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qpcR (version 1.4-2)

Modelling and Analysis of Real-Time PCR Data

Description

Sigmoidal model fitting, optimal model selection and calculation of various features that are essential in the analysis of quantitative real-time polymerase chain reaction (qPCR), according to Ritz & Spiess (2008) and Spiess et al. (2008) .

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Version

Install

install.packages('qpcR')

Monthly Downloads

239

Version

1.4-2

License

GPL (>= 2)

Maintainer

Andrej-Nikolai Spiess

Last Published

June 10th, 2025

Functions in qpcR (1.4-2)

Calculation of qPCR efficiency using dilution curves and replicate bootstrapping

Comparison of all sigmodal models within the exponential region

Outlier summary for objects of class 'modlist' or 'replist'

Batch calculation of qPCR fit parameters/efficiencies/threshold cycles with simple output, especially tailored to high-throughput data

Calculation of Akaike weights/relative likelihoods/delta-AICs

Calculation of qPCR efficiency and other important qPCR parameters

Evidence ratio for model comparisons with AIC, AICc or BIC

The amplification efficiency curve of a fitted object

Calculation of PCR efficiency by fitting an exponential model

The chi-square goodness-of-fit

Create nonlinear models from a dataframe and coerce them into a list

Batch calculation of qPCR efficiency and other qPCR parameters

Bootstrapping and jackknifing qPCR data

Sigmoidal model selection by different criteria

Parameters that can be changed to tweak the kinetic outlier methods

Melting curve analysis with (iterative) Tm identification and peak area calculation/cutoff

Calculation of the 'midpoint' region

Calculation of likelihood ratios for nested models

The maxRatio method as in Shain et al. (2008)

Summarize measures for the goodness-of-fit

Display news and changes of qpcR package versions

Calculation of ratios in a batch format from external PCR parameters

Plotting qPCR data with fitted curves

Workhorse function for qPCR model fitting

Simulation of sigmoidal qPCR data with goodness-of-fit analysis

The nonlinear/mechanistic models implemented in qpcR

Value prediction from a fitted qPCR model

Combinatorial elimination of plateau and ground phase cycles

Error propagation using different methods

The (published) datasets implemented in qpcR

Averaging of multiple reference genes

Calculation of ratios in a batch format for multiple genes/samples

Residual variance of a fitted model

Calculation of qPCR efficiency by the 'window-of-linearity' method

Amalgamation of single data models into a model containing replicates

Updating and refitting a qPCR model

Calculation of the qPCR takeoff point

An (overlayed) residuals barplot

Calculation of ratios from qPCR runs with/without reference genes

R-square value of a fitted model

Calculation of qPCR efficiency by the 'linear regression of efficiency' method

Root-mean-squared-error of a fitted model

Formal lack-Of-Fit test of a nonlinear model against a one-way ANOVA model

Allen's PRESS (Prediction Sum-Of-Squares) statistic, aka P-square

Adjusted R-square value of a fitted model

(K)inetic (O)utlier (D)etection using several methods

Cy0 alternative to threshold cycles as in Guescini et al. (2008)

Akaike's second-order corrected Information Criterion

Residual sum-of-squares of a fitted model