Visually compare spontaneous current parameters — plot_spontaneous_current_parameter

plot_spontaneous_current_parameter_comparison() is a useful function to see how spontaneous current amplitude or frequency change after adding a hormone. This function produces sina plots (with raw datapoints on top) for the raw amplitude (or raw frequency) for two time intervals: the baseline interval, and a user-specified interval after a hormone or other modification has been applied.

Usage

plot_spontaneous_current_parameter_comparison(
  data,
  plot_category = 2,
  plot_treatment = "Control",
  included_sexes = "both",
  facet_by_sex = "no",
  male_label = "Male",
  female_label = "Female",
  y_variable = "raw_amplitude",
  hormone_added = "Insulin",
  baseline_interval = "t0to5",
  post_hormone_interval = "t20to25",
  test_type,
  map_signif_level_values = F,
  geom_signif_family = "",
  geom_signif_text_size = 5,
  large_axis_text = "no",
  treatment_colour_theme,
  geom_signif_size = 0.5,
  theme_options,
  save_plot_png,
  ggplot_theme = patchclampplotteR_theme()
)

Arguments

data: Summary data for spontaneous currents generated using make_summary_EPSC_data() where current_type == "sEPSC".
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.
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.
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.
facet_by_sex: A character value ("yes" or "no") describing if the plots should be faceted by sex. This is only available if "included_sexes" is "both". The resulting plot will be split in two, with male data on the left and female data on the right.
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".
y_variable: A character value ("raw_amplitude" or "raw_frequency") only. Normalized amplitude and frequency are not available because all baseline values are 100.
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.
baseline_interval: A character value indicating the name of the interval used as the baseline. Defaults to "t0to5", but can be changed. Make sure that this matches the baseline interval that you set in make_normalized_EPSC_data().
post_hormone_interval: A character value specifying the interval used for the data points after a hormone or protocol was applied. This must match the post_hormone_interval used in make_variance_data().
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.
large_axis_text: A character ("yes" or "no"). If "yes", a ggplot theme layer will be applied which increases the axis text.
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.
geom_signif_size: A numeric value describing the size of the geom_signif bracket size. Defaults to 0.5, which is a good thickness for most applications.
theme_options: A dataframe containing theme options. See sample_theme_options for an example of what this dataframe should look like.
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

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_spontaneous_current_parameter_comparison(
  data = sample_summary_sEPSC_df$summary_data,
  plot_category = 2,
  plot_treatment = "Control",
  y_variable = "raw_amplitude",
  included_sexes = "both",
  facet_by_sex = "no",
  hormone_added = "Insulin",
  baseline_interval = "t0to5",
  post_hormone_interval = "t20to25",
  test_type = "wilcox.test",
  large_axis_text = "no",
  treatment_colour_theme = sample_treatment_names_and_colours,
  save_plot_png = "no",
  theme_options = sample_theme_options
)


# Facet by sex

plot_spontaneous_current_parameter_comparison(
  data = sample_summary_sEPSC_df$summary_data,
  plot_category = 2,
  plot_treatment = "Control",
  y_variable = "raw_amplitude",
  included_sexes = "both",
  facet_by_sex = "yes",
  hormone_added = "Insulin",
  baseline_interval = "t0to5",
  post_hormone_interval = "t20to25",
  test_type = "wilcox.test",
  large_axis_text = "no",
  treatment_colour_theme = sample_treatment_names_and_colours,
  save_plot_png = "no",
  theme_options = sample_theme_options
)
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: Computation failed in `stat_ydensity()`.
#> Caused by error in `$<-.data.frame`:
#> ! replacement has 1 row, data has 0
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: Groups with fewer than two datapoints have been dropped.
#> ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
#> Warning: Computation failed in `stat_ydensity()`.
#> Caused by error in `$<-.data.frame`:
#> ! replacement has 1 row, data has 0
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_segment()`).
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_segment()`).