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qpcR (version 1.4-0)

Modelling and analysis of real-time PCR data

Description

Model fitting, optimal model selection and calculation of various features that are essential in the analysis of quantitative real-time polymerase chain reaction (qPCR).

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Version

Install

install.packages('qpcR')

Monthly Downloads

1,577

Version

1.4-0

License

GPL (>= 2)

Maintainer

Andrej-Nikolai Spiess

Last Published

September 15th, 2014

Functions in qpcR (1.4-0)

Simple qPCR data import function (i.e. from text files or clipboard)

Sigmoidal model selection by different criteria

Calculation of Akaike weights/relative likelihoods/delta-AICs

Averaging of multiple reference genes

Summarize measures for the goodness-of-fit

The amplification efficiency curve of a fitted object

Batch calculation of qPCR efficiency and other qPCR parameters

Calculation of ratios from qPCR runs with/without reference genes

Comparison of all sigmodal models within the exponential region

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

Plotting qPCR data with fitted curves/confidence bands/error bars

Workhorse function for qPCR model fitting

Calculation of PCR efficiency by fitting an exponential model

Advanced qPCR data import function

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

Evidence ratio for model comparisons with AIC, AICc or BIC

An (overlayed) residuals barplot

Calculation of qPCR efficiency and other important qPCR parameters

Akaike's second-order corrected Information Criterion

R-square value of a fitted model

Root-mean-squared-error of a fitted model

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

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

Residual variance of a fitted model

Residual sum-of-squares of a fitted model

Error propagation using different methods

Combinatorial elimination of plateau and ground phase cycles

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

Calculation of qPCR efficiency using dilution curves and replicate bootstrapping

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

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

Calculation of the 'midpoint' region

Calculation of the qPCR takeoff point

Bootstrapping and jackknifing qPCR data

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

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

Amalgamation of single data models into a model containing replicates

Adjusted R-square value of a fitted model

The chi-square goodness-of-fit

Calculation of likelihood ratios for nested models

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

Value prediction from a fitted qPCR model

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

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

Parameters that can be changed to tweak the kinetic outlier methods

Display news and changes of qpcR package versions

The (published) datasets implemented in qpcR

Updating and refitting a qPCR model

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

The nonlinear/mechanistic models implemented in qpcR

Calculation of ratios in a batch format from external PCR parameters