@article{chialvo_che_reif_motsinger-reif_reed_2016, title={Eigenvector metabolite analysis reveals dietary effects on the association among metabolite correlation patterns, gene expression, and phenotypes}, volume={12}, ISSN={["1573-3890"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84988377963&partnerID=MN8TOARS}, DOI={10.1007/s11306-016-1117-3}, abstractNote={‘Multi-omics’ datasets obtained from an organism of interest reared under different environmental treatments are increasingly common. Identifying the links among metabolites and transcripts can help to elucidate our understanding of the impact of environment at different levels within the organism. However, many methods for characterizing physiological connections cannot address unidentified metabolites. Here, we use Eigenvector Metabolite Analysis (EvMA) to examine links between metabolomic, transcriptomic, and phenotypic variation data and to assess the impact of environmental factors on these associations. Unlike other methods, EvMA can be used to analyze datasets that include unidentified metabolites and unannotated transcripts. To demonstrate the utility of EvMA, we analyzed metabolomic, transcriptomic, and phenotypic datasets produced from 20 Drosophila melanogaster genotypes reared on four dietary treatments. We used a hierarchical distance-based method to cluster the metabolites. The links between metabolite clusters, gene expression, and overt phenotypes were characterized using the eigenmetabolite (first principal component) of each cluster. EvMA recovered chemically related groups of metabolites within the clusters. Using the eigenmetabolite, we identified genes and phenotypes that significantly correlated with each cluster. EvMA identifies new connections between the phenotypes, metabolites, and gene transcripts. EvMA provides a simple method to identify correlations between metabolites, gene expression, and phenotypes, which can allow us to partition multivariate datasets into meaningful biological modules and identify under-studied metabolites and unannotated gene transcripts that may be central to important biological processes. This can be used to inform our understanding of the effect of environmental mechanisms underlying physiological states of interest.}, number={11}, journal={METABOLOMICS}, publisher={Springer Nature}, author={Chialvo, Clare H. Scott and Che, Ronglin and Reif, David and Motsinger-Reif, Alison and Reed, Laura K.}, year={2016}, month={Nov} }
 @article{che_jack_motsinger-reif_brown_2014, title={An adaptive permutation approach for genome-wide association study: Evaluation and recommendations for use}, volume={7}, journal={Biodata Mining}, author={Che, R. L. and Jack, J. R. and Motsinger-Reif, A. A. and Brown, C. C.}, year={2014} }
 @misc{che_motsinger-reif_brown_2012, title={Loss of power in two-stage residual-outcome regression analysis in genetic association studies}, volume={36}, number={8}, journal={Genetic Epidemiology}, author={Che, R. L. and Motsinger-Reif, A. A. and Brown, C. C.}, year={2012}, pages={890–894} }