Plot severity vs. number of phenotypes

Source: R/plot_severity_vs_nphenotypes.R

Plot the mean composite GPT severity score of all all the phenotypes each cell type is significantly associated with vs. the number of phenotypes each cell types is significantly associated with.

plot_severity_vs_nphenotypes(
  results,
  gpt_annot = HPOExplorer::gpt_annot_codify(),
  cl = get_cl(),
  q_threshold = 0.5,
  n_label = 3,
  size = 3,
  min.segment.length = 0,
  remove_subtitle = FALSE,
  remove_caption = TRUE,
  point_fill = ggplot2::alpha("white", 0.75),
  base_size = 8,
  run_prune_ancestors = FALSE,
  ...
)

Arguments

results

The cell type-phenotype enrichment results generated by gen_results and merged together with merge_results

gpt_annot

A data.table of GPT annotations.

cl

Cell Ontology object.

q_threshold

The q value threshold to subset the results by.

n_label

The number of top and bottom cell types to label in the plot. Top/bottom terms are determined by sorting on both the x and y axes.

size

Label text size.

min.segment.length

Skip drawing segments shorter than this, as unit or number. Defaults to 0.5. (Default unit is lines, but other units can be specified by passing unit(x, "units")).

remove_subtitle

Remove the formula in the subtitle. See ggscatterstats for details.

remove_caption

Remove the formula in the caption. See ggscatterstats for details.

point_fill

Fill color of the points.

base_size

base font size, given in pts.

run_prune_ancestors

Prune redundant ancestral terms if any of their descendants are present. Passes to prune_ancestors.

...

Arguments passed on to ggstatsplot::ggscatterstats

data

A data frame (or a tibble) from which variables specified are to be taken. Other data types (e.g., matrix,table, array, etc.) will not be accepted. Additionally, grouped data frames from {dplyr} should be ungrouped before they are entered as data.

x

The column in data containing the explanatory variable to be plotted on the x-axis.

y

The column in data containing the response (outcome) variable to be plotted on the y-axis.

type

A character specifying the type of statistical approach:

  • "parametric"

  • "nonparametric"

  • "robust"

  • "bayes"

You can specify just the initial letter.

conf.level

Scalar between 0 and 1 (default: 95% confidence/credible intervals, 0.95). If NULL, no confidence intervals will be computed.

bf.prior

A number between 0.5 and 2 (default 0.707), the prior width to use in calculating Bayes factors and posterior estimates. In addition to numeric arguments, several named values are also recognized: "medium", "wide", and "ultrawide", corresponding to r scale values of 1/2, sqrt(2)/2, and 1, respectively. In case of an ANOVA, this value corresponds to scale for fixed effects.

bf.message

Logical that decides whether to display Bayes Factor in favor of the null hypothesis. This argument is relevant only for parametric test (Default: TRUE).

tr

Trim level for the mean when carrying out robust tests. In case of an error, try reducing the value of tr, which is by default set to 0.2. Lowering the value might help.

digits

Number of digits for rounding or significant figures. May also be "signif" to return significant figures or "scientific" to return scientific notation. Control the number of digits by adding the value as suffix, e.g. digits = "scientific4" to have scientific notation with 4 decimal places, or digits = "signif5" for 5 significant figures (see also signif()).

results.subtitle

Decides whether the results of statistical tests are to be displayed as a subtitle (Default: TRUE). If set to FALSE, only the plot will be returned.

label.var

Variable to use for points labels entered as a symbol (e.g. var1).

label.expression

An expression evaluating to a logical vector that determines the subset of data points to label (e.g. y < 4 & z < 20). While using this argument with purrr::pmap(), you will have to provide a quoted expression (e.g. quote(y < 4 & z < 20)).

marginal

Decides whether marginal distributions will be plotted on axes using {ggside} functions. The default is TRUE. The package {ggside} must already be installed by the user.

point.args

A list of additional aesthetic arguments to be passed to the ggplot2::geom_point().

point.width.jitter,point.height.jitter

Degree of jitter in x and y direction, respectively. Defaults to 0 (0%) of the resolution of the data. Note that the jitter should not be specified in the point.args because this information will be passed to two different geoms: one displaying the points and the other displaying the *labels for these points.

point.label.args

A list of additional aesthetic arguments to be passed to ggrepel::geom_label_repel()geom used to display the labels.

smooth.line.args

A list of additional aesthetic arguments to be passed to geom_smooth geom used to display the regression line.

xsidehistogram.args,ysidehistogram.args

A list of arguments passed to respective geom_s from the {ggside} package to change the marginal distribution histograms plots.

xlab

Label for x axis variable. If NULL (default), variable name for x will be used.

ylab

Labels for y axis variable. If NULL (default), variable name for y will be used.

title

The text for the plot title.

subtitle

The text for the plot subtitle. Will work only if results.subtitle = FALSE.

caption

The text for the plot caption. This argument is relevant only if bf.message = FALSE.

ggtheme

A {ggplot2} theme. Default value is theme_ggstatsplot(). Any of the {ggplot2} themes (e.g., ggplot2::theme_bw()), or themes from extension packages are allowed (e.g., ggthemes::theme_fivethirtyeight(), hrbrthemes::theme_ipsum_ps(), etc.). But note that sometimes these themes will remove some of the details that {ggstatsplot} plots typically contains. For example, if relevant, ggbetweenstats() shows details about multiple comparison test as a label on the secondary Y-axis. Some themes (e.g. ggthemes::theme_fivethirtyeight()) will remove the secondary Y-axis and thus the details as well.

ggplot.component

A ggplot component to be added to the plot prepared by {ggstatsplot}. This argument is primarily helpful for grouped_ variants of all primary functions. Default is NULL. The argument should be entered as a {ggplot2} function or a list of {ggplot2} functions.

Examples

results <- load_example_results()
out <- plot_severity_vs_nphenotypes(results=results)
#> Translating ontology terms to ids.
#> Reading cached RDS file: phenotype_to_genes.txt
#> + Version: v2025-05-06
#> 151 phenotypes do not have matching HPO IDs.
#> Reading in GPT annotations for 16,982 phenotypes.
#> Mapping cell types to cell ontology terms.
#> Adding stage information.
#> Registered S3 method overwritten by 'ggside':
#>   method from   
#>   +.gg   ggplot2