RUV for normalization of expression array data

When dealing with large scale gene expression studies, observations are commonly contaminated by sources of unwanted variation such as platforms or batches. Not taking this unwanted variation into account when analyzing the data can lead to spurious associations and to missing important signals. When the analysis is unsupervised, e.g. when the goal is to cluster the samples or to build a corrected version of the dataset — as opposed to the study of an observed factor of interest — taking unwanted variation into account can become a difficult task. The factors driving unwanted variation may be correlated with the unobserved factor of interest, so that correcting for the former can remove the latter if not done carefully. RUVnormalize implements methods described in Jacob et al. [2012] to estimate and remove unwanted variation from microarray gene expression data. These methods rely on negative control genes and replicate samples.