dpcR (version 0.1.2-1)

dpcR-package: Digital PCR Analysis

Description

The dpcR package is a collection of functions for a digital Polymerase Chain Reaction (dPCR) analysis. dPCR comprises methods to quantify nucleic acids, copy number variations (CNV), homo-/heterozygosity, and rare mutations (including single nucleotide polymorphisms (SNP)). The chemical basis of dPCR is similar to conventional PCR but the reaction-mix is divided into hundredths to thousands of small compartments with parallel amplifications reactions. The analysis is based on counting the number of positive compartments and to relate it to the total number of compartments be means of Poission statistics which enables an absolute quantification. The package includes plot functions, summary functions, data sets and simulations for dPCR and customizable GUI creators for droplet digital PCRs and chamber-based digital PCRs. The authors of the package aim to include all statistical approaches published in peer-review literature and additional selected sources of expertise currently available and to make them available to the scientific community in an open and cross-platform environment. As such the dpcR packages has a list of expressions/functions and may serve in future a reference to a unified nomenclature in dpcR. The package is primarily targeted at researchers who which to use it with an existing technology or during the development of novel digital PCR systems. In addition the dpcR package provides interactive tools that can be used in classes or by individuals to better learn about digital PCR concepts and data interpretation.

Details

ll{ Package: dpcR Type: Package Version: 0.1.1 Date: 2013-09-07 License: GPL2 }

References

Huggett J, Foy CA, Benes V, Emslie K, Garson JA, Haynes R, Hellemans J, Kubista M, Mueller RD, Nolan T, Pfaffl MW, Shipley GL, Vandesompele J, Wittwer CT, Bustin SA The Digital MIQE Guidelines: Minimum Information for Publication of Quantitative Digital PCR Experiments Clinical Chemistry, 2013. 59(6): p.892-902.

Vogelstein B, Kinzler KW, Digital PCR. PNAS, 1999. 96(16): p. 9236-9241.

See Also

qpcR.news.

Examples

adpcr <- sim_adpcr(m = 400, n = 765, times = 20, pos_sums = FALSE, n_panels = 1)
plot_panel(adpcr, 45, 17, col = "green")
pos_chambers <- sum(adpcr > 0)
dpcr_density(k = pos_chambers, n = 765)