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

BMC Genomics. 2015 Dec 9:16:1045. doi: 10.1186/s12864-015-2199-4.

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.

MeSH terms

  • Aged
  • Algorithms
  • Colorectal Neoplasms / genetics*
  • Gene Expression Profiling / methods*
  • Gene Expression Regulation, Neoplastic
  • Humans
  • MicroRNAs / genetics*
  • Middle Aged
  • Models, Genetic
  • Models, Statistical

Substances

  • MicroRNAs