transparency of the background. Default is `0.2`.
- add_box
Whether to add a box plot to the plot. Default is `FALSE`.
- box_color
A string specifying the color of the box plot. Default is `"black"`.
- box_width
The width of the box plot. Default is `0.1`.
- box_ptsize
The size of the points of the box plot. Default is `2`.
- add_point
Whether to add individual data points to the plot. Default is `FALSE`.
- pt.color
A string specifying the color of the data points. Default is `"grey30"`.
- pt.size
The size of the data points. If NULL, the size is automatically determined. Default is `NULL`.
- pt.alpha
The transparency of the data points. Default is `1`.
- jitter.width
The width of the jitter. Default is `0.5`.
- jitter.height
The height of the jitter. Default is `0.1`.
- add_trend
Whether to add a trend line to the plot. Default is `FALSE`.
- trend_color
A string specifying the color of the trend line. Default is `"black"`.
- trend_linewidth
The width of the trend line. Default is `1`.
- trend_ptsize
The size of the points of the trend line. Default is `2`.
- add_stat
A string specifying which statistical summary to add to the plot. Possible values are `"none"`, `"mean"`, or `"median"`. Default is `"none"`.
- stat_color
A string specifying the color of the statistical summary. Default is `"black"`.
- stat_size
The size of the statistical summary. Default is `1`.
- stat_stroke
The stroke width of the statistical summary. Default is `1`.
- stat_shape
The shape of the statistical summary. Default is `25`.
- add_line
The y-intercept for adding a horizontal line. Default is `NULL`.
- line_color
A string specifying the color of the horizontal line. Default is `"red"`.
- line_size
The width of the horizontal line. Default is `1`.
- line_type
The type of the horizontal line. Default is `1`.
- cells.highlight
A logical or character vector specifying the cells to highlight in the plot. If TRUE, all cells are highlighted. If FALSE, no cells are highlighted. Default is `NULL`.
- cols.highlight
A string specifying the color of the highlighted cells. Default is `"red"`.
- sizes.highlight
The size of the highlighted cells. Default is `1`.
- alpha.highlight
The transparency of the highlighted cells. Default is `1`.
- calculate_coexp
Whether to calculate co-expression values. Default is `FALSE`.
- same.y.lims
Whether to use the same y-axis limits for all plots. Default is `FALSE`.
- y.min
A numeric or character value specifying the minimum y-axis limit. If a character value is provided, it must be of the form "qN" where N is a number between 0 and 100 (inclusive) representing the quantile to use for the limit. Default is `NULL`.
- y.max
A numeric or character value specifying the maximum y-axis limit. If a character value is provided, it must be of the form "qN" where N is a number between 0 and 100 (inclusive) representing the quantile to use for the limit. Default is `NULL`.
- y.trans
A string specifying the transformation to apply to the y-axis. Possible values are `"identity"` or `"log2"`. Default is `"identity"`.
- y.nbreaks
A number of breaks to use for the y-axis. Default is `5`.
- sort
A logical or character value specifying whether to sort the groups on the x-axis. If TRUE, groups are sorted in increasing order. If FALSE, groups are not sorted. If "increasing", groups are sorted in increasing order. If "decreasing", groups are sorted in decreasing order. Default is `FALSE`.
- stack
A logical specifying whether to stack the plots on top of each other. Default is `FALSE`.
- flip
A logical specifying whether to flip the plot vertically. Default is `FALSE`.
- comparisons
A list of length-2 vectors. The entries in the vector are either the names of 2 values on the x-axis or the 2 integers that correspond to the index of the groups of interest, to be compared.
- ref_group
A string specifying the reference group for pairwise comparisons. Default is `NULL`.
- pairwise_method
Method to use for pairwise comparisons. Default is `"wilcox.test"`.
- multiplegroup_comparisons
Whether to add multiple group comparisons to the plot. Default is `FALSE`.
- multiple_method
Method to use for multiple group comparisons. Default is `"kruskal.test"`.
