Bayesian Analysis of qRT-PCR Data

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Documentation for package ‘MCMC.qpcr’ version 1.2.4

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amp.eff amplification efficiencies and experimental Cq1 (optional column)
beckham.data Cellular heat stress response data.
beckham.eff amplification efficiencies for beckham.data
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 selection of stable control genes) as implemented in selectHKgenes function (package SLqPCR)
cq2log Prepares qRT-PCR data for mcmc.qpcr analysis using lognormal and "classic" (normalization-based) models
diagnostic.mcmc Plots three diagnostic plots to check the validity of the assumptions of linear model analysis.
dilutions Data to determine amplification efficiency
getNormalizedData Extracts qPCR model predictions
HPDplot Plotting fixed effects for all genes for a single combination of factors
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 Bayesian analysis of qRT-PCR data
mcmc.qpcr Analyzes qRT-PCR data using generalized linear mixed model
mcmc.qpcr.classic Analyzes qRT-PCR data using "classic" model, based on multigene normalization.
mcmc.qpcr.lognormal Fits a lognormal linear mixed model to qRT-PCR data.
normalize.qpcr Internal function called by mcmc.qpcr.classic
outlierSamples detects outlier samples in qPCR data
padj.hpdsummary Adjusts p-values within an HPDsummary() object for multiple comparisons
padj.qpcr Calculates adjusted p-values corrected for multiple comparisons
PrimEff Determines qPCR amplification efficiencies from dilution series
softNorm Accessory function to mcmc.qpcr() to perform soft normalization
summaryPlot Wrapper function for ggplot2 to make bar and line graphs of mcmc.qpcr() results
trellisByGene For two-way designs, plots mcmc.qpcr model predictions gene by gene