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Tytuł:
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LC-N2G: a local consistency approach for nutrigenomics data analysis.
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Autorzy:
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Xu X; School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia.; Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.
Solon-Biet SM; Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.; School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, 2006, Australia.
Senior A; Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.; School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, 2006, Australia.
Raubenheimer D; Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.; School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, 2006, Australia.
Simpson SJ; Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.; School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, 2006, Australia.
Fontana L; Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.; Sydney Medical School, The University of Sydney, Sydney, NSW, 2006, Australia.
Mueller S; Department of Mathematics and Statistics, Macquarie University, Sydney, NSW, 2109, Australia.
Yang JYH; School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia. .; Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia. .
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Źródło:
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BMC bioinformatics [BMC Bioinformatics] 2020 Nov 17; Vol. 21 (1), pp. 530. Date of Electronic Publication: 2020 Nov 17.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Original Publication: [London] : BioMed Central, 2000-
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MeSH Terms:
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Algorithms*
Data Analysis*
Nutrigenomics*
Animal Nutritional Physiological Phenomena ; Animals ; Computer Simulation ; Gene Expression Regulation ; Mice ; Nonlinear Dynamics
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References:
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Grant Information:
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DP170100654 Australian Research Council Discovery Project grant; APP1111338 Australia NHMRC Career Developmental Fellowship
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Contributed Indexing:
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Keywords: Gene expression; Local consistency; Nutrigenmoics; Nutrition
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Entry Date(s):
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Date Created: 20201118 Date Completed: 20201211 Latest Revision: 20201214
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Update Code:
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20240105
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PubMed Central ID:
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PMC7672905
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DOI:
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10.1186/s12859-020-03861-3
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PMID:
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33203358
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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 relationship 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 significance. Finally, the response surfaces are generated for the subset of significant relationships. 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 nutrition 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.
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