dpcR (version 0.1.2-1)

qpcr2pp: qPCR to Poisson Process

Description

Selected platforms (e.g., Open Array) are real-time platforms. dPCR can be described by Poisson statistics. The function qpcr2pp takes a step further and interprets the dPCR as a Poisson process if it is analyzed as a "time" based process.

Usage

qpcr2pp(cycles, process, data = NULL, NuEvents = 1, delta = 1)

Arguments

cycles

the column containing the cycle data. Defaults to first column.

process

the column containing fluorescence values.

data

a dataframe containing the qPCR data.

NuEvents

NuEvents is "number of expected events" within a time frame (interval).

delta

is the difference "time (cycles) points" e.g., Cycle 18 and 25.

Value

• An object of qpcrpp class.

Details

PCR data derived from a qPCR experiment can be seen as a series of events over time. We define t_i as the time between the first (i - 1)^st and the i^th event. Therefore, the time $S_n$ is the sum of all $t_i$ from $i = 1$ to $i = n$. This is the time to the n^th event. $S(t)$ is the number of events in $[0, t]$. This can be seen as a Poisson process. The Poisson statistics is the central theorem to random processes in digital PCR.

The function qpcr2pp is used to model random point events in time units (PCR cycles), such as the increase of signal during a qPCR reaction in a single compartment. A Poisson process can be used to model times at which an event occurs in a "system". The qpcr2pp (quantitative Real-Time PCR to Poisson process) function transforms the qPCR amplification curve data to quantification points (Cq) which are visualized as Poisson process. This functions helps to spot differences between replicate runs of digital PCR experiments. In ideal scenarios the qpcr2pp plots are highly similar.

This tool might help to spot differences between experiments (e.g., inhibition of amplification reactions, influence of the chip arrays). The qPCR is unique because the amplification of conventional qPCRs takes place in discrete steps (cycles: 1, 2 ... 45), but the specific Cq values are calculated with continuous outcomes (Cq: 18.2, 25.7, ...). Other amplification methods such as isothermal amplifications are time based and thus better suited for Poisson process.

Examples

test <- cbind(reps[1L:45, ], reps2[1L:45, 2L:ncol(reps2)], 
	      reps3[1L:45, 2L:ncol(reps3)])

# before interpolation qPCR experiment must be converted into dPCR
Cq.range <- c(20, 30)
ranged <- limit_cq(data = test, cyc = 1, fluo = NULL,
                     Cq_range = Cq.range, model = l5)
                     
qpcr2pp(ranged[,1], ranged[,2], delta = 5)