Summarize current data per 5-min for statistical tests — make_summary_EPSC

make_summary_EPSC_data() allows you to divide data from a long recording (e.g. 30 minutes) into evenly-spaced intervals (e.g. 5 minutes). It will generate summary data like the mean current amplitude for each interval. This can be useful for inserting into statistical models to compare effect sizes across broad stretches of time. The interval length would have been previously specified in make_normalized_EPSC_data() using the interval_length argument.

Usage

make_summary_EPSC_data(
  data = patchclampplotteR::sample_raw_eEPSC_df,
  current_type = "eEPSC",
  save_output_as_RDS = "no",
  decimal_places = 2,
  baseline_interval = "t0to5",
  ending_interval = "t20to25"
)

Arguments

data: A data.frame object. If the data are evoked currents (current_type == "eEPSC"), this should be the raw evoked current data generated using make_normalized_EPSC_data(). If the data are spontaneous currents (current_type == "sEPSC"), this should be the pruned data $individual_cells dataset generated using make_pruned_EPSC_data().
current_type: A character describing the current type. Allowed values are "eEPSC" or "sEPSC".
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.
decimal_places: A numeric value indicating the number of decimal places the data should be rounded to. Used to reduce file size and prevent an incorrect representation of the number of significant digits.
baseline_interval: A character value describing the baseline interval. Defaults to "t0to5".
ending_interval: A character value describing the last interval in the recording. Useful for future plots in which you compare the percent decrease/increase in current amplitude relative to the baseline. Examples include "t20to25", "t10to15", etc.

Value

A list of three dataframes (current_type = eEPSC) or three dataframes (current_type = sEPSC). For evoked currents (current_type = "eEPSC") the first dataframe ($percent_change_data) contains the mean current amplitude for each interval, with a final column (percent_change) containing the final percent change in amplitude in the last interval relative to the mean amplitude during the baseline interval.

Most columns (age, sex, animal, etc.) come directly from the information imported through import_cell_characteristics_df(). However, there are some new columns of note.

t0to5 The mean evoked current amplitude (pA) for this cell during the period of 0 to 5 minutes.
t5to10 The mean evoked current amplitude (pA) for this cell during the period of 5 to 10 minutes.
t10to15, t15to20, tXtY etc... The mean evoked current amplitude (pA) for this cell during the period of X to Y minutes.
percent_change The percent change in evoked current amplitude in the interval t20to25 as a percentage of the mean baseline amplitude (t0to5). For example, if currents began at 100 pA during the baseline period, but were 50 pA by t20to25, the value of percent_change will be 50% or 0.50. You can also change the value of the intervals used in this calculation through the baseline_interval and ending_interval arguments.

The second dataframe (accessed through $summary_data) contains summary data such as the mean current amplitude, coefficient of variation, standard deviation, standard error, variance, variance-to-mean ratio, and inverse coefficient of variation squared for each interval.

New columns for evoked current data (current_type == "eEPSC") include:

mean_P1_transformed The amplitude of the first evoked current amplitude (% Baseline eEPSC amplitude) normalized to the mean baseline amplitude and averaged over the interval.
mean_P1_raw The amplitude of the first evoked current amplitude (pA) averaged over the interval.
n The number of datapoints used to create the averaged values. Corresponds to the number of sweeps per interval.
sd The standard deviation of the normalized evoked current data (P1_transformed).
cv The coefficient of variation of P1_transformed.
se The standard error of P1_transformed.
cv_inverse_square The inverse of the squared coefficient of variation of P1_transformed.
variance The variance of P1_transformed.
VMR The variance-to-mean ratio (VMR) of P1_transformed.
interval A character value indicating the interval that the data point belongs to. For example, interval will be "t0to5" for any data points from 0 to 5 minutes. Example values: "t0to5", "t5to10", etc.
letter, synapses, sex, treatment, etc. Unmodified columns from the original dataset describing the cell's properties.

