plot_diversity: Plot repertoire diversity

In rnabioco/djvdj: A collection of single-cell V(D)J tools

Plot repertoire diversity

Description

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
)

rnabioco/djvdj documentation built on Oct. 24, 2023, 7:33 p.m.