Empirical evaluation of data normalization methods for molecular classification.

ABSTRACT:

Background

Data artifacts due to variations in experimental handling are ubiquitous in microarray studies, and they can lead to biased and irreproducible findings. A popular approach to correct for such artifacts is through post hoc data adjustment such as data normalization. Statistical methods for data normalization have been developed and evaluated primarily for the discovery of individual molecular biomarkers. Their performance has rarely been studied for the development of multi-marker molecular classifiers-an increasingly important application of microarrays in the era of personalized medicine.

Methods

In this study, we set out to evaluate the performance of three commonly used methods for data normalization in the context of molecular classification, using extensive s

SUBMITTER: Huang HC 

PROVIDER: S-EPMC5899419 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature