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Import and normalize raw current data

Source: R/Process-data.R
make_normalized_EPSC_data.Rd

make_normalized_EPSC_data() creates a dataframe of evoked or spontaneous current data from a raw .csv file. The function will create a new column containing the evoked or spontaneous current amplitudes normalized relative to the mean current amplitude during the baseline period. For evoked current data, the function also adds a column for the paired pulse ratio (PPR = P2/P1, where P2 is the amplitude of the second evoked current).

Usage

make_normalized_EPSC_data(
  filename = "Data/Sample-eEPSC-data.csv",
  current_type = "eEPSC",
  min_time_value = 0,
  max_time_value = 25,
  baseline_length = 5,
  interval_length = 5,
  decimal_places = 2,
  negative_transform_currents = "yes",
  save_output_as_RDS = "no"
)

Arguments

filename

A filepath to a .csv file. See add_new_cells() for the function that will merge raw data (a .csv with 4 columns: letter, ID, P1, and P2) and a cell-characteristics.csv file (with columns for factors like animal, age, sex, synapses). Please see the section on "Required columns" below.

current_type

A character describing the current type. Allowed values are "eEPSC" or "sEPSC".

min_time_value

Minimum time value (numeric; in minutes), which defaults to 0.

max_time_value

Maximum recording length (numeric; in minutes). All data points will be filtered to time values less than or equal to this value. Defaults to 25.

baseline_length

Length of the baseline (numeric; in minutes). Refers to data collected before applying a hormone, antagonist, or a protocol like high frequency stimulation. Defaults to 5.

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.

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.

negative_transform_currents

A character ("yes" or "no") describing if P1 and P2 should be negative transformed. If "yes", the values will be multiplied by (-1). This only applies when current_type == "eEPSC" - It will be ignored for spontaneous current data.

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 that can be viewed and used for further analyses in R. New or modified columns include:

  • P1 (for evoked currents only) May be negative-transformed if negative_transform == "yes"

  • P2 (for evoked currents only) May be negative-transformed if negative_transform == "yes"

  • PPR (for evoked currents only) A numeric value that represents the paired pulse ratio (PPR) of the evoked currents, generated using dplyr::mutate(PPR = P2/P1).

  • 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.

  • baseline_range A logical value required for the baseline transformation. It is set to TRUE when time is within the baseline period (e.g. Time <= 5) and FALSE at all other times.

  • baseline_mean A numeric value representing the mean evoked current amplitude during the baseline period. There is a different baseline_mean for each letter.

  • P1_transformed (for evoked currents only) A numeric value representing the first evoked current amplitude (pA) normalized relative to the mean amplitude during the recording's baseline.

  • P2_transformed (for evoked currents only) A numeric value representing the second evoked current amplitude (pA) normalized relative to the mean amplitude during the recording's baseline.

  • amplitude_transformed (for spontaneous currents only) A numeric value representing the spontaneous current amplitude (pA) normalized relative to the mean amplitude during the recording's baseline.

Required basic columns

It doesn't matter if the data are evoked or current type - these columns should be included in your data.

  • letter A character value that is a unique identifier for a single recording. Used to link data sets for evoked or spontaneous currents and cell-characteristics.

  • synapses A character value (e.g. "Glutamate" or "GABA").

  • sex A character value (e.g. "Male" or "Female").

  • treatment A character value (e.g. "Control", "HNMPA").

  • time A numeric value that represents time in minutes. This column is autogenerated in add_new_cells().

  • ID A character value for the recording filename.

  • X A numeric value representing the x-value of the cell's location in µm. Leave this blank if you don't have this data.

  • Y A numeric value representing the y-value of the cell's location in µm. Leave this blank if you don't have this data.

  • age A numeric value representing the animal's age. Can be any value as long as the time units are consistent throughout (e.g. don't mix up days and months when reporting animal ages).

  • animal A numeric value representing the animal's ID or number.

  • category A numeric value representing the experiment type. Used to assign top-level groups for further analyses, with treatment as subgroups.

  • cell A character or numeric value representing the cell. For example, use 3.1.1 for animal #3, slice #1, cell #1.

