This method implements the method of empirical variances as described in Gagnon-Bartsch, Jacob, and Speed (2013). This function is normally called from the function variance_adjust, 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 by Gagnon-Bartsch and Speed (2012), and by Gagnon-Bartsch, Jacob and Speed (2013). 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', and 'RUV-rinv', along with various supporting algorithms.
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