sigmashrink

"Standard" degrees of freedom of the residuals

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><a rd-options="" href="/link/variance_adjust?package=ruv&version=0.9.7.1" data-mini-rdoc="ruv::variance_adjust">variance_adjust</a></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

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><a rd-options='' href='variance_adjust'>variance_adjust</a></code>, and is not normally intended for stand-alone use.

sigmashrink: Empirical Bayes shrinkage estimate of sigma^2

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