make_variance_data creates a dataframe containing variance measures at two
time points. They are the baseline period and a user-specified interval after
a hormone or protocol has been applied. The variance measures are the inverse
coefficient of variation squared and the variance-to-mean ratio (VMR). A
"before vs. after" comparison of these two variance measures is useful to
determine which mechanism is involved in modifying synaptic plasticity. For
more information, please see Huijstee & Kessels (2020).
Usage
make_variance_data(
data,
df_category,
include_all_treatments = "yes",
list_of_treatments = NULL,
baseline_interval = "t0to5",
post_hormone_interval = "t20to25",
treatment_colour_theme,
save_output_as_RDS = "no"
)Arguments
- data
A dataframe containing the summary data generated from
make_summary_EPSC_data().- df_category
A numeric value describing the experimental category. In the sample dataset for this package, 2 represents experiments where insulin was applied continuously after a 5-minute baseline period. Here,
plot_treatmentrepresents antagonists that were present on the brain slice, or the animals were fasted, etc.- 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.
- 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 indicating the name of the interval used as "after" timepoint for comparison. Defaults to "t20to25", but can be changed. Make sure that this matches an interval present in
data- 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.
- 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 containing all of the columns within the summary data (see sample_summary_eEPSC_df for a detailed description of these columns) plus three additional columns:
stateA character value describing if a data point belongs to the baseline interval ("Baseline") or an interval after a hormone or protocol has been applied ("Post-modification"). These intervals are selected frombaseline_intervalandpost_hormone_interval.mean_cv_inverse_squareThe mean inverse coefficient of variation squared within a specific state.mean_VMRThe mean variance-to-mean ratio within a specific state.
Examples
make_variance_data(
data = sample_summary_eEPSC_df,
df_category = 2,
include_all_treatments = "yes",
list_of_treatments = NULL,
baseline_interval = "t0to5",
post_hormone_interval = "t20to25",
treatment_colour_theme = sample_treatment_names_and_colours,
save_output_as_RDS = "no"
)
#> # A tibble: 38 × 23
#> # Groups: treatment, state [8]
#> category letter sex treatment interval mean_P1_transformed mean_P1_raw
#> <fct> <fct> <fct> <fct> <fct> <dbl> <dbl>
#> 1 2 AO Male Control t0to5 100 36.8
#> 2 2 AO Male Control t20to25 21.3 7.82
#> 3 2 AZ Female Control t0to5 100 44.3
#> 4 2 AZ Female Control t20to25 50.7 22.4
#> 5 2 BN Male Control t0to5 100 72.1
#> 6 2 BN Male Control t20to25 19.1 13.8
#> 7 2 L Male Control t0to5 100 77.0
#> 8 2 L Male Control t20to25 5.75 4.43
#> 9 2 BO Male HNMPA t0to5 100 87.7
#> 10 2 BO Male HNMPA t20to25 24.3 21.4
#> # ℹ 28 more rows
#> # ℹ 16 more variables: n <dbl>, sd <dbl>, cv <dbl>, se <dbl>,
#> # cv_inverse_square <dbl>, variance <dbl>, VMR <dbl>, age <dbl>,
#> # animal <dbl>, X <dbl>, Y <dbl>, time <dbl>, synapses <fct>, state <chr>,
#> # mean_cv_inverse_square <dbl>, mean_VMR <dbl>