Detect and Remove Unwanted Variation using Negative Controls
Collapse Replicates
Design Matrix
Get empirical variances
Get K
Google Search URL
Input Check One
Inverse Method Variances
Projection Plot Variables
(Randomization) Inverse Method Variances
Replicate (Mapping) Matrix
Residual Operator
Detect and Remove Unwanted Variation using Negative Controls
RUV Canonical Correlation Plot
RUV P-value Empirical CDF Plot
RUV P-value Histogram Plot
RUV Projection Plot
RUV Rank Plot
RUV Residuals
RUV RLE Plot
RUV Scree Plot
RUV Shiny App
RUV Summary
RUV SVD Grid Plot
RUV SVD Plot
RUV Variance Plot
RUV Volcano Plot
Remove Unwanted Variation, 2-step
Remove Unwanted Variation, 4-step
RUV-I
RUV-III
Remove Unwanted Variation, inverse method
Remove Unwanted Variation, ridged inverse method
Empirical Bayes shrinkage estimate of sigma^2
Adjust Estimated Variances
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.