softNorm

The dataset; output of cq2counts()

data

controls

adds a fake NORM gene with counts equal to geometric mean of conrtol genes,
removes control genes from the dataset

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

softNorm function

<dl> <dt>data</dt>
<dd>The dataset; output of cq2counts()</dd> <dt>controls</dt>
<dd>Vector fo control gene names</dd></dl>

Arguments

Mikhail Matz, UT Austin
&lt;matz@utexas.edu&gt;

Author

Accessory function to mcmc.qpcr() to perform soft normalization — softNorm

<dl>

 <dt>data</dt>
<dd>The dataset; output of cq2counts()</dd>

 <dt>controls</dt>
<dd>Vector fo control gene names</dd>

</dl>

softNorm: Accessory function to mcmc.qpcr() to perform soft normalization

Description

Usage

Value

Arguments

Author