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Normalization Methods and Statistical Inference to Identify Differentially Expressed MicroRNAs with an Application to a Residential Cohort Exposed to Environmental Toxins and Pollutants (309972)
Matthew C. Cave, University of Louisville School of MedicineBrian N. Chorley, US Environmental Protection Agency
*Christina M. Pinkston, University of Louisville School of Public Health and Information Sciences
Shesh N. Rai, University of Louisville School of Public Health and Information Sciences
Keywords: MicroRNA, Normalization, Differential Expression, Anniston Community Health Survey, Environmental Liver Disease, TASH
Identification of disease through biomarkers in accessible biofluids, including microRNAs (miRs) – small single-stranded non-coding RNA – remains a promising field of research, especially in liver disease where biopsy is the gold standard. Multiple methods are available to measure miR signatures and there are many considerations for analysis, including normalization. Here, we use the Fireplex® platform technology by Abcam. Normalization methods used with other bioassays – quantile normalization and rank normalization – significantly reduced technical variability seen in unnormalized data or normalized with manufacturer’s suggested methods: GENorm, average normalization, or normalization based on investigator-selected miRs. We briefly describe and mathematically relate these approaches. Then, we demonstrate them using targeted hepatotoxicity miR signatures of participants in the Anniston Community Health Survey, a residential cohort exposed to environmental toxins and pollutants. The effect of normalization methods with and without adjustment are thoroughly explained. Our results highlight the need to include relevant covariate data and to choose correct normalization methods.