dilutions

Cq data for a series of four-fold dilutions for two targets; the input for PrimEff()

datasets

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>.

Mikhail Matz

MCMC.qpcr

Bayesian Analysis of qRT-PCR Data

dilutions function

A data frame with 76 observations on the following 3 variables.<dl class="dl-horizontal">
 <dt><code>dna</code></dt><dd>a numeric vector</dd>
 <dt><code>cq</code></dt><dd>a numeric vector</dd>
 <dt><code>gene</code></dt><dd>a factor with levels <code>chrom</code> <code>eif3h</code></dd>
 </dl>

Format

Data to determine amplification efficiency — dilutions

A data frame with 76 observations on the following 3 variables.<dl class='dl-horizontal'>
 <dt><code>dna</code></dt><dd>a numeric vector</dd>
 <dt><code>cq</code></dt><dd>a numeric vector</dd>
 <dt><code>gene</code></dt><dd>a factor with levels <code>chrom</code> <code>eif3h</code></dd>
 </dl>

dilutions: Data to determine amplification efficiency

Description

Usage

Arguments

Format

Examples