Robust gene expression-based classification of cancers without normalization
Jiang, A.; Hilton, L. K.; Tang, J.; Rushton, C. K.; Grande, B. M.; Scott, D. W.; Morin, R. D.
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Binary classification using gene expression data is commonly used to stratify cancers into molecular subgroups that may have distinct prognoses and therapeutic options. A limitation of many such methods is the requirement for comparable training and testing data sets. Here, we describe and demonstrate a self-training implementation of probability ratio-based classification prediction score (PRPS-ST) that facilitates the porting of existing classification models to other gene expression data sets. We demonstrate its robustness through application to two binary classification problems in diffuse large B-cell lymphoma using a diverse variety of gene expression data types and normalization methods.
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