ruv_cancorplot

The data matrix. Rows are observations and columns are features (e.g. genes).

Factor(s) of interest. Can be a vector, factor, matrix, or dataframe. Must have the same length (or number of rows) as the number of row of Y.

Optional. The left singular vectors of <code>Y</code>. Can be included to speed up execution.

Optional. The left singular vectors of <code>Y[,ctl]</code>. Can be included to speed up execution.

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.

ruv_cancorplot: RUV Canonical Correlation Plot

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