MCMC.qpcr: Bayesian analysis of qRT-PCR data

This package implements generalized linear mixed model analysis of qRT-PCR data based on lognormal-Poisson model 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). Also implemented are the 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. Tutorial: http://www.bio.utexas.edu/research/matz_lab/matzlab/Methods_files/mcmc.qpcr.tutorial.pdf

Version: 1.1.5
Depends: MCMCglmm, ggplot2, coda
Published: 2015-01-27
Author: Mikhail V. Matz
Maintainer: Mikhail V. Matz <matz at utexas.edu>
License: GPL-3
NeedsCompilation: no
CRAN checks: MCMC.qpcr results

Downloads:

Reference manual: MCMC.qpcr.pdf
Package source: MCMC.qpcr_1.1.5.tar.gz
Windows binaries: r-devel: MCMC.qpcr_1.1.5.zip, r-release: MCMC.qpcr_1.1.5.zip, r-oldrel: MCMC.qpcr_1.1.5.zip
OS X Snow Leopard binaries: r-release: MCMC.qpcr_1.1.5.tgz, r-oldrel: MCMC.qpcr_1.1.5.tgz
OS X Mavericks binaries: r-release: MCMC.qpcr_1.1.5.tgz
Old sources: MCMC.qpcr archive