EmpiricalBayesPrior

Computes the prior parameters (i.e. pseudocounts incremented by 1) for
the log2 fold change distribution

Ratios of count data such as obtained from RNA-seq are modelled
using Bayesian statistics to derive posteriors for effects sizes. This
approach is described in Erhard & Zimmer (2015) <doi:10.1093/nar/gkv696>
and Erhard (2018) <doi:10.1093/bioinformatics/bty471>.

Florian Erhard

Log Fold Change Distribution Tools for Working with Ratios of
Counts

EmpiricalBayesPrior function

<dl><dt>A</dt>
<dd>Vector of counts from condition A</dd>
<dt>B</dt>
<dd>Vector of counts from condition B</dd>
<dt>min.sd</dt>
<dd>minimal standard deviation of the prior</dd></dl>

Arguments

Computes the prior parameters (i.e. pseudocounts incremented by 1) for
the log2 fold change distribution — EmpiricalBayesPrior

<dl>

<dt>A</dt>
<dd>Vector of counts from condition A</dd>


<dt>B</dt>
<dd>Vector of counts from condition B</dd>


<dt>min.sd</dt>
<dd>minimal standard deviation of the prior</dd>

</dl>

EmpiricalBayesPrior: Computes the prior parameters (i.e. pseudocounts incremented by 1) for the log2 fold change distribution

Description

Usage

Value

Arguments

See Also

Examples