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LC-N2G: a local consistency approach for nutrigenomics data analysis.

Xiangnan Xu1,2, Samantha M Solon-Biet2,3, Alistair Senior2,3

  • 1School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia.

BMC Bioinformatics
|November 18, 2020
PubMed
Summary

We developed Local Consistency Nutrition to Graphics (LC-N2G) to identify nutrient combinations influencing gene expression. This method effectively ranks and finds significant nutrient-gene relationships in nutrigenomics research.

Keywords:
Gene expressionLocal consistencyNutrigenmoicsNutrition

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Area of Science:

  • Nutrigenomics
  • Systems Biology
  • Bioinformatics

Background:

  • Nutrigenomics investigates the complex, non-linear interactions between nutrition and gene expression.
  • Traditional methods like nutritional geometry struggle with identifying informative nutrient combinations in large datasets.
  • Identifying significant nutrient-gene relationships is crucial for understanding diet's impact on health.

Purpose of the Study:

  • To introduce a novel approach, Local Consistency Nutrition to Graphics (LC-N2G), for ranking and identifying nutrient combinations associated with gene expression.
  • To provide a robust method for detecting non-random relationships between multiple nutrients and gene expression.
  • To facilitate the exploration of complex nutrient-gene interactions.

Main Methods:

  • Developed a model-free metric, the Local Consistency statistic, to quantify the relationship between nutrient combinations and gene expression.
  • Utilized sample similarity in nutrient space and gene expression differences to assess relationships.
  • Employed permutation testing for statistical significance and generated response surfaces for validated relationships.

Main Results:

  • LC-N2G successfully ranks and identifies combinations of nutrients significantly correlated with gene expression.
  • The method demonstrated accuracy in identifying informative nutrient combinations on both simulated and real-world data.
  • Validated relationships were visualized using response surface modeling.

Conclusions:

  • LC-N2G is a powerful tool for identifying key nutrition variables impacting gene expression.
  • This approach significantly advances the field of nutrigenomics by clarifying nutrient-gene interactions.
  • LC-N2G aids in understanding the molecular basis of nutrition and health.