StatPlot

Visualizes data using various plot types such as bar plots, rose plots, ring plots, pie charts, trend plots, area plots, dot plots, sankey plots, chord plots, venn diagrams, and upset plots.

Usage

StatPlot(
  meta.data,
  stat.by,
  group.by = NULL,
  split.by = NULL,
  bg.by = NULL,
  flip = FALSE,
  NA_color = "grey",
  NA_stat = TRUE,
  keep_empty = FALSE,
  individual = FALSE,
  stat_level = NULL,
  plot_type = c("bar", "rose", "ring", "pie", "trend", "area", "dot", "sankey", "chord",
    "venn", "upset"),
  stat_type = c("percent", "count"),
  position = c("stack", "dodge"),
  palette = "Paired",
  palcolor = NULL,
  alpha = 1,
  bg_palette = "Paired",
  bg_palcolor = NULL,
  bg_alpha = 0.2,
  label = FALSE,
  label.size = 3.5,
  label.fg = "black",
  label.bg = "white",
  label.bg.r = 0.1,
  aspect.ratio = NULL,
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  legend.position = "right",
  legend.direction = "vertical",
  theme_use = "theme_scp",
  theme_args = list(),
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  force = FALSE,
  seed = 11
)

Arguments

meta.data: The data frame containing the data to be plotted.
stat.by: The column name(s) in meta.data specifying the variable(s) to be plotted.
group.by: The column name in meta.data specifying the grouping variable.
split.by: The column name in meta.data specifying the splitting variable.
bg.by: The column name in meta.data specifying the background variable for bar plots.
flip: Logical indicating whether to flip the plot.
NA_color: The color to use for missing values.
NA_stat: Logical indicating whether to include missing values in the plot.
keep_empty: Logical indicating whether to keep empty groups in the plot.
individual: Logical indicating whether to plot individual groups separately.
stat_level: The level(s) of the variable(s) specified in stat.by to include in the plot.
plot_type: The type of plot to create. Can be one of "bar", "rose", "ring", "pie", "trend", "area", "dot", "sankey", "chord", "venn", or "upset".
stat_type: The type of statistic to compute for the plot. Can be one of "percent" or "count".
position: The position adjustment for the plot. Can be one of "stack" or "dodge".
palette: The name of the color palette to use for the plot.
palcolor: The color to use in the color palette.
alpha: The transparency level for the plot.
bg_palette: The name of the background color palette to use for bar plots.
bg_palcolor: The color to use in the background color palette.
bg_alpha: The transparency level for the background color in bar plots.
label: Logical indicating whether to add labels on the plot.
label.size: The size of the labels.
label.fg: The foreground color of the labels.
label.bg: The background color of the labels.
label.bg.r: The radius of the rounded corners of the label background.
aspect.ratio: The aspect ratio of the plot.
title: The main title of the plot.
subtitle: The subtitle of the plot.
xlab: The x-axis label of the plot.
ylab: The y-axis label of the plot.
legend.position: The position of the legend in the plot. Can be one of "right", "left", "bottom", "top", or "none".
legend.direction: The direction of the legend in the plot. Can be one of "vertical" or "horizontal".
theme_use: The name of the theme to use for the plot. Can be one of the predefined themes or a custom theme.
theme_args: A list of arguments to be passed to the theme function.
combine: Logical indicating whether to combine multiple plots into a single plot.
nrow: The number of rows in the combined plot.
ncol: The number of columns in the combined plot.
byrow: Logical indicating whether to fill the plot by row or by column.
force: Logical indicating whether to force the plot even if some variables have more than 100 levels.
seed: The random seed to use for reproducible results.

