dpcR: Digital PCR Analysis

#' Digitalized Data from a Fluidigm Array
#'
#' These are the results data from the \code{White} data as measured by the
#' UT digital PCR on Fluidigm 12.765 digital Array. The data were digtilized
#' from a supplementary figure "1471-2164-10-116-S1.pdf"
#' by White et al. (2009) BMC Genomics
#'
#' Setup: Experimental details were described be White et al. (2009) BMC
#' Genomics. The digitalization of the figure was done with imageJ and the
#' "MicroArray Profile" plugin by Bob Dougherty (rpd@@optinav.com) and
#' Wayne Rasband.
#'
#' Annotation: See the White et al. (2009) BMC Genomics paper for details.
#'
#' @name White
#' @docType data
#' @format
#' \describe{ A dataframe with 9180 rows and 10 columns.
#'  \item{Image_position}{Position of an array in the figure
#'  1471-2164-10-116-S1.pdf from White et al. (2009) BMC Genomics (e.g.,
#'  11 is the image in the first colum and the first row, 24 is second column
#'  and fourth image)}
#'  \item{Sample}{is the sample (e.g., "Ace 1:100") as described by White et
#'                al. (2009) BMC Genomics}
#'  \item{X.1}{Running index for *all* samples}
#'  \item{Index}{Index within an array}
#'  \item{Row}{Row within an array}
#'  \item{Column}{Column within an array}
#'  \item{Area}{is the area that was measured with "MicroArray Profile"}
#'  \item{Min}{is the minimum intensity of an area that was measured with
#'             "MicroArray Profile"}
#'  \item{Max}{is the maximum intensity of an area that was measured with
#'             "MicroArray Profile"}
#'  \item{Mean}{is the mean intensity of an area that was measured with
#'             "MicroArray Profile"}
#' }
#' @author Stefan Roediger, Michal Burdukiewcz, White et al. (2009) BMC Genomics
#' @references White RA, Blainey PC, Fan HC, Quake SR. Digital PCR provides
#' sensitive and absolute calibration for high throughput sequencing.
#' BMC Genomics 2009;10:116. doi:10.1186/1471-2164-10-116.
#'
#' Dougherty B, Rasband W. MicroArray Profile ImageJ Plugin n.d.
#' http://www.optinav.com/imagej.html (accessed August 20, 2015).
#'
#' @source Data were digitalized from the supplement material (Additional file
#' 1. dPCR analysis of mock library control.) "1471-2164-10-116-S1.pdf"
#' by White et al. (2009) BMC Genomics
#' @keywords datasets
#' @examples
#'
#' str(White)
#' par(mfrow = c(3, 3))
#'
# Plot the panels of the arrays (similar to the supplementary figure
# in White et al. (2009) BMC Genomics).
#'
#' White_data <- sapply(unique(White[["Image_position"]]), function(i) {
#'   White[White[["Image_position"]] == i, "Mean"]
#' })
#'
#' assays <- sapply(unique(White[["Image_position"]]), function(i) {
#'   unique(White[White[["Image_position"]] == i, "Sample"])
#' })
#'
#' White_adpcr <- create_dpcr(White_data > 115,
#'   n = 765, assay = assays,
#'   type = "np", adpcr = TRUE
#' )
#'
#' White_k <- colSums(White_data > 115)
#'
#' sapply(2:4, function(i) {
#'   plot_panel(extract_run(White_adpcr, i))
#'
#'   # Create the ECDF of the image scan data to define
#'   # a cut-off for positive and negative partitions
#'   # Plot the ECDF of the image scan data an define a cut-off
#'   plot(ecdf(White_data[, i]),
#'     main = paste0("ECDF of Image Scan Data\n", assays[i]),
#'     xlab = "Grey value", ylab = "Density of Grey values"
#'   )
#'   abline(v = 115, col = 2, cex = 2)
#'   text(80, 0.5, "User defined cut-off", col = 2, cex = 1.5)
#'
#'   # Plot the density of the dPCR experiment
#'   dpcr_density(k = White_k[i], n = 765, bars = TRUE)
#' })
#'
#' par(mfrow = c(1, 1))
#'
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