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()
. Ifcurrent_type
is "eEPSC", this must be the$summary_data
element of the list produced bymake_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_treatment
represents 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 inlist_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 inmake_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 indata
- 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:
state
A 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_interval
andpost_hormone_interval
.mean_cv_inverse_square
The mean inverse coefficient of variation squared within a specific state and sex.mean_VMR
The mean variance-to-mean ratio within a specific state and sex.
See also
plot_variance_comparison_data()
to plot this data.
Examples
make_variance_data(
data = sample_summary_eEPSC_df$summary_data,
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 × 25
#> # Groups: treatment, state, sex [16]
#> 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 18 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>,
#> # days_alone <fct>, animal_or_slicing_problems <fct>, state <chr>,
#> # mean_cv_inverse_square <dbl>, mean_VMR <dbl>