power.hart

Use the formula of Hart et al (2013) to compute power for comparing RNA-seq expression across two groups assuming a Negative Binomial distribution. The power calculation is based on asymptotic normal approximation.

Defines a collection of functions to compute average power and sample size for studies that use the false discovery rate as the final measure of statistical significance. A three-rectangle approximation method of a p-value histogram is proposed to derive a formula to compute the statistical power for analyses that involve the FDR. The methodology paper of this package is under review.

Yonghui Ni

FDRsamplesize2

Computing Power and Sample Size for the False Discovery Rate in
Multiple Applications

Stanley Pounds

power.hart function

<dl><dt>n</dt>
<dd>per-group sample size (scalar)</dd>
<dt>alpha</dt>
<dd>p-value threshold (scalar)</dd>
<dt>log.fc</dt>
<dd>log fold-change (vector), usual null hypothesis is log.fc=0</dd>
<dt>mu</dt>
<dd>read depth per gene (vector, same length as log.fc)</dd>
<dt>sig</dt>
<dd>coefficient of variation (CV) per gene (vector, same length as log.fc)</dd></dl>

Arguments

Compute power for RNA-seq experiments assuming Negative Binomial distribution — power.hart

<dl>

<dt>n</dt>
<dd>per-group sample size (scalar)</dd>


<dt>alpha</dt>
<dd>p-value threshold (scalar)</dd>


<dt>log.fc</dt>
<dd>log fold-change (vector), usual null hypothesis is log.fc=0</dd>


<dt>mu</dt>
<dd>read depth per gene (vector, same length as log.fc)</dd>


<dt>sig</dt>
<dd>coefficient of variation (CV) per gene (vector, same length as log.fc)</dd>

</dl>

power.hart: Compute power for RNA-seq experiments assuming Negative Binomial distribution

Description

Usage

Value

Arguments

Details

References

Examples