R/qPCR2fdata.R

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

#' A helper function to convert amplification curve data to the fdata format.
#'
#' \code{qPCR2fdata} is a helper function to convert qPCR data to the functional
#' \code{\link{fdata}} class as proposed by  Febrero-Bande & de la Fuente (2012). This
#' function prepares the data for further analysis with the \code{\link{fda.usc}} package,
#' which includes utilities for functional data analysis (e.g., Hausdorff
#' distance).
#' @return gives an \code{fdata} object (S3 class, type of \code{list}) as output 
#' for a converted amplification curve.
#' @param data is a data set containing the amplification cycles (1. column)
#' and the fluorescence (subsequent columns).
#' @param preprocess is a logical parameter (default FALSE). If TRUE, the \code{\link{CPP}}
#' function from the chipPCR package (Roediger et al. 2015) is used to pre-process
#' the data (e.g., imputation of missing values).
#' and the fluorescence (subsequent columns).
#' @author Stefan Roediger, Michal Burdukiewcz
#' @references M. Febrero-Bande, M.O. de la Fuente, others, \emph{Statistical
#' computing in functional data analysis: The R package fda.usc}, Journal of
#' Statistical Software. 51 (2012) 1--28. http://www.jstatsoft.org/v51/i04/
#'
#' S. Roediger, M. Burdukiewicz, P. Schierack, \emph{chipPCR: an R package to
#' pre-process raw data of amplification curves}, Bioinformatics. 31 (2015)
#' 2900--2902. doi:10.1093/bioinformatics/btv205.
#' @keywords fdata
#' @examples
#' default.par <- par(no.readonly = TRUE)
#' # Calculate slope and intercept on noise (negative) amplification curve data
#' # for the last eight cycles.
#' library(qpcR)
#' library(fda.usc)
#'
#' # Convert the qPCR data set to the fdata format
#' res_fdata <- qPCR2fdata(testdat)
#'
#' # Extract column names and create rainbow color to label the data
#' res_fdata_colnames <- colnames(testdat[-1])
#' data_colors <- rainbow(length(res_fdata_colnames), alpha=0.5)
#'
#' # Plot the converted qPCR data
#' par(mfrow=c(1,2))
#' plot(res_fdata, xlab="cycles", ylab="RFU", main="testdat", type="l",
#'                    lty=1, lwd=2, col=data_colors)
#' legend("topleft", as.character(res_fdata_colnames), pch=19,
#'          col=data_colors, bty="n", ncol=2)
#'
#' # Calculate the Hausdorff distance (fda.usc) package and plot the distances
#' # as clustered data.
#'
#' res_fdata_hclust <- metric.hausdorff(res_fdata)
#' plot(hclust(as.dist(res_fdata_hclust)), main="Clusters of the amplification\n
#'    curves as calculated by the Hausdorff distance")
#' par(default.par)
#' @export qPCR2fdata

qPCR2fdata <- function(data, preprocess=FALSE) {
  data_colnames <- colnames(data)
  if (preprocess) {
    data_tmp <- do.call(cbind, lapply(2L:ncol(data), function(i) {
      CPP(data[, 1], data[, i], bg.outliers = TRUE)$y.norm
    }))
    data <- cbind(data[, 1], data_tmp)
    colnames(data) <- data_colnames
  }
  fdata(t(data[, -1]), argvals = data[, 1], rangeval = range(data[, 1]))
}