R/summary_dpcr.R

In dpcR: Digital PCR Analysis

#' Methods for Function \code{summary}
#' 
#' Expands function \code{\link[base]{summary}} allowing printing summaries
#' objects of the class \code{\linkS4class{adpcr}} to or
#' \code{\linkS4class{dpcr}}.
#' 
#' The function prints a summary of the dPCR reaction, including k (number of
#' positive chambers), n (total number of chambers), estimated lambda and 
#' concentration, as well as confidence intervals for the
#' last two variables.
#' 
#' @name summary-methods
#' @aliases summary-methods summary,adpcr-method summary,dpcr-method summary
#' summary.adpcr summary.dpcr summary,dpcr-method summary.dpcr
#' @docType methods
#' @param object object of class \code{\linkS4class{adpcr}},
#' \code{\linkS4class{dpcr}} or \code{\linkS4class{qdpcr}}.
#' @param print if \code{FALSE}, no output is printed.
#' @return The data frame with estimated values of lambda, m and corresponding
#' confidence intervals.
#' @note If summary is used on an object containing results of many
#' experiments, all experiments would be independently summarized. Currently
#' supported only for objects of class \code{\linkS4class{adpcr}}.
#' @author Michal Burdukiewicz, Stefan Roediger.
#' @references Bhat S, Herrmann J, Corbisier P, Emslie K, \emph{ Single
#' molecule detection in nanofluidic digital array enables accurate measurement
#' of DNA copy number}.  Analytical and Bioanalytical Chemistry 2 (394), 2009.
#' 
#' Dube S, Qin J, Ramakrishnan R, \emph{Mathematical Analysis of Copy Number
#' Variation in a DNA Sample Using Digital PCR on a Nanofluidic Device}.  PLoS
#' ONE 3(8), 2008.
#' 
#' @keywords methods utilities
#' @examples
#' 
#' # array dpcr
#' # Simulates a chamber based digital PCR with m total number of template molecules  
#' # and n number of chambers per plate and assigns it as object ptest of the class 
#' # adpcr for a single panel. The summary function on ptest gets assigned to summ 
#' # and the result with statistics according to Dube et al. 2008 and Bhat et al. 2009 
#' # gets printed.
#' ptest <- sim_adpcr(m = 400, n = 765, times = 5, dube = FALSE, n_panels = 1)
#' summ <- summary(ptest) #save summary
#' print(summ)
#' 
#' # multiple experiments
#' # Similar to the previous example but with five panels
#' ptest <- sim_adpcr(m = 400, n = 765, times = 5, dube = FALSE, n_panels = 5)
#' summary(ptest)
#' 
#' # droplet dpcr - fluorescence
#' # Simulates a droplet digital PCR with m = 7 total number of template molecules 
#' # and n = 20 number of  droplets. The summary function on dropletf gives the 
#' # statistics according to Dube et al. 2008 and Bhat et al. 2009. The fluo parameter 
#' # is used to change the smoothness of the fluorescence curve and the space between
#' #  two consecutive measured peaks (droplets).
#' dropletf <- sim_dpcr(m = 7, n = 20, times = 5, fluo = list(0.1, 0))
#' summary(dropletf)
#' 
#' # droplet dpcr - number of molecules
#' # Similar to the previous example but with five panels but without and modifications
#' #  to the peaks.
#' droplet <- sim_dpcr(m = 7, n = 20, times = 5)
#' summary(droplet)
#' 
#' # Visualize the results of dropletf and dropletf
#' # The curves of dropletf are smoother.
#' par(mfrow = c(1,2))
#' plot(dropletf, main = "With fluo parameter", type = "l")
#' plot(droplet, main = "Without fluo parameter", type = "l")
#' 
#' 
NULL


setMethod("summary", signature(object = "dpcr"), function(object, print = TRUE) {
  data <- slot(object, ".Data")

  col_dat <- ncol(data)
  type <- slot(object, "type")
  n <- slot(object, "n")
  
  if (type %in% c("ct"))
    stop(paste0("'summary' is currently not implemented for data type ", type, "."))
  
  
  if(class(object) == "adpcr")
    if (type %in% c("fluo"))
      stop(paste0("'summary' is currently not implemented for data type ", type, "."))
  
  if(class(object) == "dpcr")
    if (type %in% c("fluo")) 
      k <- apply(data, 2, function(x) get_k_n(x, slot(object, "threshold")))
  
  if (type %in% c("nm", "np"))
    k <- colSums(data > 0, na.rm = TRUE)
  
  if (type %in% c("tnp")) 
    k <- data
  
  invisible(print_summary(k = as.vector(k), col_dat = col_dat, n = as.vector(n), 
                          print = print, run_names = colnames(data), 
                          exper_names = slot(object, "exper"),
                          replicate_names = slot(object, "replicate"),
                          assay_names = slot(object, "assay"),
                          v = slot(object, "v"),
                          uv = slot(object, "uv")))
})