MCMC.qpcr

Plotting fixed effects for all genes for a single combination of factors

Plots qPCR analysis results for individual genes.

Plots qPCR analysis results for individual genes

HPDplot, HPDpoints

Summarizes and plots results of mcmc.qpcr function series.

Bayesian analysis of qRT-PCR data

Determines qPCR amplification efficiencies from dilution series

amplification efficiencies and experimental Cq1 (optional column)

Cellular heat stress response data.

amplification efficiencies for beckham.data

RT-qPCR of stress response in coral Porites astreoides

Prepares qRT-PCR data for mcmc.qpcr analysis

Reformats raw Ct data for geNorm analysis (non-parametric selection of stable control genes) as implemented in selectHKgenes function (package SLqPCR)

Prepares qRT-PCR data for mcmc.qpcr analysis using lognormal and "classic" (normalization-based) models

Plots three diagnostic plots to check the validity of the assumptions of linear model analysis.

Data to determine amplification efficiency

Extracts qPCR model predictions

MCMC diagnostic plots

calculates p-value based on Bayesian z-score or MCMC sampling

Analyzes qRT-PCR data using generalized linear mixed model

Analyzes qRT-PCR data using "classic" model, based on multigene normalization.

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

Internal function called by mcmc.qpcr.classic

detects outlier samples in qPCR data

Adjusts p-values within an HPDsummary() object for multiple comparisons

Calculates adjusted p-values corrected for multiple comparisons

Accessory function to mcmc.qpcr() to perform soft normalization

Wrapper function for ggplot2 to make bar and line graphs of mcmc.qpcr() results

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