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Abstract:
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Genomic datasets contain the effects of various unobserved biological variables in addition to the variable of primary interest. These latent variables often affect a large number of features (e.g., genes), giving rise to dense latent variation, which presents both challenges and opportunities for classification. While some of these latent variables may be partially correlated with the phenotype of interest and thus helpful, others may be uncorrelated and merely contribute additional noise. Moreover, whether potentially helpful or not, these latent variables may obscure weaker effects that impact only a small number of features but more directly capture the signal of primary interest. To address these challenges, we propose the cross-residualization classifier (CRC), a linear discriminant-based ensemble classifier that accounts for latent variables without discarding any potentially predictive information. We apply the method to simulated data and a variety of genomic datasets from multiple platforms. In general, we find that the CRC performs well relative to existing classifiers and sometimes offers substantial gains.
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