quantro: A data-driven approach to guide the choice of an appropriate normalization method

Stephanie C. Hicks, Rafael A. Irizarry

Research output: Contribution to journalArticlepeer-review

Abstract

Normalization is an essential step in the analysis of high-throughput data. Multi-sample global normalization methods, such as quantile normalization, have been successfully used to remove technical variation. However, these methods rely on the assumption that observed global changes across samples are due to unwanted technical variability. Applying global normalization methods has the potential to remove biologically driven variation. Currently, it is up to the subject matter experts to determine if the stated assumptions are appropriate. Here, we propose a data-driven alternative. We demonstrate the utility of our method (quantro) through examples and simulations. A software implementation is available from http://www.bioconductor.org/packages/release/bioc/html/quantro.html.

Original languageEnglish (US)
Article number117
JournalGenome biology
Volume16
Issue number1
DOIs
StatePublished - Jun 4 2015

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

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