Statistical plot of features

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 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)
#> 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 
FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType"
) |> thisplot::panel_fix(height = 1, width = 2)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  plot_type = "box"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  plot_type = "bar"
)
#> Warning: Computation failed in `stat_summary()`.
#> Caused by error in `fun.data()`:
#> ! The package "Hmisc" is required.
#> Warning: Computation failed in `stat_summary()`.
#> Caused by error in `fun.data()`:
#> ! The package "Hmisc" is required.
#> Warning: Computation failed in `stat_summary()`.
#> Caused by error in `fun.data()`:
#> ! The package "Hmisc" is required.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: Computation failed in `stat_summary()`.
#> Caused by error in `fun.data()`:
#> ! The package "Hmisc" is required.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  plot_type = "dot"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.

FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  plot_type = "col"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  add_box = TRUE
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  add_point = TRUE
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.

FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  add_trend = TRUE
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  add_stat = "mean"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  add_line = 0.2,
  line_type = 2
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  split.by = "Phase"
)
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  split.by = "Phase",
  add_box = TRUE,
  add_trend = TRUE
)
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("G2M_score", "Fev"),
  group.by = "SubCellType",
  split.by = "Phase",
  comparisons = TRUE
)
#>  
#> → Will install 34 packages.
#> → All 34 packages (0 B) are cached.
#> + Deriv            4.2.0      
#> + Formula          1.2-5      
#> + MatrixModels     0.5-4      
#> + Rdpack           2.6.4      
#> + SparseM          1.84-2     
#> + TTR              0.24.4     
#> + car              3.1-3      
#> + carData          3.0-5      
#> + colorspace       2.1-2      
#> + corrplot         0.95       
#> + doBy             4.7.1      
#> + forecast         8.24.0     
#> + fracdiff         1.5-3      
#> + ggpubr           0.6.2      
#> + ggsci            4.1.0      
#> + ggsignif         0.6.4      
#> + lme4             1.1-38     
#> + microbenchmark   1.5.0      
#> + minqa            1.2.8       + ✔ make
#> + modelr           0.1.11     
#> + nloptr           2.2.1       + ✔ cmake
#> + numDeriv         2016.8-1.1 
#> + pbkrtest         0.5.5      
#> + polynom          1.4-1      
#> + quadprog         1.5-8      
#> + quantmod         0.4.28     
#> + quantreg         6.1        
#> + rbibutils        2.4        
#> + reformulas       0.4.2      
#> + rstatix          0.7.3      
#> + timeDate         4051.111   
#> + tseries          0.10-58    
#> + urca             1.3-4      
#> + xts              0.14.1     
#> ✔ All system requirements are already installed.
#>   
#> ℹ No downloads are needed, 34 pkgs are cached
#> ✔ Got Deriv 4.2.0 (x86_64-pc-linux-gnu-ubuntu-24.04) (152.58 kB)
#> ✔ Got MatrixModels 0.5-4 (x86_64-pc-linux-gnu-ubuntu-24.04) (408.50 kB)
#> ✔ Got Rdpack 2.6.4 (x86_64-pc-linux-gnu-ubuntu-24.04) (637.80 kB)
#> ✔ Got nloptr 2.2.1 (x86_64-pc-linux-gnu-ubuntu-24.04) (567.65 kB)
#> ✔ Got reformulas 0.4.2 (x86_64-pc-linux-gnu-ubuntu-24.04) (139.23 kB)
#> ✔ Got carData 3.0-5 (x86_64-pc-linux-gnu-ubuntu-24.04) (1.70 MB)
#> ✔ Got microbenchmark 1.5.0 (x86_64-pc-linux-gnu-ubuntu-24.04) (65.96 kB)
#> ✔ Got xts 0.14.1 (x86_64-pc-linux-gnu-ubuntu-24.04) (1.22 MB)
#> ✔ Got minqa 1.2.8 (x86_64-pc-linux-gnu-ubuntu-24.04) (122.60 kB)
#> ✔ Got car 3.1-3 (x86_64-pc-linux-gnu-ubuntu-24.04) (1.54 MB)
#> ✔ Got Formula 1.2-5 (x86_64-pc-linux-gnu-ubuntu-24.04) (159.13 kB)
#> ✔ Got numDeriv 2016.8-1.1 (x86_64-pc-linux-gnu-ubuntu-24.04) (114.36 kB)
#> ✔ Got TTR 0.24.4 (x86_64-pc-linux-gnu-ubuntu-24.04) (524.49 kB)
#> ✔ Got colorspace 2.1-2 (x86_64-pc-linux-gnu-ubuntu-24.04) (2.64 MB)
#> ✔ Got pbkrtest 0.5.5 (x86_64-pc-linux-gnu-ubuntu-24.04) (222.06 kB)
#> ✔ Got SparseM 1.84-2 (x86_64-pc-linux-gnu-ubuntu-24.04) (887.98 kB)
#> ✔ Got rstatix 0.7.3 (x86_64-pc-linux-gnu-ubuntu-24.04) (615.19 kB)
#> ✔ Got corrplot 0.95 (x86_64-pc-linux-gnu-ubuntu-24.04) (3.82 MB)
#> ✔ Got rbibutils 2.4 (x86_64-pc-linux-gnu-ubuntu-24.04) (1.15 MB)
#> ✔ Got modelr 0.1.11 (x86_64-pc-linux-gnu-ubuntu-24.04) (200.70 kB)
#> ✔ Got polynom 1.4-1 (x86_64-pc-linux-gnu-ubuntu-24.04) (406.99 kB)
#> ✔ Got quantreg 6.1 (x86_64-pc-linux-gnu-ubuntu-24.04) (1.46 MB)
#> ✔ Got ggsignif 0.6.4 (x86_64-pc-linux-gnu-ubuntu-24.04) (602.07 kB)
#> ✔ Got ggsci 4.1.0 (x86_64-pc-linux-gnu-ubuntu-24.04) (2.30 MB)
#> ✔ Got ggpubr 0.6.2 (x86_64-pc-linux-gnu-ubuntu-24.04) (2.13 MB)
#> ℹ Installing system requirements
#> ℹ Executing `sudo sh -c apt-get -y update`
#> Get:1 file:/etc/apt/apt-mirrors.txt Mirrorlist [144 B]
#> Hit:2 http://azure.archive.ubuntu.com/ubuntu noble InRelease
#> Hit:3 http://azure.archive.ubuntu.com/ubuntu noble-updates InRelease
#> Hit:4 http://azure.archive.ubuntu.com/ubuntu noble-backports InRelease
#> Hit:5 http://azure.archive.ubuntu.com/ubuntu noble-security InRelease
#> Hit:6 https://packages.microsoft.com/repos/azure-cli noble InRelease
#> Hit:7 https://packages.microsoft.com/ubuntu/24.04/prod noble InRelease
#> Reading package lists...
#> ℹ Executing `sudo sh -c apt-get -y install make cmake libcurl4-openssl-dev libssl-dev pandoc libicu-dev`
#> Reading package lists...
#> Building dependency tree...
#> Reading state information...
#> make is already the newest version (4.3-4.1build2).
#> cmake is already the newest version (3.28.3-1build7).
#> libcurl4-openssl-dev is already the newest version (8.5.0-2ubuntu10.6).
#> libssl-dev is already the newest version (3.0.13-0ubuntu3.6).
#> pandoc is already the newest version (3.1.3+ds-2).
#> libicu-dev is already the newest version (74.2-1ubuntu3.1).
#> 0 upgraded, 0 newly installed, 0 to remove and 49 not upgraded.
#> ✔ Installed car 3.1-3  (73ms)
#> ✔ Installed carData 3.0-5  (90ms)
#> ✔ Installed colorspace 2.1-2  (134ms)
#> ✔ Installed corrplot 0.95  (197ms)
#> ✔ Installed Deriv 4.2.0  (143ms)
#> ✔ Installed doBy 4.7.1  (66ms)
#> ✔ Installed forecast 8.24.0  (67ms)
#> ✔ Installed Formula 1.2-5  (65ms)
#> ✔ Installed fracdiff 1.5-3  (65ms)
#> ✔ Installed ggpubr 0.6.2  (69ms)
#> ✔ Installed ggsci 4.1.0  (123ms)
#> ✔ Installed ggsignif 0.6.4  (73ms)
#> ✔ Installed lme4 1.1-38  (71ms)
#> ✔ Installed MatrixModels 0.5-4  (68ms)
#> ✔ Installed microbenchmark 1.5.0  (65ms)
#> ✔ Installed minqa 1.2.8  (67ms)
#> ✔ Installed modelr 0.1.11  (67ms)
#> ✔ Installed nloptr 2.2.1  (119ms)
#> ✔ Installed numDeriv 2016.8-1.1  (68ms)
#> ✔ Installed pbkrtest 0.5.5  (65ms)
#> ✔ Installed polynom 1.4-1  (63ms)
#> ✔ Installed quadprog 1.5-8  (63ms)
#> ✔ Installed quantmod 0.4.28  (69ms)
#> ✔ Installed quantreg 6.1  (71ms)
#> ✔ Installed rbibutils 2.4  (123ms)
#> ✔ Installed Rdpack 2.6.4  (72ms)
#> ✔ Installed reformulas 0.4.2  (67ms)
#> ✔ Installed rstatix 0.7.3  (68ms)
#> ✔ Installed SparseM 1.84-2  (69ms)
#> ✔ Installed timeDate 4051.111  (69ms)
#> ✔ Installed tseries 0.10-58  (363ms)
#> ✔ Installed TTR 0.24.4  (356ms)
#> ✔ Installed urca 1.3-4  (65ms)
#> ✔ Installed xts 0.14.1  (47ms)
#> ✔ 1 pkg + 99 deps: kept 66, added 34, dld 25 (23.79 MB) [10.2s]
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.


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
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("Rbp4", "Pyy"),
  group.by = "SubCellType",
  comparisons = list(c("Alpha", "Beta"), c("Alpha", "Delta"))
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("Rbp4", "Pyy"),
  group.by = "SubCellType",
  comparisons = list(c("Alpha", "Beta"), c("Alpha", "Delta")),
  sig_label = "p.format"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.


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"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.


FeatureStatPlot(
  pancreas_sub,
  stat.by = c("Neurog3", "Rbp4", "Ins1"),
  group.by = "CellType",
  plot.by = "feature"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.


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
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.


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
)


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
)


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)
)
#> Warning: Different features in new layer data than already exists for scale.data
FeatureStatPlot(
  pancreas_sub,
  stat.by = c("Neurog3", "Rbp4"),
  group.by = "CellType",
  layer = "scale.data",
  ylab = "FoldChange",
  same.y.lims = TRUE,
  y.max = 4
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.

Usage

Arguments

See also

Examples