set_calc_concentrations: Calculate concentrations for the set using contained...
In bioset: Convert a Matrix of Raw Values into Nice and Tidy Data

Description Usage Arguments Details Value See Also Examples

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).

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)

`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()`.

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

A tibble containing all original and additional columns.

Other set functions: set_calc_variability, set_read, sets_read

# 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)