Plot repertoire diversity

Plot repertoire diversity

Usage

plot_diversity(
  input,
  data_col,
  cluster_col = NULL,
  group_col = NULL,
  method = abdiv::simpson,
  downsample = FALSE,
  n_boots = 0,
  chain = NULL,
  chain_col = global$chain_col,
  sep = global$sep,
  plot_colors = NULL,
  plot_lvls = names(plot_colors),
  panel_nrow = NULL,
  panel_scales = "free",
  n_label = NULL,
  p_label = "all",
  p_method = NULL,
  p_file = NULL,
  label_params = list(),
  ...
)

Arguments

input

Single cell object or data.frame containing V(D)J data. If a data.frame is provided, the cell barcodes should be stored as row names.

data_col

meta.data column containing values to use for calculating diversity, e.g. 'clonotype_id'

cluster_col

meta.data column containing cluster IDs to use for grouping cells when calculating clonotype abundance

group_col

meta.data column to use for grouping clusters present in cluster_col

method

Function to use for calculating diversity, e.g. abdiv::simpson. A named list of functions can be passed to plot multiple diversity metrics, e.g. list(simpson = abdiv::simpson, shannon = abdiv::shannon)

downsample

Downsample clusters to the same size when calculating diversity metrics

n_boots

Number of bootstrap replicates for calculating standard deviation, if n_boots is 0 this will be skipped.

chain

Chain to use for calculating diversity. To calculate diversity for a single chain, the column passed to the data_col argument must contain per-chain data such as CDR3 sequences. Set to NULL to include all chains.

chain_col

meta.data column containing chains for each cell

sep

Separator used for storing per-chain V(D)J data for each cell

plot_colors

Character vector containing colors for plotting

plot_lvls

Character vector containing levels for ordering

panel_nrow

The number of rows to use for arranging plot panels

panel_scales

Should scales for plot panels be fixed or free. This passes a scales specification to ggplot2::facet_wrap, can be 'fixed', 'free', 'free_x', or 'free_y'. 'fixed' will cause panels to share the same scales.

n_label

Location on plot where n label should be added, this can be any combination of the following:

• 'corner', display the total number of cells plotted in the top right corner, the position of the label can be modified by passing x and y specifications with the label_params argument

• 'axis', display the number of cells plotted for each group shown on the x-axis

• 'legend', display the number of cells plotted for each group shown in the plot legend

• 'none', do not display the number of cells plotted

p_label

Specification indicating how p-values should be labeled on plot, this can one of the following:

• 'none', do not display p-values

• 'all', show p-values for all groups

• A named vector providing p-value cutoffs and labels to display, e.g. c('*' = 0.05, '**' = 0.01, '***' = 0.001). The keyword 'value' can be used to display the p-value for those less than a certain cutoff, e.g. c(value = 0.05, ns = 1.1) will show significant p-values, all others will be labeled 'ns'.

p_method

Method to use for calculating p-values, by default when comparing two groups a t-test will be used. When comparing more than two groups the Kruskal-Wallis test will be used. p-values are adjusted for multiple testing using Bonferroni correction. Possible methods include:

• 't', two sample t-test performed with stats::t.test()

• 'wilcox', Wilcoxon rank sum test performed with stats::wilcox.test()

• 'kruskal', Kruskal-Wallis test performed with stats::kruskal.test()

p_file

File path to save table containing p-values for each comparison.

label_params

Named list providing additional parameters to modify n label aesthetics, e.g. list(size = 4, color = "red")

...

Additional arguments to pass to ggplot2, e.g. color, fill, size, linetype, etc.

Value

ggplot object

See also

calc_diversity(), plot_rarefaction()

Examples

# Specify method to use for calculating repertoire diversity
plot_diversity(
  vdj_sce,
  data_col = "clonotype_id",
  method   = abdiv::shannon
)


# Plot diversity separately for each cell cluster
plot_diversity(
  vdj_sce,
  data_col    = "clonotype_id",
  cluster_col = "orig.ident"
)


# Plot multiple diversity metrics
mets <- list(
  simpson = abdiv::simpson,
  shannon = abdiv::shannon
)

plot_diversity(
  vdj_sce,
  data_col    = "clonotype_id",
  cluster_col = "orig.ident",
  method      = mets
)


# Specify how to organize panels when plotting multiple metrics
plot_diversity(
  vdj_sce,
  data_col   = "clonotype_id",
  method     = mets,
  panel_nrow = 2
)