get_empirical_variances

sigma2

betahat

The expected value of the average of the smallest <code>bin - 1</code> of <code>bin</code> independent chi-square random variables. This can be specified to save computational time (otherwise, it is calculated by simulation).

rescaleconst

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 <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

get_empirical_variances function

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

get_empirical_variances: Get empirical variances

Description

Usage

Arguments

Value

References

See Also