Cell statistics plot — CellStatPlot • scplotter

This function creates a plot to visualize the statistics of cells in a Seurat object, a Giotto object, a path to an .h5ad file or an opened H5File by hdf5r package. It can create various types of plots, including bar plots, circos plots, pie charts, pies (heatmap with cell_type = 'pie'), ring/donut plots, trend plots area plots, sankey/alluvial plots, heatmaps, radar plots, spider plots, violin plots, and box plots. The function allows for grouping, splitting, and faceting the data based on metadata columns. It also supports calculating fractions of cells based on specified groupings.#'

Usage

CellStatPlot(
  object,
  ident = NULL,
  group_by = NULL,
  group_by_sep = "_",
  spat_unit = NULL,
  feat_type = NULL,
  split_by = NULL,
  split_by_sep = "_",
  facet_by = NULL,
  rows_by = NULL,
  columns_split_by = NULL,
  frac = c("none", "group", "ident", "cluster", "all"),
  rows_name = NULL,
  name = NULL,
  plot_type = c("bar", "circos", "pie", "pies", "ring", "donut", "trend", "area",
    "sankey", "alluvial", "heatmap", "radar", "spider", "violin", "box"),
  swap = FALSE,
  ylab = NULL,
  ...
)

Arguments

object

A Seurat object, a Giotto object, a path to h5ad file or an opened H5File (from hdf5r package) object a data frame (for internal use) containing cell metadata.

ident

The column with the cell identities. i.e. clusters. Default: NULL If NULL, the active identity of the Seurat object and the name "Identity" will be used. For 'pies', this will be used as the pie_group_by. For 'heatmap' plot, this will be used as the rows_by of the heatmap.

group_by

The column name in the meta data to group the cells. Default: NULL This should work as the columns of the plot_type: heatmap. For violin/box plot, at most 2 group_by columns are allowed and they will not be concatenated. The first one is used to break down the values in groups, and the second one works as the group_by argument in plotthis::ViolinPlot/plotthis::BoxPlot.

group_by_sep

The separator to use when combining multiple columns in group_by. Default: "_" For 'sankey'/'heatmap' plot, multiple columns will not be combined, and each of them will be used as a node.

spat_unit

The spatial unit to use for the plot. Only applied to Giotto objects.

feat_type

feature type of the features (e.g. "rna", "dna", "protein"), only applied to Giotto objects.

split_by

The column name in the meta data to split the cells. Default: NULL Each split will be plotted in a separate plot.

split_by_sep

The separator to use when combining multiple columns in split_by. Default: "_"

facet_by

The column name in the meta data to facet the plots. Default: NULL Not available for 'circos', 'sankey', and 'heatmap' plots.

rows_by

The column names in the data used as the rows of the 'pies' (heatmap with cell_type = 'pie'). Default: NULL. Only available for 'pies' plot. The values don't matter, and they only indicate the cells overlapping with the columns and distributed in different ident values.

columns_split_by

The column name in the meta data to split the columns of the 'pies'/'heatmap' plot. Default: NULL

frac

The way of calculating the fraction. Default is "none". Possible values are "group", "ident", "cluster", "all", "none". Note that the fractions are calculated in each split and facet group if split_by and facet_by are specified.

group: calculate the fraction in each group. The total fraction of the cells of idents in each group will be 1. When group-by is not specified, it will be the same as all.
ident: calculate the fraction in each ident. The total fraction of the cells of groups in each ident will be 1. Only works when group-by is specified.
cluster: alias of ident.
all: calculate the fraction against all cells.
none: do not calculate the fraction, use the number of cells instead.

rows_name

The name of the rows in the 'pies'/'heatmap' plot. Default is NULL.

name

The name of the 'pies'/'heatmap' plot, shown as the name of the main legend. Default is NULL.

plot_type

The type of plot to use. Default is "bar". Possible values are "bar", "circos", "pie", "pies", "ring"/"donut", "trend", "area", "heatmap", "sankey"/"alluvial", "radar" and "spider". 'pie' vs 'pies': 'pie' plot will plot a single pie chart for each group, while 'pies' plot will plot multiple pie charts for each group and split. 'pies' basically is a heatmap with 'cell_type = "pie"'.

swap

Whether to swap the cluster and group, that is, using group as the x-axis and cluster to fill the plot. For circos plot, when transposed, the arrows will be drawn from the idents (by ident) to the the groups (by group_by). Only works when group_by is specified. For 'pies' plot, this will swap the group_by and ident. For 'heatmap' plot, this will swap the group_by and columns_split_by. Note that this is different from flip. flip is used to transpose the plot. But swap is used to swap the x and group_by during plotting.

ylab

The y-axis label. Default is NULL.

...

Other arguments passed to the specific plot function.

For bar plot, see plotthis::BarPlot().
For circos plot, see plotthis::CircosPlot().
For pie chart, see plotthis::PieChart().
For pies plot, see plotthis::Heatmap().
For heatmap plot, see plotthis::Heatmap().
For ring/donut plot, see plotthis::RingPlot().
For trend plot, see plotthis::TrendPlot().
For area plot, see plotthis::AreaPlot().
For sankey/alluvial plot, see plotthis::SankeyPlot().
For radar plot, see plotthis::RadarPlot().
For spider plot, see plotthis::SpiderPlot().
For violin plot, see plotthis::ViolinPlot().
For box plot, see plotthis::BoxPlot().

Value

A ggplot object or a list if combine is FALSE

Details

See:

https://pwwang.github.io/scplotter/articles/Giotto_Xenium.html

for examples of using this function with a Giotto object.