The third dataframe (accessed with $mean_SE) contains summary statistics that will be useful for publications. It presents mean evoked current amplitudes (taken from raw P1 values) grouped by category, treatment, and sex. This will make it easy to report your findings as mean +/- SE or SD with n in publications. For example, "eEPSC amplitude decreased significantly in males (baseline: 24.1 +/- 0.11 pA, n = 6, insulin: 12.4 +/- 0.23 pA, n = 7)."

category The experiment category (please see import_cell_characteristics_df() for more details).
sex The sex of the animal
treatment The treatment applied.
n The number of data points (i.e. cells)
mean_baseline_raw_P1 The average evoked current amplitude (taken from mean_P1_raw) during the baseline_interval.
sd_baseline_raw_P1 The standard deviation of mean_baseline_raw_P1.
se_baseline_raw_P1 The standard error of mean_baseline_raw_P1. Taken by dividing sd_baseline_raw_P1 by the square root of n.
mean_ending_raw_P1 The average evoked current amplitude (taken from mean_P1_raw) during the ending_interval.
sd_ending_raw_P1 The standard deviation of mean_ending_raw_P1.
se_ending_raw_P1 The standard error of mean_ending_raw_P1. Taken by dividing sd_ending_raw_P1 by the square root of n.
VMR The variance-to-mean ratio (VMR) of P1_transformed.
interval A character value indicating the interval that the data point belongs to. For example, interval will be "t0to5" for any data points from 0 to 5 minutes. Example values: "t0to5", "t5to10", etc.
letter, synapses, sex, treatment, etc. Unmodified columns from the original dataset describing the cell's properties.

Spontaneous Current Data

Spontaneous current data results in five dataframes. The first dataframe ($summary_data) contains summary statistics for each interval, as outlined below:

mean_transformed_amplitude The average normalized spontaneous current amplitude (% Baseline sEPSC amplitude).
mean_raw_amplitude The average raw spontaneous current amplitude (pA).
n The number of datapoints used to create the average.
sd_transformed_amplitude The standard deviation of the normalized spontaneous current data (mean_transformed_amplitude).
se_transformed_amplitude The standard error of mean_transformed_amplitude.
mean_transformed_frequency The average normalized frequency (% Baseline frequency).
sd_transformed_frequency The standard deviation of mean_transformed_frequency.
se_frequency The standard error of mean_transformed_frequency.
mean_raw_frequency The average raw frequency (Hz).
letter, synapses, sex, treatment, etc. Unmodified columns from the original dataset describing the cell's properties.

The second and third dataframes contain percent change data for spontaneous current amplitude and frequency, respectively. The columns are the same as the ones produced for evoked currents (read the documentation for $percent_change_data.

The fourth and fifth dataframes contain the mean, SE, SD, and n data for spontaneous current amplitude and frequency, respectively. Read the description for the $mean_SE to learn about these columns.

