...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Metabolites >Visualization and Interpretation of Multivariate Associations with Disease Risk Markers and Disease Risk—The Triplot
【24h】

Visualization and Interpretation of Multivariate Associations with Disease Risk Markers and Disease Risk—The Triplot

机译：具有疾病风险标记和疾病风险的多元关联的可视化和解释-三重奏

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Metabolomics has emerged as a promising technique to understand relationships between environmental factors and health status. Through comprehensive profiling of small molecules in biological samples, metabolomics generates high-dimensional data objectively, reflecting exposures, endogenous responses, and health effects, thereby providing further insights into exposure-disease associations. However, the multivariate nature of metabolomics data contributes to high complexity in analysis and interpretation. Efficient visualization techniques of multivariate data that allow direct interpretation of combined exposures, metabolome, and disease risk, are currently lacking. We have therefore developed the ‘triplot’ tool, a novel algorithm that simultaneously integrates and displays metabolites through latent variable modeling (e.g., principal component analysis, partial least squares regression, or factor analysis), their correlations with exposures, and their associations with disease risk estimates or intermediate risk factors. This paper illustrates the framework of the ‘triplot’ using two synthetic datasets that explore associations between dietary intake, plasma metabolome, and incident type 2 diabetes or BMI, an intermediate risk factor for lifestyle-related diseases. Our results demonstrate advantages of triplot over conventional visualization methods in facilitating interpretation in multivariate risk modeling with high-dimensional data. Algorithms, synthetic data, and tutorials are open source and available in the R package ‘triplot’.
机译：代谢组学已经成为了解环境因素与健康状况之间关系的一种有前途的技术。通过对生物样品中的小分子进行全面分析，代谢组学可以客观地生成高维数据，从而反映出暴露，内源性反应和健康影响，从而进一步了解暴露-疾病的关联。但是，代谢组学数据的多变量性质导致分析和解释的高度复杂性。当前缺乏能够直接解释组合暴露，代谢组和疾病风险的多变量数据的有效可视化技术。因此，我们开发了“ triplot”工具，这是一种新颖的算法，可通过潜在变量建模（例如，主成分分析，偏最小二乘回归或因子分析），与暴露的相关性以及与疾病的关联性来同时整合和显示代谢物风险估计或中间风险因素。本文使用两个综合数据集说明了“三元组”的框架，该数据集探讨了饮食摄入，血浆代谢组和突发性2型糖尿病或BMI（生活方式相关疾病的中间危险因素）之间的关联。我们的研究结果表明，在简化具有高维数据的多变量风险建模中的解释时，三重图优于常规可视化方法。算法，综合数据和教程是开源的，可以在R包“ triplot”中找到。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号