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

Removing batch effects for prediction problems with frozen surrogate variable analysis