  • R_a A list of values for the access resistance, which would have been monitored at several timepoints throughout the recording. See the documentation for the R_a column in the documentation for sample_cell_characteristics with ?sample_cell_characteristics.

  • notes An optional character column with notes about any issues, sweeps that were removed during Clampfit processing, etc.

  • days_alone A numeric value describing the number of days that the animal was left alone in a cage. This typically ranges from 0 to 2. Fasted animals will have 1 day alone.

  • animal_or_slicing_problems A character value ("yes" or "no") describing if there were any issues with the animal (for example, the animal was unusually anxious) or slicing (there were delays during the process, the slices were crumpling, etc.).

Evoked current data:

If the data are evoked currents (current_type == "eEPSC"), the data must contain the basic columns mentioned in Required basic columns plus these columns:

  • P1 A numeric value representing the amplitude of the first evoked current in pA.

  • P2 A numeric value representing the amplitude of the second evoked current in pA.

Spontaneous current data:

If the data are spontaneous currents (current_type == "sEPSC"), the data must contain the basic columns mentioned in Required basic columns plus these columns:

  • recording_num A numeric value representing the recording number. This was incorporated before we switched to concatenating all recordings into one, but it needs to remain here to prevent breaking previous projects. It should be set to 1.

  • trace A numeric value representing the trace (automatically generated in Clampfit) where the current occurred.

  • time_of_peak A numeric value representing the time of the peak in milliseconds relative to trace number. This is automatically calculated in Clampfit.

  • time A numeric value representing the absolute time when the current happened, relative to the start of the recording. This is autogenerated. See add_new_cells() for a description of how the true time value (time) is calculated from the recording_num and trace.

  • amplitude A numeric value representing the amplitude of the evoked current in pA.

See also

add_new_cells() to add new recording data to your existing raw csv. It will merge raw data (a .csv with 4 columns: letter, ID, P1, and P2) and a cell-characteristics.csv file (with columns for factors like animal, age, sex, synapses).

add_new_cells() and make_summary_EPSC_data() for functions that further process the data.

Examples

make_normalized_EPSC_data(
  filename = import_ext_data("sample_eEPSC_data.csv"),
  current_type = "eEPSC",
  min_time_value = 0,
  max_time_value = 25,
  interval_length = 5,
  baseline_length = 5,
  decimal_places = 2,
  negative_transform_currents = "yes"
)
#> # A tibble: 2,107 × 25
#> # Groups:   letter [7]
#>      x.1 letter synapses  sex   treatment  time ID          P1    P2     X     Y
#>    <dbl> <fct>  <fct>     <fct> <fct>     <dbl> <fct>    <dbl> <dbl> <dbl> <dbl>
#>  1     1 AO     Glutamate Male  Control    0    23623002  17.4  78.4    NA    NA
#>  2     2 AO     Glutamate Male  Control    0.08 23623002  24.1  58.9    NA    NA
#>  3     3 AO     Glutamate Male  Control    0.17 23623002  35.6  60.0    NA    NA
#>  4     4 AO     Glutamate Male  Control    0.25 23623002  15.8  32.3    NA    NA
#>  5     5 AO     Glutamate Male  Control    0.33 23623002  53.9  26.4    NA    NA
#>  6     6 AO     Glutamate Male  Control    0.42 23623002  45.5  74.8    NA    NA
#>  7     7 AO     Glutamate Male  Control    0.5  23623002  28.1  69.6    NA    NA
#>  8     8 AO     Glutamate Male  Control    0.58 23623002  27.8  62.0    NA    NA
#>  9     9 AO     Glutamate Male  Control    0.67 23623002  60.0  52.7    NA    NA
#> 10    10 AO     Glutamate Male  Control    0.75 23623002  46.9  60.3    NA    NA
#> # ℹ 2,097 more rows
#> # ℹ 14 more variables: age <dbl>, animal <dbl>, category <fct>, cell <chr>,
#> #   notes <lgl>, days_alone <fct>, animal_or_slicing_problems <fct>, R_a <chr>,
#> #   PPR <dbl>, interval <fct>, baseline_range <lgl>, baseline_mean <dbl>,
#> #   P1_transformed <dbl>, P2_transformed <dbl>