- sig_label
A string specifying the label to use for significant comparisons. Possible values are `"p.signif"` or `"p.format"`. Default is `"p.format"`.
- sig_labelsize
The size of the significant comparison labels. Default is `3.5`.
- aspect.ratio
The aspect ratio of the plot. Default is `NULL`.
- title
A string specifying the title of the plot. Default is `NULL`.
- subtitle
A string specifying the subtitle of the plot. Default is `NULL`.
- xlab
A string specifying the label of the x-axis. Default is `NULL`.
- ylab
A string specifying the label of the y-axis. Default is `"Expression level"`.
- legend.position
A string specifying the position of the legend. Possible values are `"right"`, `"left"`, `"top"`, `"bottom"`, or `"none"`. Default is `"right"`.
- legend.direction
A string specifying the direction of the legend. Possible values are `"vertical"` or `"horizontal"`. Default is `"vertical"`.
- theme_use
A string specifying the theme to use for the plot. Default is `"theme_scop"`.
- theme_args
A list of arguments to pass to the theme function. Default is an empty list.
- combine
Whether to combine the individual plots into a single plot. Default is `TRUE`.
- nrow
A number of rows for the combined plot. Default is `NULL`.
- ncol
A number of columns for the combined plot. Default is `NULL`.
- byrow
Whether to fill the combined plot by row or by column. Default is `TRUE`.
- force
Whether to force the plot creation even if there are more than 100 levels in a variable. Default is `FALSE`.
- seed
An integer specifying the random seed to use for generating jitter. Default is `11`.
Examples
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2025-11-13 11:58:56] Start standard scop workflow...
#> ℹ [2025-11-13 11:58:56] Checking a list of <Seurat> object...
#> ! [2025-11-13 11:58:56] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2025-11-13 11:58:56] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2025-11-13 11:58:58] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2025-11-13 11:58:59] Use the separate HVF from srt_list
#> ℹ [2025-11-13 11:58:59] Number of available HVF: 2000
#> ℹ [2025-11-13 11:58:59] Finished check
#> ℹ [2025-11-13 11:58:59] Perform `Seurat::ScaleData()`
#> ℹ [2025-11-13 11:59:00] Perform pca linear dimension reduction
#> StandardPC_ 1
#> Positive: Aplp1, Cpe, Gnas, Fam183b, Map1b, Hmgn3, Pcsk1n, Chga, Tuba1a, Bex2
