Make a PPR plot for multiple treatments

plot_PPR_data_multiple_treatments() creates a categorical scatter plot with experimental state (i.e. grouped as baseline/before and after) and treatment on the x-axis, and the paired-pulse ratio (PPR) on the y-axis. There are also lines connecting the "before" data point to the "after" data point for each letter. It is the same as plot_PPR_data_single_treatment() but for more than one treatment.

Usage

plot_PPR_data_multiple_treatments(
  data,
  include_all_treatments = "yes",
  list_of_treatments = NULL,
  plot_category = 2,
  included_sexes = "both",
  male_label = "Male",
  female_label = "Female",
  baseline_label = "B",
  post_hormone_label = "A",
  test_type,
  map_signif_level_values = F,
  geom_signif_family = "",
  geom_signif_text_size = 3,
  geom_signif_size = 0.3,
  treatment_colour_theme,
  theme_options,
  filename_suffix = "",
  save_plot_png = "no",
  ggplot_theme = patchclampplotteR_theme()
)

Arguments

data: Paired pulse ratio data generated from make_PPR_data().
include_all_treatments: A character ("yes" or "no") specifying if the plot will include data from all treatments. If "no", you must specify a list of treatments in list_of_treatments.
list_of_treatments: A list of character values describing the treatments that will be in the plot. Defaults to NULL, since include_all_treatments is "yes" by default.
plot_category: A numeric value specifying the category, which can be used to differentiate different protocol types. In the sample dataset for this package, plot_category == 2 represents experiments where insulin was applied continuously after a 5-minute baseline period.
included_sexes: A character value ("both", "male" or "female"). Useful if you want to have a plot with data from one sex only. Defaults to "both". If you choose a single sex, the resulting plot will have "-males-only" or "-females-only" in the file name. WARNING!! If you choose "male" or "female", you MUST ensure that the t_test_df contains data that has been filtered to only include one sex. Otherwise, the significance stars will represent both sexes and it will be inaccurate.
male_label: A character value used to describe how males are encoded in the sex column of the dataframe used in data. This MUST match the value for male data in the sex column, and it must be consistent across data sheets. Defaults to "Male".
female_label: A character value used to describe how females are encoded in the sex column of the dataframe used in data. This MUST match the value for female data in the sex column, and it must be consistent across data sheets. This must be consistent in all data sheets. Defaults to "Female".
baseline_label: A character value for the x-axis label applied to the pre-hormone state. Defaults to "Baseline".
post_hormone_label: A character value for x-axis label applied to the post-hormone or post-protocol state. Defaults to "Post-hormone" but you will likely change this to the hormone or protocol name.
test_type: A character (must be "wilcox.test", "t.test" or "none") describing the statistical model used to create a significance bracket comparing the pre- and post-hormone groups.
map_signif_level_values: A TRUE/FALSE value or a list of character values for mapping p-values. If TRUE, p-values will be mapped with asterisks (e.g. \* for p < 0.05, for p < 0.01). If FALSE, raw p-values will display. You can also insert a list of custom mappings or a function. For example, use map_signif_level_values = function(p) if (p < 0.1) {round(p, 3)} else {"ns"} to only display the p-values when they are below 0.1.
geom_signif_family: A character value describing the font family used for the p-value annotations used by ggsignif::geom_signif(). Defaults to "" (empty value, will be replaced with default system font), but can be replaced with a named font. Use a package like extrafont to load system fonts into R.
geom_signif_text_size: A numeric value describing the size of the text annotations (significance stars or p-values) on the plot. Defaults to 8.
geom_signif_size: A numeric value describing the size of the geom_signif bracket size. Defaults to 0.3, which is a good thickness for most applications.
treatment_colour_theme: A dataframe containing treatment names and their associated colours as hex values. See sample_treatment_names_and_colours for an example of what this dataframe should look like.
theme_options: A dataframe containing theme options, defaults to sample_theme_options. See sample_theme_options for an example of what this dataframe should look like and how you can customize these values.
filename_suffix: Optional character value to add a suffix to the filename of the .png file created with this plot. Could be useful if you have specified a custom list of treatments.
save_plot_png: A character ("yes" or "no"). If "yes", the plot will be saved as a .png using ggsave(). The filepath depends on the current type, but they will all go in subfolders below Figures/ in your project directory.
ggplot_theme: The name of a ggplot theme or your custom theme. This will be added as a layer to a ggplot object. The default is patchclampplotteR_theme(), but other valid entries include theme_bw(), theme_classic() or the name of a custom ggplot theme stored as an object.

Value

A ggplot object. If save_plot_png is defined as "yes", it will also generate a .png file in the folder Figures/Evoked-currents/PPR relative to the project directory.

Details

If you specify a test_type, the function will perform a paired t-test or paired wilcox test and add brackets with significance stars through ggsignif::geom_signif().

Examples

plot_PPR_data_multiple_treatments(
  data = sample_PPR_df,
  include_all_treatments = "yes",
  plot_category = 2,
  baseline_label = "B",
  post_hormone_label = "I",
  included_sexes = "both",
  test_type = "t.test",
  theme_options = sample_theme_options,
  treatment_colour_theme = sample_treatment_names_and_colours
)

Usage

Arguments

Value

Details

See also

Examples