R/encu.R

In devSJR/PCRedux: Quantitative Polymerase Chain Reaction (qPCR) Data Mining and Machine Learning Toolkit

#' A function to calculate numerous features from amplification curve data
#' from a quantitative PCR experiment.
#'
#' encu (ENcode CUrves) is a function to calculate numerous
#' features of a large amplification curve data set.
#' The \code{\link[PCRedux]{pcrfit_single}} is
#' performing the analysis for a single process.
#' 
#' @return gives a \code{data.frame} vector (S3 class, type of \code{list}) as 
#' output for features
#'
#' @param data is the data set containing the cycles and fluorescence amplitudes.
#' @param detection_chemistry contains additional meta information about the
#' detection chemistry (e.g., probes, intercalating dye) that was used.
#' @param device contains additional meta information about the qPCR system that
#' was used.
#' @return The output of the encu function is
#' identical to the \code{\link[PCRedux]{pcrfit_single}} function.
#' @author Stefan Roediger, Michal Burdukiewcz
#' @keywords slope intercept preprocessing normalization
#' @importFrom pbapply pblapply
#' @examples
#'
#' # Calculate curve features of an amplification curve data. Note that not all
#' # available CPU cores are used. If need set "all" to use all available cores.
#' # In this example the testdat data set from the qpcR package is used.
#' # The samples F1.1 and F1.2 are positive amplification curves. The samples
#' # F1.3 and F1.4 are negative.
#'
#' library(qpcR)
#' res_encu <- encu(testdat[, 1:3])
#' res_encu
#'
#' @export

encu <- function(data, detection_chemistry = NA, device = NA) {
  # Determine the number of available cores and register them

  # Prepare the data for further processing
  # Normalize RFU values to the alpha quantiles (0.999)
  cycles <- data.frame(cycles = data[, 1])
  data_RFU <- data.frame(data[, -1, drop = FALSE])
  ncol_data_RFU <- ncol(data_RFU)

  run_res <- do.call(rbind, pblapply(1L:ncol_data_RFU, function(ith_run) {
    pcrfit_single(data_RFU[, ith_run])
  }))
  
  rownames(run_res) <- NULL
  
  cbind(runs = colnames(data_RFU), run_res,
        detection_chemistry = detection_chemistry,
        device = device)
}