Examples


# Evoked Currents
# Will return a list of three dataframes

make_summary_EPSC_data(
  data = sample_raw_eEPSC_df,
  current_type = "eEPSC",
  save_output_as_RDS = "no",
  decimal_places = 2
)
#> $percent_change_data
#> # A tibble: 19 × 17
#> # Groups:   category, treatment, letter [19]
#>    category letter sex    treatment   age animal     X     Y synapses days_alone
#>    <fct>    <fct>  <fct>  <fct>     <dbl>  <dbl> <dbl> <dbl> <fct>    <fct>     
#>  1 2        AO     Male   Control      39   17     NA    NA  Glutama… 1         
#>  2 2        AZ     Female Control      32   21    353.  332. Glutama… 0         
#>  3 2        BN     Male   Control      29   25    153.  337. Glutama… 0         
#>  4 2        BO     Male   HNMPA        32   27     NA    NA  Glutama… 0         
#>  5 2        BT     Male   HNMPA        37   30    387.  587. Glutama… 0         
#>  6 2        CG     Female HNMPA        36   35    164.  367. Glutama… 0         
#>  7 2        CZ     Male   HNMPA        28   41    297.  493. Glutama… 0         
#>  8 2        FT     Female HNMPA        39   72    153.  337. Glutama… 1         
#>  9 2        FX     Female PPP          28   74    235.  496. Glutama… 0         
#> 10 2        GF     Female PPP          36   77    217.  324. Glutama… 0         
#> 11 2        GI     Female PPP          28   81    236.  284. Glutama… 0         
#> 12 2        GK     Male   PPP          35   84    249.  574. Glutama… 0         
#> 13 2        GR     Male   PPP          34   90    314.  416. Glutama… 0         
#> 14 2        GX     Male   PPP_and_…    33   97     NA    NA  Glutama… 1         
#> 15 2        HB     Male   PPP_and_…    39  100    133.  590. Glutama… 2         
#> 16 2        HC     Male   PPP_and_…    39  100    173.  576. Glutama… 2         
#> 17 2        HG     Female PPP_and_…    34  103     NA    NA  Glutama… 0         
#> 18 2        HN     Male   PPP_and_…    38  109    289.  400. Glutama… 0         
#> 19 2        L      Male   Control      38    8.5   NA    NA  Glutama… 0         
#> # ℹ 7 more variables: animal_or_slicing_problems <fct>, t0to5 <dbl>,
#> #   t5to10 <dbl>, t10to15 <dbl>, t15to20 <dbl>, t20to25 <dbl>,
#> #   percent_change <dbl>
#> 
#> $summary_data
#> # A tibble: 95 × 22
#>    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   t5to10                  34.0       12.5 
#>  3 2        AO     Male   Control   t10to15                 17.7        6.49
#>  4 2        AO     Male   Control   t15to20                 18.6        6.85
#>  5 2        AO     Male   Control   t20to25                 21.3        7.82
#>  6 2        AZ     Female Control   t0to5                  100         44.3 
#>  7 2        AZ     Female Control   t5to10                  74.1       32.8 
#>  8 2        AZ     Female Control   t10to15                 53.7       23.8 
#>  9 2        AZ     Female Control   t15to20                 56.0       24.8 
#> 10 2        AZ     Female Control   t20to25                 50.7       22.4 
#> # ℹ 85 more rows
#> # ℹ 15 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>
#> 
#> $mean_SE
#> # A tibble: 8 × 10
#> # Groups:   category, treatment [4]
#>   category treatment     sex        n mean_baseline_raw_P1 sd_baseline_raw_P1
#>   <fct>    <fct>         <fct>  <int>                <dbl>              <dbl>
#> 1 2        Control       Female     1                 44.3               NA  
#> 2 2        Control       Male       3                 62.0               22.0
#> 3 2        HNMPA         Female     2                 61.1               23.6
#> 4 2        HNMPA         Male       3                 69.0               18.2
#> 5 2        PPP           Female     3                 60.5               19.1
#> 6 2        PPP           Male       2                 84.1               21.2
#> 7 2        PPP_and_HNMPA Female     1                 79.9               NA  
#> 8 2        PPP_and_HNMPA Male       4                 54.3               23.2
#> # ℹ 4 more variables: se_baseline_raw_P1 <dbl>, mean_ending_raw_P1 <dbl>,
#> #   sd_ending_raw_P1 <dbl>, se_ending_raw_P1 <dbl>
#> 

