  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 normalization, median normalization, and
variance stabilizing normalization-in survival prediction using various
approaches for model building and designs for sample assignment. We showed that
handling effects can have a strong impact on survival prediction, and that
quantile normalization, a most popular method in current practice, tends to
underperform median normalization and variance stabilizing normalization. We
demonstrated with a small example the reason for quantile normalization's poor
performance in this setting. Our finding highlights the importance of putting
normalization evaluation in the context of the downstream analysis setting and
the potential of improving the development of survival predictors by applying
median normalization. We make available our benchmarking tool for performing
such evaluation on additional normalization methods in connection with
prediction modeling approaches.
