dpcR (version 0.5)

White: Digitalized Data from a Fluidigm Array

Description

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

Format

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"

Details

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 ([e-mail]) and Wayne Rasband.

Annotation: See the White et al. (2009) BMC Genomics paper for details.

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).

Examples

# NOT RUN {
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))

# }