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pSVA
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permuted SVA is an algorithm that uses a new statistical model that is blind to biological covariates to correct for technical artifacts while retaining biological heterogeneity in genomic data. This algorithm facilitated accurate subtype identification in head and neck cancer from gene expression data in both formalin fixed and frozen samples. When applied to predict HPV status, pSVA improved cross-study validation even if the sample batches were highly confounded with HPV status in the training set.
Details about the algorithm and these results are published as Parker et al. (2014) Bioinformatics (http://dx.doi.org/10.1093/bioinformatics/btu375)
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