Perform t-tests (or Wilcoxon tests) for EPSC summary plots

This function enables you to perform a series of paired t-tests (or Wilcoxon tests) comparing the mean current amplitude within each interval relative to the mean current amplitude during the baseline. This uses the pairwise_t_test or pairwise_wilcox_test functions from rstatix, with paired = TRUE and Holm's adjustment for multiple comparisons (p_adjust_method = "holm") by default. The resulting output table can also be used to apply significance stars to the plot in plot_summary_current_data().

Usage

perform_t_tests_for_summary_plot(
  data,
  include_all_treatments = "yes",
  list_of_treatments = NULL,
  include_all_categories = "yes",
  list_of_categories = NULL,
  current_type = "eEPSC",
  parameter = "amplitude",
  baseline_interval = "t0to5",
  interval_length = 5,
  treatment_colour_theme,
  test_type = "pairwise.t.test",
  p_adjust_method = "holm",
  save_output_as_RDS = "no"
)

Arguments

data: A dataframe containing the summary data generated from make_summary_EPSC_data(). If current_type is "eEPSC", this must be the $summary_data element of the list produced by make_summary_EPSC_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.
include_all_categories: A character ("yes" or "no") specifying if the table will include data from all categories. If "no", you must specify a list of categories in list_of_categories.
list_of_categories: A list of character values describing the categories that will be in the table. Defaults to NULL, since include_all_categories is "yes" by default.
current_type: A character describing the current type. Allowed values are "eEPSC" or "sEPSC".
parameter: A character value specifying the parameter to be plotted on the y-axis. For evoked currents (current_type = "eEPSC"), the available parameter is "amplitude", which contains amplitudes normalized relative to the baseline. For spontaneous currents (current_type = "sEPSC"), the available parameters are "amplitude" (normalized currents), "raw_amplitude", "frequency" (normalized frequency) or "raw_frequency".
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().
interval_length: Length of each interval (numeric; in minutes). Used to divide the dataset into broad ranges for statistical analysis. Important! max_recording_length must be evenly divisible by interval_length. Defaults to 5.
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.
test_type: A character (must be "pairwise.wilcox.test" or "pairwise.t.test") describing the statistical model used in this function.
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_output_as_RDS: A character ("yes" or "no") describing if the resulting object should be saved as an RDS file in the raw data folder.

Value

A dataframe

References

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

Examples

perform_t_tests_for_summary_plot(
  data = sample_summary_eEPSC_df$summary_data,
  include_all_treatments = "yes",
  list_of_treatments = NULL,
  current_type = "eEPSC",
  parameter = "amplitude",
  baseline_interval = "t0to5",
  test_type = "pairwise.t.test",
  interval_length = 5,
  treatment_colour_theme = sample_treatment_names_and_colours,
  save_output_as_RDS = "no"
)
#> # A tibble: 16 × 15
#> # Groups:   category, treatment [4]
#>    group2  asterisk_time category treatment   .y.   group1    n1    n2 statistic
#>    <chr>           <dbl> <fct>    <fct>       <chr> <chr>  <int> <int>     <dbl>
#>  1 t5to10            7.5 2        Control     mean… t0to5      4     4      2.28
#>  2 t10to15          12.5 2        Control     mean… t0to5      4     4      4.89
#>  3 t15to20          17.5 2        Control     mean… t0to5      4     4      6.96
#>  4 t20to25          22.5 2        Control     mean… t0to5      4     4      8.01
#>  5 t5to10            7.5 2        HNMPA       mean… t0to5      5     5      3.1 
#>  6 t10to15          12.5 2        HNMPA       mean… t0to5      5     5      3.15
#>  7 t15to20          17.5 2        HNMPA       mean… t0to5      5     5      2.99
#>  8 t20to25          22.5 2        HNMPA       mean… t0to5      5     5      3.19
#>  9 t5to10            7.5 2        PPP         mean… t0to5      5     5      0.78
#> 10 t10to15          12.5 2        PPP         mean… t0to5      5     5      0.89
#> 11 t15to20          17.5 2        PPP         mean… t0to5      5     5      1.5 
#> 12 t20to25          22.5 2        PPP         mean… t0to5      5     5      1.96
#> 13 t5to10            7.5 2        PPP_and_HN… mean… t0to5      5     5     -0.59
#> 14 t10to15          12.5 2        PPP_and_HN… mean… t0to5      5     5      0.94
#> 15 t15to20          17.5 2        PPP_and_HN… mean… t0to5      5     5      0.84
#> 16 t20to25          22.5 2        PPP_and_HN… mean… t0to5      5     5      1.39
#> # ℹ 6 more variables: df <dbl>, p <dbl>, p.adj <dbl>, p.adj.signif <chr>,
#> #   p_string <chr>, significance_stars <chr>

Usage

Arguments

Value

References

See also

Examples