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bioRxiv · 10.1101/2020.04.28.051953

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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Abstract

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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Jiang, A., Hilton, L. K., Tang, J., Rushton, C. K., Grande, B. M., Scott, D. W., Morin, R. D.. 2020-04-29. Robust gene expression-based classification of cancers without normalization. https://doi.org/10.1101/2020.04.28.051953

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