Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

The sensitivity of transcriptomics BMD modeling to the methods used for microarray data normalization

Roman Mezencev, Scott Auerbach
doi: https://doi.org/10.1101/781567
Roman Mezencev
1National Center for Environmental Assessment, US EPA, Washington DC, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: Mezencev.Roman@epa.gov
Scott Auerbach
2National Institute of Environmental Health Sciences, NIH, Research Triangle Park NC, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Whole-genome expression data generated by microarray studies have shown promise for quantitative human health risk assessment. While numerous approaches have been developed to determine benchmark doses (BMDs) from probeset-level dose responses, sensitivity of the results to methods used for normalization of the data has not yet been systematically investigated. Normalization of microarray data converts raw hybridization signals to expression estimates that are expected to be proportional to the amounts of transcripts in the profiled specimens. Different approaches to normalization have been shown to greatly influence the results of some downstream analyses, including biological interpretation. In this study we evaluate the influence of microarray normalization methods on the transcriptomic BMDs. We demonstrate using in vivo data that the use of alternative pipelines for normalization of Affymetrix microarray data can have a considerable impact on the number of detected differentially expressed genes and pathways (processes) determined to be treatment responsive, which may lead to alternative interpretations of the data. In addition, we found that normalization can have a considerable effect (as much as ∼30-fold in this study) on estimation of the minimum biological potency (transcriptomic point of departure). We argue for consideration of alternative normalization methods and their data-informed selection to most effectively interpret microarray data for use in human health risk assessment.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license.
Back to top
PreviousNext
Posted September 25, 2019.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
The sensitivity of transcriptomics BMD modeling to the methods used for microarray data normalization
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
The sensitivity of transcriptomics BMD modeling to the methods used for microarray data normalization
Roman Mezencev, Scott Auerbach
bioRxiv 781567; doi: https://doi.org/10.1101/781567
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
The sensitivity of transcriptomics BMD modeling to the methods used for microarray data normalization
Roman Mezencev, Scott Auerbach
bioRxiv 781567; doi: https://doi.org/10.1101/781567

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Pharmacology and Toxicology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4381)
  • Biochemistry (9581)
  • Bioengineering (7087)
  • Bioinformatics (24845)
  • Biophysics (12598)
  • Cancer Biology (9952)
  • Cell Biology (14347)
  • Clinical Trials (138)
  • Developmental Biology (7945)
  • Ecology (12103)
  • Epidemiology (2067)
  • Evolutionary Biology (15985)
  • Genetics (10921)
  • Genomics (14735)
  • Immunology (9869)
  • Microbiology (23647)
  • Molecular Biology (9477)
  • Neuroscience (50839)
  • Paleontology (369)
  • Pathology (1539)
  • Pharmacology and Toxicology (2681)
  • Physiology (4013)
  • Plant Biology (8655)
  • Scientific Communication and Education (1508)
  • Synthetic Biology (2391)
  • Systems Biology (6427)
  • Zoology (1346)