# Unifying and Generalizing Methods for Removing Unwanted Variation Based on Negative Controls

## TL;DR

This paper introduces RUV*, a unifying framework for removing unwanted variation in gene expression studies, which clarifies relationships between existing methods and enables new approaches like RUVB based on Bayesian analysis.

## Contribution

The paper presents a general framework, RUV*, that unifies and extends existing methods for unwanted variation removal, facilitating development of new algorithms.

## Key findings

- RUV* clarifies connections between existing RUV methods.
- RUVB, based on RUV*, performs competitively in simulations.
- Challenges remain in achieving consistent calibration across datasets.

## Abstract

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.