Title: Bayesian analysis of qRT-PCR data
Description: This package implements generalized linear mixed model analysis of qRT-PCR data based on lognormal-Poisson model fitted using MCMC. Also implemented are the lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization. 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.release1.pdf

Description: This package implements generalized linear mixed model analysis of qRT-PCR data based on lognormal-Poisson model fitted using MCMC. Also implemented are the lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization. Several plotting functions are included to extract and visualize results. Tutorial: http://www.bio.utexas.edu/research/matz_lab/matzlab/Methods_18files/mcmc.qpcr.tutorial.release1.pdf

Description: This package implements generalized linear mixed model analysis of qRT-PCR data based on lognormal-Poisson model fitted using MCMC. Also implemented are the lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization. Several plotting functions are included to extract and visualize results.

Description: 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

Title: 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: http://www.bio.utexas.edu/research/matz_lab/matzlab/Methods_files/mcmc.qpcr.tutorial.pdf.