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: <https://matzlab.weebly.com/uploads/7/6/2/2/76229469/mcmc.qpcr.tutorial.v1.2.4.pdf>.

Version:	1.2.4
Depends:	MCMCglmm, ggplot2, coda
Published:	2020-03-29
DOI:	10.32614/CRAN.package.MCMC.qpcr
Author:	Mikhail V. Matz
Maintainer:	Mikhail V. Matz <matz at utexas.edu>
License:	GPL-3
NeedsCompilation:	no
In views:	MixedModels, Omics
CRAN checks:	MCMC.qpcr results

Documentation:

Reference manual:

MCMC.qpcr.pdf

Downloads:

Package source:	MCMC.qpcr_1.2.4.tar.gz
Windows binaries:	r-devel: MCMC.qpcr_1.2.4.zip, r-release: MCMC.qpcr_1.2.4.zip, r-oldrel: MCMC.qpcr_1.2.4.zip
macOS binaries:	r-release (arm64): MCMC.qpcr_1.2.4.tgz, r-oldrel (arm64): MCMC.qpcr_1.2.4.tgz, r-release (x86_64): MCMC.qpcr_1.2.4.tgz, r-oldrel (x86_64): MCMC.qpcr_1.2.4.tgz
Old sources:	MCMC.qpcr archive

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