White: Digitalized Data from a Fluidigm Array
In dpcR: Digital PCR Analysis

Description Format Details Author(s) Source References Examples

These are the results data from the 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

A dataframe with 9180 rows and 10 columns.

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)
Sample: is the sample (e.g., "Ace 1:100") as described by White et al. (2009) BMC Genomics
X.1: Running index for *all* samples
Index: Index within an array
Row: Row within an array
Column: Column within an array
Area: is the area that was measured with "MicroArray Profile"
Min: is the minimum intensity of an area that was measured with "MicroArray Profile"
Max: is the maximum intensity of an area that was measured with "MicroArray Profile"
Mean: is the mean intensity of an area that was measured with "MicroArray Profile"

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.

Stefan Roediger, Michal Burdukiewcz, White et al. (2009) BMC Genomics

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

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).

str(White)
par(mfrow = c(3,3))


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))