MCMC.qpcr: Bayesian Analysis of qRT-PCR Data

Quantitative RT-PCR data are analyzed using generalized linear mixed models based on lognormal-Poisson error distribution, fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). The package also implements a lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results. The detailed tutorial is available here: <>.

AuthorMikhail V. Matz
Date of publication2016-11-09 23:56:55
MaintainerMikhail V. Matz <>

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Man pages

amp.eff: amplification efficiencies and experimental Cq1 (optional... Cellular heat stress response data.

beckham.eff: amplification efficiencies for

coral.stress: RT-qPCR of stress response in coral Porites astreoides

cq2counts: Prepares qRT-PCR data for mcmc.qpcr analysis

cq2genorm: Reformats raw Ct data for geNorm analysis (non-parametric...

cq2log: Prepares qRT-PCR data for mcmc.qpcr analysis using lognormal...

diagnostic.mcmc: Plots three diagnostic plots to check the validity of the...

dilutions: Data to determine amplification efficiency

getNormalizedData: Extracts qPCR model predictions

HPDplot: Plotting fixed effects for all genes for a single combination...

HPDplotBygene: Plots qPCR analysis results for individual genes.

HPDplotBygeneBygroup: Plots qPCR analysis results for individual genes

HPDpoints: HPDplot, HPDpoints

HPDsummary: Summarizes and plots results of mcmc.qpcr function series.

mcmc.converge.check: MCMC diagnostic plots

mcmc.pval: calculates p-value based on Bayesian z-score or MCMC sampling

mcmc.qpcr: Analyzes qRT-PCR data using generalized linear mixed model

mcmc.qpcr.classic: Analyzes qRT-PCR data using "classic" model, based on...

mcmc.qpcr.lognormal: Fits a lognormal linear mixed model to qRT-PCR data.

MCMC.qpcr-package: Bayesian analysis of qRT-PCR data

normalize.qpcr: Internal function called by mcmc.qpcr.classic

outlierSamples: detects outlier samples in qPCR data

padj.qpcr: Calculates adjusted p-values corrected for multiple...

PrimEff: Determines qPCR amplification efficiencies from dilution...

softNorm: Accessory function to mcmc.qpcr() to perform soft...

summaryPlot: Wrapper function for ggplot2 to make bar and line graphs of...

trellisByGene: For two-way designs, plots mcmc.qpcr model predictions gene...

Files in this package

MCMC.qpcr/R/cq2counts.R MCMC.qpcr/R/mcmc.qpcr.classic.R MCMC.qpcr/R/cq2log.R MCMC.qpcr/R/mcmc.qpcr.R MCMC.qpcr/R/summaryPlot.R MCMC.qpcr/R/HPDplotBygeneBygroup.R MCMC.qpcr/R/HPDpoints.R MCMC.qpcr/R/HPDplotBygene.R MCMC.qpcr/R/mcmc.qpcr.lognormal.R MCMC.qpcr/R/diagnostic.mcmc.R MCMC.qpcr/R/softNorm.R MCMC.qpcr/R/cq2genorm.R MCMC.qpcr/R/mcmc.converge.check.R MCMC.qpcr/R/PrimEff.R MCMC.qpcr/R/normalize.qpcr.R MCMC.qpcr/R/mcmc.pval.R MCMC.qpcr/R/padj.qpcr.R MCMC.qpcr/R/HPDplot.R MCMC.qpcr/R/outlierSamples.R MCMC.qpcr/R/getNormalizedData.R MCMC.qpcr/R/trellisByGene.R MCMC.qpcr/R/HPDsummary.R
MCMC.qpcr/man/trellisByGene.Rd MCMC.qpcr/man/mcmc.converge.check.Rd MCMC.qpcr/man/mcmc.qpcr.Rd MCMC.qpcr/man/cq2counts.Rd MCMC.qpcr/man/mcmc.pval.Rd MCMC.qpcr/man/outlierSamples.Rd MCMC.qpcr/man/MCMC.qpcr-package.Rd MCMC.qpcr/man/getNormalizedData.Rd MCMC.qpcr/man/summaryPlot.Rd MCMC.qpcr/man/softNorm.Rd MCMC.qpcr/man/HPDsummary.Rd MCMC.qpcr/man/normalize.qpcr.Rd MCMC.qpcr/man/coral.stress.Rd MCMC.qpcr/man/beckham.eff.Rd MCMC.qpcr/man/cq2log.Rd MCMC.qpcr/man/mcmc.qpcr.lognormal.Rd MCMC.qpcr/man/HPDplotBygene.Rd MCMC.qpcr/man/cq2genorm.Rd MCMC.qpcr/man/ MCMC.qpcr/man/HPDpoints.Rd MCMC.qpcr/man/dilutions.Rd MCMC.qpcr/man/HPDplot.Rd MCMC.qpcr/man/HPDplotBygeneBygroup.Rd MCMC.qpcr/man/padj.qpcr.Rd MCMC.qpcr/man/amp.eff.Rd MCMC.qpcr/man/PrimEff.Rd MCMC.qpcr/man/mcmc.qpcr.classic.Rd MCMC.qpcr/man/diagnostic.mcmc.Rd

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