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

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.

Impact/Purpose

This study identified, for the first time, impact of pre-processing of expression microarray data on the results of transcriptomics BMD modeling, including identification of differentially expressed probe sets, responsive pathways and pathway-level BMD (BMDL) values, which are used to approximate biological effect points of departure to determine toxicity values. Our results demonstrate that fold differences among lowest pathway-level BMDL values determined from microarrays normalized by seven different methods can be as low as 1.1 -fold, but also as big as 30.4-fold. Historically, genomic BMD/L analysis using Affymetrix microarrays has relied exclusively on RMA normalization method; however, the findings presented here suggest that the appropriateness of the choice of normalization methods should likely be considered on a case-by-case basis. We argue that different normalization methods need to be considered as a part of quantitative toxicogenomic studies and that sensitivity of reported BMD and BMDL values to different normalization, or justification of appropriateness of the selected normalization method need to be provided. Consequently, RMA pipeline, which has been almost exclusively used for processing of Affymetrix microarray data in quantitative toxicogenomics, should not be applied indiscriminately.

Citation

Mezencev, R. AND S. Auerbach. The sensitivity of transcriptomics BMD modeling to the methods used for microarray data normalization. Public Library of Science, San Francisco, CA, 15(5):e0232955, (2020). [DOI: 10.1371/journal.pone.0232955]

• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7228135/

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