| Popis: |
Gene expression classifiers are gaining increasing popularity for stratifying tumors into subgroups with distinct biological features. A fundamental limitation shared by current classifiers is the requirement for comparable training and testing datasets. Here, we describe a self-training implementation of our probability ratio-based classification prediction score method (PRPS-ST), which facilitates the porting of existing classification models to other gene expression datasets. In comparison with gold standards, we demonstrate favorable performance of PRPS-ST in gene expression–based classification of diffuse large B-cell lymphoma (DLBCL) and B-lineage acute lymphoblastic leukemia (B-ALL) using a diverse variety of gene expression data types and preprocessing methods, including in classifications with a high degree of class imbalance. Tumors classified by our method were significantly enriched for prototypical genetic features of their respective subgroups. Interestingly, this included cases that were unclassifiable by established methods, implying the potential enhanced sensitivity of PRPS-ST.Significance:The adoption of binary classifiers such as cell of origin (COO) has been thwarted, in part, by the challenges imposed by batch effects and continual evolution of gene expression technologies. PRPS-ST resolves this by enabling classifiers to be ported across platforms while retaining high accuracy.This article is highlighted in the In This Issue feature, p. 215 |
