Performance evaluation of transcriptomics data normalization for survival risk prediction.

ABSTRACT: One pivotal feature of transcriptomics data is the unwanted variations caused by disparate experimental handling, known as handling effects. Various data normalization methods were developed to alleviate the adverse impact of handling effects in the setting of differential expression analysis. However, little research has been done to evaluate their performance in the setting of survival outcome prediction, an important analysis goal for transcriptomics data in biomedical research. Leveraging a unique pair of datasets for the same set of tumor samples-one with handling effects and the other without, we developed a benchmarking tool for conducting such an evaluation in microRNA microarrays. We applied this tool to evaluate the performance of three popular normalization methods-quantile norm

SUBMITTER: Ni A 

PROVIDER: S-EPMC8575026 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature