The importance of data transformation in RNA-Seq preprocessing for bladder cancer subtyping

Abstract

Objective RNA-Seq provides an accurate quantification of gene expression levels and it is widely used for molecular subtype classification in cancer, with special importance in prognosis. However, the reliability and validity of these analyses can significantly be influenced by how data are processed. In this study we evaluate how RNA-Seq preprocessing methods influence molecular subtype classification in bladder cancer. By benchmarking various aligners, quantifiers and methods of normalization and transformation, we stress the importance of preprocessing choices for accurate and consistent subtype classification. Results Our findings highlight that log-transformation plays a crucial role in centroid-based classifiers such as consensusMIBC and TCGAclas, while distribution-free algorithms like LundTax offer robustness to preprocessing variations. Non log-transformed data resulted in low classification rates and poor agreement with reference classifications in consensusMIBC and TCGAclas classifiers. Additionally, LundTax consistently demonstrated better separation among subtypes, compared to consensusMIBC and TCGAclas, regardless of preprocessing methods. Nonetheless, the study is limited by the lack of a true reference for objective assessment of the accuracy of the assigned subtypes. Hence, future work will be necessary to determine the robustness and scalability of the obtained results.