#> Syt13, Isl1, 1700086L19Rik, Pax6, Chgb, Scgn, Rbp4, Scg3, Gch1, Camk2n1
#> Cryba2, Pcsk2, Pyy, Tspan7, Mafb, Hist3h2ba, Dbpht2, Abcc8, Rap1b, Slc38a5
#> Negative: Spp1, Anxa2, Sparc, Dbi, 1700011H14Rik, Wfdc2, Gsta3, Adamts1, Clu, Mgst1
#> Bicc1, Ldha, Vim, Cldn3, Cyr61, Rps2, Mt1, Ptn, Phgdh, Nudt19
#> Smtnl2, Smco4, Habp2, Mt2, Col18a1, Rpl12, Galk1, Cldn10, Acot1, Ccnd1
#> StandardPC_ 2
#> Positive: Rbp4, Tagln2, Tuba1b, Fkbp2, Pyy, Pcsk2, Iapp, Tmem27, Meis2, Tubb4b
#> Pcsk1n, Dbpht2, Rap1b, Dynll1, Tubb2a, Sdf2l1, Scgn, 1700086L19Rik, Scg2, Abcc8
#> Atp1b1, Hspa5, Fam183b, Papss2, Slc38a5, Scg3, Mageh1, Tspan7, Ppp1r1a, Ociad2
#> Negative: Neurog3, Btbd17, Gadd45a, Ppp1r14a, Neurod2, Sox4, Smarcd2, Mdk, Pax4, Btg2
#> Sult2b1, Hes6, Grasp, Igfbpl1, Gpx2, Cbfa2t3, Foxa3, Shf, Mfng, Tmsb4x
#> Amotl2, Gdpd1, Cdc14b, Epb42, Rcor2, Cotl1, Upk3bl, Rbfox3, Cldn6, Cer1
#> StandardPC_ 3
#> Positive: Nusap1, Top2a, Birc5, Aurkb, Cdca8, Pbk, Mki67, Tpx2, Plk1, Ccnb1
#> 2810417H13Rik, Incenp, Cenpf, Ccna2, Prc1, Racgap1, Cdk1, Aurka, Cdca3, Hmmr
#> Spc24, Kif23, Sgol1, Cenpe, Cdc20, Hist1h1b, Cdca2, Mxd3, Kif22, Ska1
#> Negative: Anxa5, Pdzk1ip1, Acot1, Tpm1, Anxa2, Dcdc2a, Capg, Sparc, Ttr, Pamr1
#> Clu, Cxcl12, Ndrg2, Hnf1aos1, Gas6, Gsta3, Krt18, Ces1d, Atp1b1, Muc1
#> Hhex, Acadm, Spp1, Enpp2, Bcl2l14, Sat1, Smtnl2, 1700011H14Rik, Tgm2, Fam159a
#> StandardPC_ 4
#> Positive: Glud1, Tm4sf4, Akr1c19, Cldn4, Runx1t1, Fev, Pou3f4, Gm43861, Pgrmc1, Arx
#> Cd200, Lrpprc, Hmgn3, Ppp1r14c, Pam, Etv1, Tsc22d1, Slc25a5, Akap17b, Pgf
#> Fam43a, Emb, Jun, Krt8, Dnajc12, Mid1ip1, Ids, Rgs17, Uchl1, Alcam
#> Negative: Ins2, Ins1, Ppp1r1a, Nnat, Calr, Sytl4, Sdf2l1, Iapp, Pdia6, Mapt
#> G6pc2, C2cd4b, Npy, Gng12, P2ry1, Ero1lb, Adra2a, Papss2, Arhgap36, Fam151a
#> Dlk1, Creld2, Gip, Tmem215, Gm27033, Cntfr, Prss53, C2cd4a, Lyve1, Ociad2
#> StandardPC_ 5
#> Positive: Pdx1, Nkx6-1, Npepl1, Cldn4, Cryba2, Fev, Jun, Chgb, Gng12, Adra2a
#> Mnx1, Sytl4, Pdk3, Gm27033, Nnat, Chga, Ins2, 1110012L19Rik, Enho, Krt7
#> Mlxipl, Tmsb10, Flrt1, Pax4, Tubb3, Prrg2, Gars, Frzb, BC023829, Gm2694
#> Negative: Irx2, Irx1, Gcg, Ctxn2, Tmem27, Ctsz, Tmsb15l, Nap1l5, Pou6f2, Gria2
#> Ghrl, Peg10, Smarca1, Arx, Lrpap1, Rgs4, Ttr, Gast, Tmsb15b2, Serpina1b
#> Slc16a10, Wnk3, Ly6e, Auts2, Sct, Arg1, Dusp10, Sphkap, Dock11, Edn3
#> ℹ [2025-11-13 11:59:01] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-11-13 11:59:01] Reorder clusters...
