R/mblrr.R

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

#' A function to perform a Local Robust Regression in Ranges defined by Qunantile-filtering
#'
#' \code{mblrr} is a function to perform the Median based Local Robust Regression (mblrr)
#' from a quantitative PCR experiment. In detail, this function attempts to break the
#' amplification curve in two parts (head (~background) and tail (~plateau)).
#' Subsequent, a robust linear regression analysis (\code{\link{lmrob}}) is
#' preformed individually on both parts. The rational behind this analysis is
#' that the slope and intercept of an amplification curve differ in the
#' background and plateau region.
#' @return gives a \code{numeric} (S3 class, type of \code{double}) as output 
#' for the regressed regions
#' @details
#' \emph{mblrr_intercept_bg} is the intercept of the head region,
#' \emph{mblrr_slope_bg} is the slope of the head region,
#' \emph{mblrr_cor_bg} is the coefficient of correlation of the head region,
#' \emph{mblrr_intercept_pt} is the intercept of the tail region,
#' \emph{mblrr_intercept_pt} is the slope of the tail region,
#' \emph{mblrr_cor_pt} is the coefficient of correlation of the tail region
#'
#' @param x is the cycle numbers (x-axis).
#' @param y is the cycle dependent fluorescence amplitude (y-axis).
#' @param sig.level is the significance level for the correlation test.
#' @param normalize is a logical parameter, which indicates if the amplification curve
#' data should be normalized to the 99 percent quantile of the amplification curve.
#' @author Stefan Roediger, Michal Burdukiewcz
#' @keywords segmented regression
#' @examples
#'
#' # Perform an mblrr analysis on noise (negative) amplification data of qPCR data
#' # with 35 cycles.
#' library(qpcR)
#' mblrr(x=boggy[, 1], y=boggy[, 2], normalize=TRUE)
#'
#' @export mblrr
mblrr <-
  function(x, y, sig.level=0.01, normalize=FALSE) {
    res_mblrr <- c(NA, NA, NA, NA, NA, NA)

    if (normalize) y <- y / quantile(y, 0.999, na.rm = TRUE)

    res_q25 <- y < quantile(y, 0.25, na.rm = TRUE)
    res_q75 <- y > quantile(y, 0.75, na.rm = TRUE)

    res_q25_lm <- try(suppressWarnings(lmrob(y[res_q25] ~ x[res_q25])), silent = TRUE)
    res_q75_lm <- try(suppressWarnings(lmrob(y[res_q75] ~ x[res_q75])), silent = TRUE)

    res_q25_cor.test <- try(cor.test(x[res_q25], y[res_q25]), silent = TRUE)
    res_q75_cor.test <- try(cor.test(x[res_q75], y[res_q75]), silent = TRUE)

    if (inherits_error(res_q25_lm) || is.na(res_q25_lm[[1]])[1] == TRUE) {
#       mblrr_intercept_bg <- NA
#       mblrr_slope_bg <- NA
      mblrr_intercept_bg <- 0
      mblrr_slope_bg <- 0
    } else {
      mblrr_intercept_bg <- res_q25_lm$coefficients[[1]]
      mblrr_slope_bg <- res_q25_lm$coefficients[[2]]
    }

    if (inherits_error(res_q75_lm) || is.na(res_q75_lm[[1]])[1] == TRUE) {
#       mblrr_intercept_pt <- NA
#       mblrr_slope_pt <- NA
      mblrr_intercept_pt <- 0
      mblrr_slope_pt <- 0
    } else {
      mblrr_intercept_pt <- res_q75_lm$coefficients[[1]]
      mblrr_slope_pt <- res_q75_lm$coefficients[[2]]
    }



    if (inherits_error(res_q25_cor.test) || is.na(res_q25_cor.test$p.value)) {
#       res_q25_cor.test_estimate <- NA
      res_q25_cor.test_estimate <- 0
    } else {
      ifelse(res_q25_cor.test$p.value < sig.level,
        res_q25_cor.test_estimate <- res_q25_cor.test$estimate,
#         res_q25_cor.test_estimate <- NA
        res_q25_cor.test_estimate <- 0
      )
    }

    if (inherits_error(res_q75_cor.test) || is.na(res_q75_cor.test$p.value)) {
#       res_q75_cor.test_estimate <- NA
      res_q75_cor.test_estimate <- 0
    } else {
      ifelse(res_q75_cor.test$p.value < sig.level,
        res_q75_cor.test_estimate <- res_q75_cor.test$estimate,
#         res_q75_cor.test_estimate <- NA
        res_q75_cor.test_estimate <- 0
      )
    }


    res_mblrr <- c(
      mblrr_intercept_bg,
      mblrr_slope_bg,
      res_q25_cor.test_estimate,
      mblrr_intercept_pt,
      mblrr_slope_pt,
      res_q75_cor.test_estimate
    )

    names(res_mblrr) <- c(
      "mblrr_intercept_bg",
      "mblrr_slope_bg",
      "mblrr_cor_bg",
      "mblrr_intercept_pt",
      "mblrr_slope_pt",
      "mblrr_cor_pt"
    )
    res_mblrr
  }