  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.
