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>.

Bayesian Analysis of qRT-PCR Data

package-ubuntu-20.04-r-4.0.5#cran#MCMC.qpcr-1.2.4

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

A data frame with 76 observations on the following 3 variables.

<dl>
<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>

package-ubuntu-20.04-r-4.0.5#cran#MCMC.qpcr-1.2.4#docs#dilutions

dilutions

Description

Usage

Format

Examples

MCMC.qpcr

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