DOI: 10.1101/19012377Nov 19, 2019Paper

Novel meta-analysis pipeline of heterogeneous high-throughput gene expression datasets reveals dysregulated interactions and pathways in asthma

MedRxiv : the Preprint Server for Health Sciences
Brandon GuoK. C. Nadeau

Abstract

Introduction: Asthma is a complex and chronic inflammatory disorder with varying degrees of airway inflammation. It affects ~235 million people worldwide, and about 8% of the United States population. Unlike single-gene disorders, asthma phenotypes are guided by a highly variable combination of genotypes, making it a complex disease to study computationally. Recently, several independent high-throughput gene expression studies in bioinformatics have identified and proposed numerous molecular drivers involved in asthma initiation and progression. However, there is a poor consensus in our understanding of the molecular factors involved in the mechanism of this disease due to inherent genetic heterogeneity. Such an uncertainty in bioinformatics studies have led to a "reproducibility crisis" in the field, where similar analyses can often yield greatly varying results. In this study, we seek to harness heterogeneity in asthma by applying a meta-analysis that explores varying tissue environments. Methods: We use three publicly-available microarray gene expression datasets, belonging to different tissues in asthma patients, from NCBI's Gene Expression Omnibus (GEO). As a meta-analysis, we apply a mixed-model effect size test to determ...Continue Reading

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