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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 |
| Reference manual: | MCMC.qpcr.pdf |
| 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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.