And see:

https://pwwang.github.io/scplotter/articles/Working_with_anndata_h5ad_files.html

for examples of using this function with .h5ad files.

Examples

# \donttest{
# library(patchwork)
data(ifnb_sub)

# Bar plot
p1 <- CellStatPlot(ifnb_sub)
p2 <- CellStatPlot(ifnb_sub, group_by = "stim")
p3 <- CellStatPlot(ifnb_sub, group_by = "stim", palette = "Set2")
p4 <- CellStatPlot(ifnb_sub, group_by = "stim", position = "stack")
(p1 | p2) / (p3 | p4)


# Fraction of cells
p1 <- CellStatPlot(ifnb_sub, group_by = "stim", frac = "group",
                   ident = "seurat_annotations", x_text_angle = 60)
p2 <- CellStatPlot(ifnb_sub, group_by = "stim", frac = "group")
p3 <- CellStatPlot(ifnb_sub, group_by = "stim", frac = "ident",
                   position = "stack", alpha = .6)
p4 <- CellStatPlot(ifnb_sub, group_by = "stim", frac = "group",
                   swap = TRUE, position = "stack")
(p1 | p2) / (p3 | p4)


# Splitting/Facetting the plot
CellStatPlot(ifnb_sub, split_by = "stim")

CellStatPlot(ifnb_sub, facet_by = "stim")

CellStatPlot(ifnb_sub, facet_by = "stim", facet_nrow = 2)


# Circos plot
CellStatPlot(ifnb_sub, group_by = "stim", plot_type = "circos")

CellStatPlot(ifnb_sub, group_by = "stim", ident = "seurat_annotations",
             plot_type = "circos", labels_rot = TRUE)


# Pie plot
CellStatPlot(ifnb_sub, plot_type = "pie")

CellStatPlot(ifnb_sub, plot_type = "pie", split_by = "stim")


# Ring plot
CellStatPlot(ifnb_sub, plot_type = "ring", group_by = "stim",
             palette = "Spectral")
#> 'frac' is forced to 'group' for 'ring' plot.


# Trend plot
CellStatPlot(ifnb_sub, plot_type = "trend", frac = "group",
             x_text_angle = 90, group_by = c("stim", "seurat_annotations"))
#> Multiple columns are provided in 'group_by'. They will be concatenated into one column.


# Sankey plot
CellStatPlot(ifnb_sub, plot_type = "sankey", group_by = c("seurat_clusters", "stim"),
             links_alpha = .6)
#> Missing alluvia for some stratum combinations.

CellStatPlot(ifnb_sub, plot_type = "sankey", links_alpha = .6,
             group_by = c("stim", "seurat_annotations", "orig.ident"))
#> Missing alluvia for some stratum combinations.

CellStatPlot(ifnb_sub, plot_type = "sankey", links_alpha = .6,
             group_by = c("seurat_clusters", "stim", "seurat_annotations", "orig.ident"))
#> Missing alluvia for some stratum combinations.


# Area plot
CellStatPlot(ifnb_sub, plot_type = "area", frac = "group", x_text_angle = 90,
             group_by = "seurat_annotations", split_by = "stim")


# Pies
# Simulate some sets of cells (e.g. clones)
ifnb_sub$r1 <- ifelse(ifnb_sub$seurat_clusters %in% c("0", "1", "2"), 1, 0)
ifnb_sub$r2 <- sample(c(1, 0), ncol(ifnb_sub), prob = c(0.5, 0.5), replace = TRUE)
ifnb_sub$r3 <- sample(c(1, 0), ncol(ifnb_sub), prob = c(0.7, 0.3), replace = TRUE)
CellStatPlot(ifnb_sub, plot_type = "pies", group_by = "stim", rows_name = "Clones",
   rows_by = c("r1", "r2", "r3"), show_row_names = TRUE, add_reticle = TRUE,
   show_column_names = TRUE, column_names_side = "top", cluster_columns = FALSE,
   row_names_side = "right", pie_size = "identity", pie_values = "sum")


# Heatmap
CellStatPlot(ifnb_sub, plot_type = "heatmap", group_by = "stim", palette = "Blues")

CellStatPlot(ifnb_sub, plot_type = "heatmap", group_by = "stim",
   frac = "group", columns_split_by = "seurat_annotations", swap = TRUE)


# Radar plot/Spider plot
pr <- CellStatPlot(ifnb_sub, plot_type = "radar", group_by = "stim")
ps <- CellStatPlot(ifnb_sub, plot_type = "spider", group_by = "stim")
pr | ps


# Box/Violin plot
ifnb_sub$group <- sample(paste0("g", 1:10), nrow(ifnb_sub), replace = TRUE)
CellStatPlot(ifnb_sub, group_by = c("group", "stim"), frac = "group",
   plot_type = "violin", add_box = TRUE, ident = "seurat_annotations",
   x_text_angle = 60, comparisons = TRUE, aspect.ratio = 0.8)
#> Warning: Computation failed in `stat_pwc()`.
#> Caused by error in `mutate()`:
#> ℹ In argument: `data = map(.data$data, .f, ...)`.
#> Caused by error in `map()`:
#> ℹ In index: 13.
#> Caused by error in `utils::combn()`:
#> ! n < m
#> Warning: Computation failed in `stat_pwc()`.
#> Caused by error in `mutate()`:
#> ℹ In argument: `data = map(.data$data, .f, ...)`.
#> Caused by error in `map()`:
#> ℹ In index: 13.
#> Caused by error in `utils::combn()`:
#> ! n < m

# }