LC-N2G: a local consistency approach for nutrigenomics data analysis
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METHODOLOGY ARTICLE
Open Access
LC‑N2G: a local consistency approach for nutrigenomics data analysis Xiangnan Xu1,2, Samantha M. Solon‑Biet2,3, Alistair Senior2,3, David Raubenheimer2,3, Stephen J. Simpson2,3, Luigi Fontana2,4, Samuel Mueller5† and Jean Y. H. Yang1,2*†
*Correspondence: [email protected] † Samuel Mueller and Jean Y. H. Yang contributed equally to this work 2 Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia Full list of author information is available at the end of the article
Abstract Background: Nutrigenomics aims at understanding the interaction between nutrition and gene information. Due to the complex interactions of nutrients and genes, their relationship exhibits non-linearity. One of the most effective and efficient methods to explore their relationship is the nutritional geometry framework which fits a response surface for the gene expression over two prespecified nutrition variables. However, when the number of nutrients involved is large, it is challenging to find combinations of informative nutrients with respect to a certain gene and to test whether the relation‑ ship is stronger than chance. Methods for identifying informative combinations are essential to understanding the relationship between nutrients and genes. Results: We introduce Local Consistency Nutrition to Graphics (LC-N2G), a novel approach for ranking and identifying combinations of nutrients with gene expression. In LC-N2G, we first propose a model-free quantity called Local Consistency statistic to measure whether there is non-random relationship between combinations of nutrients and gene expression measurements based on (1) the similarity between samples in the nutrient space and (2) their difference in gene expression. Then combinations with small LC are selected and a permutation test is performed to evaluate their signifi‑ cance. Finally, the response surfaces are generated for the subset of significant relation‑ ships. Evaluation on simulated data and real data shows the LC-N2G can accurately find combinations that are correlated with gene expression. Conclusion: The LC-N2G is practically powerful for identifying the informative nutri‑ tion variables correlated with gene expression. Therefore, LC-N2G is important in the area of nutrigenomics for understanding the relationship between nutrition and gene expression information. Keywords: Nutrigenmoics, Local consistency, Nutrition, Gene expression
Background Nutrients are simple organic compounds involved in biochemical reactions that produce energy or are constituents of cellular biomass [1]. Nutrigenomics, the combination of nutrition and genomics research, which aims to shed light on and describe, characterize, and integrate the interactions between nutritional compounds and genome-wide gene expression [2], has been thriving after the completion of the human genome 15 years © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing,
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