sigmashrink

This function (re)implements the empirical bayes shrinkage estimate of Smyth (2004), which is also implemented in the Limma package. This function is normally called from the function <code>variance_adjust</code>, and is not normally intended for stand-alone use.

Implements the 'RUV' (Remove Unwanted Variation) algorithms. These algorithms attempt to adjust for systematic errors of unknown origin in high-dimensional data. The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well. These algorithms were proposed in Gagnon-Bartsch and Speed (2012) <doi:10.1093/nar/gkz433>, Gagnon-Bartsch, Jacob and Speed (2013), and Molania, et. al. (2019) <doi:10.1093/nar/gkz433>. The algorithms require the user to specify a set of negative control variables, as described in the references. The algorithms included in this package are 'RUV-2', 'RUV-4', 'RUV-inv', 'RUV-rinv', 'RUV-I', and RUV-III', along with various supporting algorithms.

Johann Gagnon-Bartsch

Detect and Remove Unwanted Variation using Negative Controls

sigmashrink function

<dl> <dt>s2</dt>
<dd>"Standard" estimates of sigma^2</dd> <dt>d</dt>
<dd>"Standard" degrees of freedom of the residuals</dd></dl>

Arguments

Johann Gagnon-Bartsch <a href="/link/johanngb%40umich.edu?package=ruv&version=0.9.7.2" data-mini-rdoc="ruv::johanngb@umich.edu">johanngb@umich.edu</a>

Author

Empirical Bayes shrinkage estimate of sigma^2 — sigmashrink

<dl>

 <dt>s2</dt>
<dd>"Standard" estimates of sigma^2</dd>

 <dt>d</dt>
<dd>"Standard" degrees of freedom of the residuals</dd>

</dl>

Johann Gagnon-Bartsch <a href='mailto:johanngb@umich.edu'>johanngb@umich.edu</a>

sigmashrink: Empirical Bayes shrinkage estimate of sigma^2

Description

Usage

Value

Arguments

Author

References

See Also