  Unwanted variation, including hidden confounding, is a well-known problem in
many fields, particularly large-scale gene expression studies. Recent proposals
to use control genes --- genes assumed to be unassociated with the covariates
of interest --- have led to new methods to deal with this problem. Going by the
moniker Removing Unwanted Variation (RUV), there are many versions --- RUV1,
RUV2, RUV4, RUVinv, RUVrinv, RUVfun. In this paper, we introduce a general
framework, RUV*, that both unites and generalizes these approaches. This
unifying framework helps clarify connections between existing methods. In
particular we provide conditions under which RUV2 and RUV4 are equivalent. The
RUV* framework also preserves an advantage of RUV approaches --- their
modularity --- which facilitates the development of novel methods based on
existing matrix imputation algorithms. We illustrate this by implementing RUVB,
a version of RUV* based on Bayesian factor analysis. In realistic simulations
based on real data we found that RUVB is competitive with existing methods in
terms of both power and calibration, although we also highlight the challenges
of providing consistently reliable calibration among data sets.
