sim_adpcr: Simulate Array Digital PCR
In dpcR: Digital PCR Analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

A function that simulates results of an array digital PCR.

1	sim_adpcr(m, n, times, n_panels = 1, dube = FALSE, pos_sums = FALSE)

`m`	the total number of template molecules added to the plate. Must be a positive integer.
`n`	the number of chambers per plate. Must be a positive integer.
`times`	number of repetitions (see Details).
`n_panels`	the number of panels that are simulated by the function. Cannot have higher value than the `times` argument.
`dube`	if `TRUE`, the function is strict implementation of array digital PCR simulation (as in Dube et al., 2008). If `FALSE`, the function calculates only approximation of Dube's experiment. See Details and References.
`pos_sums`	if `TRUE`, function returns only the total number of positive (containing at least one molecule) chamber per panel. If `FALSE`, the functions returns a vector of length equal to the number of chambers. Each element of the vector represents the number of template molecules in a given chamber.

The array digital PCR is performed on plates containing many microfluidic chambers with a randomly distributed DNA template, fluorescence labels and standard PCR reagents. After the amplification reaction, performed independently in each chamber, the chambers with the fluorescence level below certain threshold are treated as negative. From differences between amplification curves of positive chambers it is possible to calculate both total number of template molecules and their approximate number in a single chamber.

The function contains two implementations of the array digital PCR simulation. First one was described in Dube at. al (2008). This method is based on random distributing m * times molecules between n * times chambers. After this step, the required number of plates is created by the random sampling of chambers without replacement. The above method is used, when the dube argument has value TRUE.

The second method treats the total number of template molecules as random variable with a normal distribution N(n, 0.05*n). The exact sum of total molecules per plate is calculated and randomly adjusted to the value of m * times. The above method is used, when the dube argument has value FALSE. This implementation is much faster than previous one, especially for big simulations. The higher the value of the argument times, the simulation result is closer to theoretical calculations.

If the pos_sums argument has value FALSE, the function returns a matrix with n rows and n_panels columns. Each column represents one plate. The type of such simulation would be "nm". If the pos_sums argument has value TRUE, the function returns a matrix with one row and n_panels columns. Each column contains the total number of positive chambers in each plate and type of simulation would be set as "tnp".

In each case the value is an object of the adpcr class.

Michal Burdukiewicz.

Dube S, Qin J, Ramakrishnan R, Mathematical Analysis of Copy Number Variation in a DNA Sample Using Digital PCR on a Nanofluidic Device. PLoS ONE 3(8), 2008.

sim_dpcr.

# Simulation of a digital PCR experiment with a chamber based technology.
# The parameter pos_sums was altered to change how the total number of positive 
# chamber per panel are returned. An alteration of the parameter has an impact 
# in the system performance.
adpcr_big <- sim_adpcr(m = 10, n = 40, times = 1000, pos_sums = FALSE, n_panels = 1000)
adpcr_small <- sim_adpcr(m = 10, n = 40, times = 1000, pos_sums = TRUE, n_panels = 1000)
# with pos_sums = TRUE, output allocates less memory
object.size(adpcr_big)
object.size(adpcr_small)

# Mini version of Dube et al. 2008 experiment, full requires replic <- 70000
# The number of replicates was reduced by a factor of 100 to lower the computation time.
replic <- 700
dube <- sim_adpcr(400, 765, times = replic, dube = TRUE, 
    pos_sums = TRUE, n_panels = replic)
mean(dube) # 311.5616
sd(dube) # 13.64159

# Create a barplot from the simulated data similar to Dube et al. 2008
bp <- barplot(table(factor(dube, levels = min(dube):max(dube))), 
	      space = 0)
lines(bp, dnorm(min(dube):max(dube), mean = 311.5, sd = 13.59)*replic, 
      col = "green", lwd = 3)

# Exact Dube's method is a bit slower than other one, but more accurate
system.time(dub <- sim_adpcr(m = 400, n = 765, times = 500, n_panels = 500, 
  pos_sums = TRUE))
system.time(mul <- sim_adpcr(m = 400, n = 765, times = 500, n_panels = 500, 
  pos_sums = FALSE))