R/dpcR-package.R

In dpcR: Digital PCR Analysis

#' Digital PCR Analysis
#' 
#' 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- and heterozygosity, as well as 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 relating it to the total number of compartments by 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 for droplet digital PCRs and
#' array-based digital PCRs. We aim to include all
#' statistical approaches published in peer-review literature and additional
#' selected sources of expertise currently available. We intent to make these methods 
#' available to the scientific community in an open and cross-platform environment. Using
#' the naming convention derived from the MIQE Guidelines for digital PCR, we hope to 
#' become a reference to a unified nomenclature in dpcR. 
#' 
#' The package is primarily targeted at researchers who wish 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 to 
#' better learn about digital PCR concepts and data interpretation.
#' 
#' @name dpcR-package
#' @aliases dpcR-package dpcR
#' @docType package
#' @author Michal Burdukiewicz, Stefan Roediger.
#' 
#' Maintainer: Michal Burdukiewicz <michalburdukiewicz@@gmail.com>
#' @seealso \link[qpcR]{qpcR.news}, \url{http://michbur.github.io/pcRuniveRsum/}.
#' @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 \emph{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, \emph{Digital PCR}. PNAS, 1999. 96(16): p.
#' 9236-9241. %%MORE REFERENCES
#' @keywords package
#' @import methods
#' @importFrom grDevices adjustcolor col2rgb rainbow rgb 
#' @importFrom graphics abline axis box hist legend lines par points polygon rect symbols text 
#' @importFrom stats aggregate dbinom dpois glm integrate mad median p.adjust predict prop.test 
#' qnorm quantile quasibinomial quasipoisson rnorm sd smooth.spline symnum t.test var binomial 
#' pchisq vcov 
#' @importFrom utils combn getFromNamespace head read.csv read.table setTxtProgressBar tail txtProgressBar 
#' unzip
#' @importFrom binom binom.confint
#' @importFrom chipPCR inder
#' @importFrom dgof ks.test cvm.test
#' @importFrom e1071 skewness kurtosis
#' @importFrom multcomp cld glht mcp
#' @importFrom qpcR eff efficiency pcrfit modlist
#' @importFrom pracma findpeaks
#' @importFrom rateratio.test rateratio.test
#' @importFrom readxl read_excel
#' @importFrom shiny runApp
#' @importFrom signal sgolayfilt
#' @importFrom spatstat ppp quadrat.test
#' @examples
#' 
#' adpcr <- sim_adpcr(m = 400, n = 765, times = 20, pos_sums = FALSE, n_panels = 1)
#' plot_panel(adpcr, col = "green")
#' pos_chambers <- sum(adpcr > 0)
#' dpcr_density(k = pos_chambers, n = 765)
#' 
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