Plot action potential frequency curves for a single treatment — plot_AP_frequencies_single

This function allows you to generate a plot of action potential frequency (y-axis) for each current injection (x-axis), coloured by state (baseline or post-hormone/treatment).

Usage

plot_AP_frequencies_single_treatment(
  data,
  plot_treatment,
  plot_category,
  included_sexes = "both",
  male_label = "Male",
  female_label = "Female",
  large_axis_text = "no",
  baseline_label = "Baseline",
  hormone_added,
  test_type,
  significance_display_method = "p-values",
  geom_signif_text_size = 5,
  geom_signif_y_spacer = 1,
  geom_signif_family = "",
  p_adjust_method = "holm",
  save_plot_png = "no",
  treatment_colour_theme,
  baseline_shape = 16,
  post_treatment_shape = 17,
  save_output_as_RDS = "no",
  theme_options,
  ggplot_theme = patchclampplotteR_theme()
)

Arguments

data: Action potential frequency data imported through add_new_cells() with data_type == "AP_count"
plot_treatment: A character value specifying the treatment you would like to plot (e.g. "Control"). plot_treatment represents antagonists that were present on the brain slice, or the animals were fasted, etc.
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".
large_axis_text: A character ("yes" or "no"). If "yes", a ggplot theme layer will be applied which increases the size of the axis text.
baseline_label: A character value for the x-axis label applied to the pre-hormone state. Defaults to "Baseline".
hormone_added: A character value that will be used as the label over the line annotating the period when a hormone was applied. Examples include "500 nM Insulin", "CCK + Leptin", and "Insulin". If you applied a high-frequency stimulation (HFS) protocol instead, write "HFS", and an annotation arrow will be added instead.
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.
significance_display_method: A character value ("stars" or "p-value") describing how significance values should be displayed. These annotations will not appear if test_type is "none".)
geom_signif_text_size: A numeric value describing the size of the text annotations (significance stars or p-values) on the plot. Defaults to 5.
geom_signif_y_spacer: A numeric value describing the vertical spacing applied to the significance markers. Defaults to 1, but can be set to a higher value if the p-values or significance stars are too close to the error bars.
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.
p_adjust_method: This argument is directly related to p.adjust.method in rstatix::t_test. This is the method used to adjust the p-value in multiple pairwise comparisons. Allowed values include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none" (although "none" is not recommended).
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.
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.
baseline_shape: A numeric value describing the shape used for the baseline data. Defaults to 16, which is a circle.
post_treatment_shape: A numeric value describing the shape used for the post-treatment/post-protocol data. Defaults to 17, which is a triangle.
save_output_as_RDS: A character ("yes" or "no") describing if the resulting object should be saved as an RDS file in the folder "Data/Output-Data-from-R". The function will automatically create this folder if it doesn't already exist.
theme_options: A dataframe containing theme options. See sample_theme_options for an example of what this dataframe should look like.
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 == "yes", it will also generate a .png file in the folder Figures/Action-potentials relative to the project directory, with the treatment and category included in the filename.

References

Nutter B (2018). lazyWeave: LaTeX Wrappers for R #' Users. R package version 3.0.2, https://CRAN.R-project.org/package=lazyWeave.

Examples

plot_AP_frequencies_single_treatment(
  data = sample_AP_count_data,
  plot_treatment = "Control",
  plot_category = 2,
  baseline_label = "Baseline",
  hormone_added = "Insulin",
  included_sexes = "both",
  significance_display_method = "stars",
  treatment_colour_theme = sample_treatment_names_and_colours,
  large_axis_text = "no",
  test_type = "wilcox.test",
  p_adjust_method = "holm",
  theme_options = sample_theme_options,
  save_plot_png = "no"
)