#> ℹ [2025-11-13 11:59:01] Perform umap nonlinear dimension reduction
#> ℹ [2025-11-13 11:59:01] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-11-13 11:59:01] UMAP will return its model
#> ℹ [2025-11-13 11:59:05] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-11-13 11:59:05] UMAP will return its model
#> ✔ [2025-11-13 11:59:08] Run scop standard workflow done
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"
)
FeatureStatPlot(
pancreas_sub,
stat.by = c("G2M_score", "Fev"),
group.by = "SubCellType",
split.by = "Phase",
add_box = TRUE,
add_trend = TRUE
)
FeatureStatPlot(
pancreas_sub,
stat.by = c("G2M_score", "Fev"),
group.by = "SubCellType",
split.by = "Phase",
comparisons = TRUE
)
#> ✔ [2025-11-13 11:59:21] ggpubr installed successfully
#> ℹ [2025-11-13 11:59:21] Detected more than 2 groups. Use "kruskal.test" for comparison
#> ℹ [2025-11-13 11:59:21] 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
)
#> ✔ [2025-11-13 11:59:23] ggpubr installed successfully
FeatureStatPlot(
pancreas_sub,
stat.by = c("Rbp4", "Pyy"),
group.by = "SubCellType",
comparisons = list(c("Alpha", "Beta"), c("Alpha", "Delta"))
)
#> ✔ [2025-11-13 11:59:25] ggpubr installed successfully
FeatureStatPlot(
pancreas_sub,
stat.by = c("Rbp4", "Pyy"),
group.by = "SubCellType",
comparisons = list(c("Alpha", "Beta"), c("Alpha", "Delta")),
sig_label = "p.format"
)
#> ✔ [2025-11-13 11:59:26] ggpubr installed successfully
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(
"Sox9", "Anxa2", "Bicc1", # Ductal
"Neurog3", "Hes6", # EPs
"Fev", "Neurod1", # Pre-endocrine
"Rbp4", "Pyy", # Endocrine
"Ins1", "Gcg", "Sst", "Ghrl" # Beta, Alpha, Delta, Epsilon
),
legend.position = "top",
legend.direction = "horizontal",
group.by = "SubCellType",
bg.by = "CellType",
stack = TRUE
)
FeatureStatPlot(
pancreas_sub,
stat.by = c(
"Sox9", "Anxa2", "Bicc1", # Ductal
"Neurog3", "Hes6", # EPs
"Fev", "Neurod1", # Pre-endocrine
"Rbp4", "Pyy", # Endocrine
"Ins1", "Gcg", "Sst", "Ghrl" # Beta, Alpha, Delta, Epsilon
),
fill.by = "feature",
plot_type = "box",
group.by = "SubCellType",
bg.by = "CellType", stack = TRUE, flip = TRUE
) |> thisplot::panel_fix_overall(
width = 8, height = 5
)
# As the plot is created by combining,
# we can adjust the overall height and width directly.
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 [2025-11-13 11:59:39] 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 [2025-11-13 11:59:40] Setting `group.by` to "Features" as `plot.by` is set to "feature"
#> ✔ [2025-11-13 11:59:40] ggpubr installed successfully
#> ✔ [2025-11-13 11:59:40] ggpubr installed successfully
#> ✔ [2025-11-13 11:59:41] ggpubr installed successfully
#> ✔ [2025-11-13 11:59:41] ggpubr installed successfully
#> ✔ [2025-11-13 11:59:41] ggpubr installed successfully
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 [2025-11-13 11:59:43] Setting `group.by` to "Features" as `plot.by` is set to "feature"
#> ✔ [2025-11-13 11:59:43] ggpubr installed successfully
#> ✔ [2025-11-13 11:59:43] ggpubr installed successfully
#> ✔ [2025-11-13 11:59:44] ggpubr installed successfully
#> ✔ [2025-11-13 11:59:44] ggpubr installed successfully
#> ✔ [2025-11-13 11:59:44] ggpubr installed successfully
FeatureStatPlot(pancreas_sub,
stat.by = c(
"Sox9", "Anxa2", "Bicc1", # Ductal
"Neurog3", "Hes6", # EPs
"Fev", "Neurod1", # Pre-endocrine
"Rbp4", "Pyy", # Endocrine
"Ins1", "Gcg", "Sst", "Ghrl" # Beta, Alpha, Delta, Epsilon
), group.by = "SubCellType",
plot.by = "feature",
stack = TRUE
)
#> ℹ [2025-11-13 11:59:46] 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
)
