Statistical Workflow for Feature Selection in Human Metabolomics Data

代谢组学 工作流程 计算机科学 数据科学 领域(数学) 比例(比率) 标准化 数据挖掘 生物信息学 生物 数学 量子力学 数据库 操作系统 物理 纯数学
作者
Joseph Antonelli,Brian Claggett,Mir Henglin,Andy Kim,Gavin Ovsak,Nicole Kim,Katherine Deng,Kevin Rao,Octavia Tyagi,Jeramie D. Watrous,Kim A. Lagerborg,Pavel Hushcha,Olga Demler,Samia Mora,Teemu J. Niiranen,Alexandre C. Pereira,Mohit Jain,Susan Cheng
出处
期刊:Metabolites [Multidisciplinary Digital Publishing Institute]
卷期号:9 (7): 143-143 被引量:64
标识
DOI:10.3390/metabo9070143
摘要

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations.
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