plot_severity_vs_nphenotypes: Plot severity vs. number of phenotypes

In neurogenomics/MultiEWCE: Multi-Scale Target Explorer

Plot severity vs. number of phenotypes

Description

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.

Usage

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)

neurogenomics/MultiEWCE documentation built on April 17, 2025, 9:27 p.m.