R: Calculate concentrations for the set using contained...

set_calc_concentrations {bioset} | R Documentation

## Calculate concentrations for the set using contained calibrators.

### Description

If the data set is generated, for example by reading extinction rates or relative light units from a plate, these raw values can be converted to concentrations using data fields with known concentrations (calibrators).

### Usage

    set_calc_concentrations(data, cal_names, cal_values, col_names = name,
      col_values = value, col_target = conc, col_real = real,
      col_recov = recovery, model_func = fit_linear,
      interpolate_func = interpolate_linear)

### Arguments

`data` | A tibble containing the data.
`cal_names` | A vector of strings containing the names of the samples used as calibrators.
`cal_values` | A numeric vector with the known concentrations of those samples (must be in the same order).
`col_names` | The name of the column where the `cal_names` can be found.
`col_values` | The name of the column holding the raw values.
`col_target` | The name of the column to created for the calculated concentration.
`col_real` | The name of the column to create for the known concentrations.
`col_recov` | The name of the column to create for the recovery of the calibrators.
`model_func` | A function generating a model to fit the calibrators, e.g. `fit_linear()`, `fit_lnln()`.
`interpolate_func` | A function used to interpolate the concentrations of the other samples, based on the model, e.g. `interpolate_linear()`, `interpolate_lnln()`.

### Details

If the data set contains samples with known concentrations (calibrators) those can be used to interpolate the concentrations of the other samples.

### Value

A tibble containing all original and additional columns.

### See Also

Other set functions: `set_calc_variability`, `set_read`, `sets_read`

### Examples

    # generate data
    library("tibble")

    data <- tibble(
      name = c("CAL1", "CAL2", "CAL3", "A", "B", "C"),
      value = c(1, 5, 10, 2, 4, 6)
    )

    data

    # the known concentration of the calibrators
    cals <- c(1, 5, 10)
    names(cals) <- c("CAL1", "CAL2", "CAL3")

    set_calc_concentrations(
      data = data,
      cal_names = names(cals),
      cal_values = cals
    )

    # to set column names use notation like in dplyr / tidyverse
    # set the name of the column holding the final concentration to "my_protein"
    set_calc_concentrations(
      data = data,
      cal_names = names(cals),
      cal_values = cals,
      col_target = my_protein
    )

    ## Not run:
    # notice that col_target is given a string
    # this will fail
    set_calc_concentrations(
      data = data,
      cal_names = names(cals),
      cal_values =  cals,
      col_target = "my_protein"
    )

    ## End(Not run)

    # simulate data which has to be transformed to get a good fit
    cals <- exp(cals)
    data$value <- exp(data$value)

    # use ln-transformation on values and known concentrations prior to
    # fitting a model

    data <- set_calc_concentrations(
      data = data,
      cal_names = names(cals),
      cal_values = cals,
      model_func = fit_lnln,
      interpolate_func = interpolate_lnln
    )

    data

    # inspect goodnes of fit
    plot_lnln(data$real, data$value)

    rm(cals, data)

[Package bioset version 0.2.2 Index]