The qpcR library is an extension to the R environment that assists in the modelling and analysis of quantitative real-time PCR data.

With the qpcR library you can:

• Fit sigmoidal (three-, four-, five-, six- and seven-parameter) models to the raw fluorescence data and display the curves with various options.
  
  
• Calculate essential PCR parameters (efficiency, threshold cycles, initial template fluorescence F₀) from the sigmoidal fits and display comprehensive graphics.
  
  
• Conduct a model selection process in which the best sigmoidal model is chosen by nested F-tests on the residual variance or other criteria such as Likelihood ratio, Akaike weights or reduced chi-square.
  
  
• Derive values from more classical quantitation methods, such as the ‘window-of-linearity’ method (also with baseline optimization), exponential fitting of the identified exponential region or a calibration curve from diluted samples.
  
  
• In calibration curve analysis, find the threshold fluorescence value which maximizes the linearity of the dilution curve 'threshold cycles'.
  
  
• Further optimize the fitting process by eliminating cycles in the ground and plateau phase, using all possible combinations.
  
  
• Calculate many measures for the goodness-of-fit, such as the residual variance, R², adjusted R², Akaike Information Criterion (AIC), corrected AIC (AICc), Bayesian Information Criterion (BIC), root-mean-squared-error (RMSE), Allen's PRESS statistic and reduced chi-square. 
  
  
• Make goodness-of-fit tests such as 'lack-of-fit' or Neill's test for non-replicates.
  
  
• Do a batch analysis of many runs with all methods (this often reveals dramatic differences in the estimated parameters!).
  
  
• Predict either fluorecence or cycle values from data.
  
  
• Calculate the goodness-of-fit (by means of RMSE) of all different sigmoidal models within the exponential region of the qPCR curve.
  
  
• Conduct gaussian error propagation with Monte Carlo simulation using multivariate normal distributions if a covariance matrix is given.
  
  
• Calculate ratios and their propagated errors for qPCR runs, using single or replicated data. If reference PCRs are supplied, the ratios are normalized against these.
  
  
• Calculate ratios with a permutation approach such as in the popular REST software.
  
  
• Build an averaged model from several housekeeping PCRs.
  
  
• Calculate the Cy0 value as described in Guescini et al and do a maxRatio analysis as in Shain et al.
  
  
• Bootstrap qPCR data and obtain confidence intervals for all estimated parameters, including those from efficiency and threshold cycle analysis.
  
  
• Simulate qPCR curves starting from a fitted curve and including defined homo/heteroscedastic noise.
  
  
• Do automatic plotting of large-scale batch PCRs by using 3D-plots or plot matrices.
  
  
• Identify deviating qPCR runs within a group of replicates by Kinetic Outlier Detection and non-replicated runs by Sigmoidal Outlier Detection .
  
  
• Conduct batch ratio analysis from 96- or 384-well plates that contain different numbers of control/treatment samples or gene-of-interests/reference genes with automatic sample recognition from the column headers.
  
  
• Do a complete melting curve analysis of qPCR runs, including graphical display of melt curves, automatic T_m identification of the products and peak area calculation.
  
  
• Import PCR data from all kinds of systems by a sophisticated import function which queries several formatting steps and keeps these stored on the hard drive for subsequent analysis of future runs or batch analysis of all files in a directory.
  
  
• Use the mechanistic 'mak2' model from Boggy et al. (2010) to calculate the initial template fluorescence D₀ of any qPCR data, making the use of efficiencies/threshold cycles expendable. In collaboration with Greg Boggy, an improved 'mak3' model was developed which has an added slope parameter and results in even better fits.