Removing batch effects for prediction problems with frozen surrogate variable analysis

TL;DR

This paper introduces frozen surrogate variable analysis (fSVA), a novel batch effect correction method designed for individual sample prediction in clinical genomics, improving accuracy over existing techniques.

Contribution

fSVA is the first batch correction method tailored specifically for prediction tasks in clinical genomics, leveraging training data for individual sample correction.

Findings

fSVA improves prediction accuracy in simulations.

fSVA enhances results in public genomic studies.

fSVA is available in the sva Bioconductor package.

Abstract

Batch effects are responsible for the failure of promising genomic prognos- tic signatures, major ambiguities in published genomic results, and retractions of widely-publicized findings. Batch effect corrections have been developed to re- move these artifacts, but they are designed to be used in population studies. But genomic technologies are beginning to be used in clinical applications where sam- ples are analyzed one at a time for diagnostic, prognostic, and predictive applica- tions. There are currently no batch correction methods that have been developed specifically for prediction. In this paper, we propose an new method called frozen surrogate variable analysis (fSVA) that borrows strength from a training set for individual sample batch correction. We show that fSVA improves prediction ac- curacy in simulations and in public genomic studies. fSVA is available as part of the sva Bioconductor package.