# Spontaneous Data
# Will return a list of three dataframes

make_summary_EPSC_data(
  data = sample_pruned_sEPSC_df$individual_cells,
  current_type = "sEPSC",
  save_output_as_RDS = "no",
  decimal_places = 2,
  baseline_interval = "t0to5",
  ending_interval = "t20to25"
)
#> $summary_data
#> # A tibble: 35 × 18
#> # Groups:   category, letter, sex, treatment [7]
#>    category letter sex    treatment interval mean_transformed_amplitude
#>    <fct>    <fct>  <fct>  <fct>     <fct>                         <dbl>
#>  1 2        AZ     Female Control   t0to5                         100. 
#>  2 2        AZ     Female Control   t5to10                         96.1
#>  3 2        AZ     Female Control   t10to15                        94.1
#>  4 2        AZ     Female Control   t15to20                        94.8
#>  5 2        AZ     Female Control   t20to25                        86.8
#>  6 2        BO     Male   HNMPA     t0to5                         100. 
#>  7 2        BO     Male   HNMPA     t5to10                         87.0
#>  8 2        BO     Male   HNMPA     t10to15                        81.9
#>  9 2        BO     Male   HNMPA     t15to20                        82.2
#> 10 2        BO     Male   HNMPA     t20to25                        82.0
#> # ℹ 25 more rows
#> # ℹ 12 more variables: mean_raw_amplitude <dbl>,
#> #   sd_transformed_amplitude <dbl>, n <dbl>, se_transformed_amplitude <dbl>,
#> #   mean_transformed_frequency <dbl>, sd_transformed_frequency <dbl>,
#> #   se_transformed_frequency <dbl>, mean_raw_frequency <dbl>, time <dbl>,
#> #   synapses <fct>, days_alone <dbl>, animal_or_slicing_problems <chr>
#> 
#> $percent_change_amplitude
#> # A tibble: 7 × 14
#> # Groups:   category, treatment, letter [7]
#>   category letter sex    treatment         n synapses  days_alone
#>   <fct>    <fct>  <fct>  <fct>         <dbl> <fct>          <dbl>
#> 1 2        AZ     Female Control           5 Glutamate          0
#> 2 2        BO     Male   HNMPA             5 Glutamate          0
#> 3 2        FX     Female PPP               5 Glutamate          0
#> 4 2        GR     Male   PPP               5 Glutamate          0
#> 5 2        GX     Male   PPP_and_HNMPA     5 Glutamate          1
#> 6 2        HC     Male   PPP_and_HNMPA     5 Glutamate          2
#> 7 2        L      Male   Control           5 Glutamate          0
#> # ℹ 7 more variables: animal_or_slicing_problems <chr>, t0to5 <dbl>,
#> #   t5to10 <dbl>, t10to15 <dbl>, t15to20 <dbl>, t20to25 <dbl>,
#> #   percent_change <dbl>
#> 
#> $percent_change_frequency
#> # A tibble: 7 × 14
#> # Groups:   category, treatment, letter [7]
#>   category letter sex    treatment         n synapses  days_alone
#>   <fct>    <fct>  <fct>  <fct>         <dbl> <fct>          <dbl>
#> 1 2        AZ     Female Control           5 Glutamate          0
#> 2 2        BO     Male   HNMPA             5 Glutamate          0
#> 3 2        FX     Female PPP               5 Glutamate          0
#> 4 2        GR     Male   PPP               5 Glutamate          0
#> 5 2        GX     Male   PPP_and_HNMPA     5 Glutamate          1
#> 6 2        HC     Male   PPP_and_HNMPA     5 Glutamate          2
#> 7 2        L      Male   Control           5 Glutamate          0
#> # ℹ 7 more variables: animal_or_slicing_problems <chr>, t0to5 <dbl>,
#> #   t5to10 <dbl>, t10to15 <dbl>, t15to20 <dbl>, t20to25 <dbl>,
#> #   percent_change <dbl>
#> 
#> $mean_SE_amplitude
#> # A tibble: 6 × 10
#> # Groups:   category, treatment [4]
#>   category treatment    sex       n mean_baseline_amplit…¹ sd_baseline_amplitude
#>   <fct>    <fct>        <fct> <int>                  <dbl>                 <dbl>
#> 1 2        Control      Fema…     1                   11.0                 NA   
#> 2 2        Control      Male      1                   29.1                 NA   
#> 3 2        HNMPA        Male      1                   12.7                 NA   
#> 4 2        PPP          Fema…     1                   14.5                 NA   
#> 5 2        PPP          Male      1                   14.1                 NA   
#> 6 2        PPP_and_HNM… Male      2                   15.3                  6.08
#> # ℹ abbreviated name: ¹mean_baseline_amplitude
#> # ℹ 4 more variables: se_baseline_amplitude <dbl>, mean_ending_amplitude <dbl>,
#> #   sd_ending_amplitude <dbl>, se_ending_amplitude <dbl>
#> 
#> $mean_SE_frequency
#> # A tibble: 6 × 10
#> # Groups:   category, treatment [4]
#>   category treatment    sex       n mean_baseline_freque…¹ sd_baseline_frequency
#>   <fct>    <fct>        <fct> <int>                  <dbl>                 <dbl>
#> 1 2        Control      Fema…     1                   7.99                 NA   
#> 2 2        Control      Male      1                   2.25                 NA   
#> 3 2        HNMPA        Male      1                  19.2                  NA   
#> 4 2        PPP          Fema…     1                   6.74                 NA   
#> 5 2        PPP          Male      1                  13.2                  NA   
#> 6 2        PPP_and_HNM… Male      2                   6.09                  3.59
#> # ℹ abbreviated name: ¹mean_baseline_frequency
#> # ℹ 4 more variables: se_baseline_frequency <dbl>, mean_ending_frequency <dbl>,
#> #   sd_ending_frequency <dbl>, se_ending_frequency <dbl>
#>