padj.qpcr

Output of HPDplot(...,plot=FALSE); may be several several such outputs concatenated with rbind

data

controls

p-value correction method (see function p.adjust), default is Benjamini-Hochberg

method

Takes the output of HPDplot(...,plot=FALSE), adds two columns of adjusted p-values (z and mcmc)
Disregards the entries corresponding to control genes

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>.

padj.qpcr: Calculates adjusted p-values corrected for multiple comparisons

Description

Usage

Arguments

Value

References