Dissemin is shutting down on January 1st, 2025

Published in

BioMed Central, BMC Genomics, 1(16), 2015

DOI: 10.1186/s12864-015-2199-4

Links

Tools

Export citation

Search in Google Scholar

Incorporation of subject-level covariates in quantile normalization of miRNA data

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Green circle
Postprint: archiving allowed
Green circle
Published version: archiving allowed
Data provided by SHERPA/RoMEO

Abstract

Abstract Background Most currently-used normalization methods for miRNA array data are based on methods developed for mRNA arrays despite fundamental differences between the data characteristics. The application of conventional quantile normalization can mask important expression differences by ignoring demographic and environmental factors. We present a generalization of the conventional quantile normalization method, making use of available subject-level covariates in a colorectal cancer study. Results In simulation, our weighted quantile normalization method is shown to increase statistical power by as much as 10 % when relevant subject-level covariates are available. In application to the colorectal cancer study, this increase in power is also observed, and previously-reported dysregulated miRNAs are rediscovered. Conclusions When any subject-level covariates are available, the weighted quantile normalization method should be used over the conventional quantile normalization method.