Examples

data("pancreas_sub")
head(pancreas_sub@meta.data)
#>                     orig.ident nCount_RNA nFeature_RNA     S_score   G2M_score nCount_spliced nFeature_spliced
#> CAGCCGAAGCGATATA SeuratProject      10653         3295  0.33188155  0.54532743          10653             3295
#> AGTGTCATCGCCGTGA SeuratProject       4596         2053 -0.07156909 -0.08865353           4596             2053
#> GATGAAAAGTTGTAGA SeuratProject      14091         3864  0.08940628  0.77610326          14091             3864
#> CACAGTACATCCGTGG SeuratProject       5484         2510 -0.25927997 -0.25941831           5484             2510
#> CGGAGCTCATTGGGCC SeuratProject       7357         2674 -0.11764368  0.46237856           7357             2674
#> AGAGCTTGTGTGACCC SeuratProject       6498         2516 -0.11406432 -0.17830831           6498             2516
#>                  nCount_unspliced nFeature_unspliced      CellType   SubCellType Phase
#> CAGCCGAAGCGATATA             1587               1063        Ductal        Ductal   G2M
#> AGTGTCATCGCCGTGA             1199                803 Pre-endocrine Pre-endocrine    G1
#> GATGAAAAGTTGTAGA             2166               1379   Ngn3 low EP   Ngn3 low EP   G2M
#> CACAGTACATCCGTGG             1339                859     Endocrine          Beta    G1
#> CGGAGCTCATTGGGCC              976                745        Ductal        Ductal   G2M
#> AGAGCTTGTGTGACCC              822                591        Ductal        Ductal    G1
StatPlot(pancreas_sub@meta.data, stat.by = "Phase", group.by = "CellType", plot_type = "bar", label = TRUE)


head(pancreas_sub[["RNA"]]@meta.features)
#>               highly_variable_genes
#> Mrpl15                        False
#> Npbwr1                         <NA>
#> 4732440D04Rik                 False
#> Gm26901                       False
#> Sntg1                          True
#> Mybl1                         False
StatPlot(pancreas_sub[["RNA"]]@meta.features, stat.by = "highly_variable_genes", plot_type = "ring", label = TRUE)
#> Warning: Removed 1 row containing missing values or values outside the scale range (`geom_col()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range (`geom_text_repel()`).


pancreas_sub <- AnnotateFeatures(pancreas_sub, species = "Mus_musculus", IDtype = "symbol", db = "GeneType")
#> Species: Mus_musculus
#> Warning: Annotation package org.Mm.eg.db does not exist.
#> Warning: Use the human annotation to create the GeneType database for Mus_musculus
#> Preparing database: GeneType
#> Convert species for the database: GeneType
#> Connect to the Ensembl archives...
#> Using the 103 version of biomart...
#> Connecting to the biomart...
#> Searching the dataset hsapiens ...
#> Connecting to the dataset hsapiens_gene_ensembl ...
#> Converting the geneIDs...
#> 25650 genes mapped with entrez_id
#> ==============================
#> 25650 genes mapped
#> 167780 genes unmapped
#> ==============================
#> Error in unnest(data = geneID_collapse, cols = colnames(geneID_collapse)[sapply(geneID_collapse,     class) %in% c("list", "AsIs")], keep_empty = FALSE): could not find function "unnest"
head(pancreas_sub[["RNA"]]@meta.features)
#>               highly_variable_genes
#> Mrpl15                        False
#> Npbwr1                         <NA>
#> 4732440D04Rik                 False
#> Gm26901                       False
#> Sntg1                          True
#> Mybl1                         False
StatPlot(pancreas_sub[["RNA"]]@meta.features,
  stat.by = "highly_variable_genes", group.by = "GeneType",
  stat_type = "count", plot_type = "bar", position = "dodge", label = TRUE, NA_stat = FALSE
)
#> Error in StatPlot(pancreas_sub[["RNA"]]@meta.features, stat.by = "highly_variable_genes",     group.by = "GeneType", stat_type = "count", plot_type = "bar",     position = "dodge", label = TRUE, NA_stat = FALSE): GeneType is not in the meta.data.

Usage

Arguments

See also

Examples