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MCMC.qpcr (version 1.2.4)

Bayesian Analysis of qRT-PCR Data

Description

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: .

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Version

Install

install.packages('MCMC.qpcr')

Monthly Downloads

222

Version

1.2.4

License

GPL-3

Maintainer

Mikhail Matz

Last Published

March 29th, 2020

Functions in MCMC.qpcr (1.2.4)

RT-qPCR of stress response in coral Porites astreoides

Analyzes qRT-PCR data using generalized linear mixed model

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

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

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

Prepares qRT-PCR data for mcmc.qpcr analysis

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

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

mcmc.qpcr.classic

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

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

Plots qPCR analysis results for individual genes.

diagnostic.mcmc

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

mcmc.qpcr.lognormal

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

Calculates adjusted p-values corrected for multiple comparisons

getNormalizedData

Extracts qPCR model predictions

mcmc.converge.check

MCMC diagnostic plots

padj.hpdsummary

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

Data to determine amplification efficiency

Internal function called by mcmc.qpcr.classic

detects outlier samples in qPCR data

Determines qPCR amplification efficiencies from dilution series

amplification efficiencies and experimental Cq1 (optional column)

Cellular heat stress response data.

HPDplotBygeneBygroup

Plots qPCR analysis results for individual genes

HPDplot, HPDpoints

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

Summarizes and plots results of mcmc.qpcr function series.

amplification efficiencies for beckham.data

MCMC.qpcr-package

Bayesian analysis of qRT-PCR data