Statistics and bioinformatics in nutritional sciences: Analysis of complex data in the era of systems biology

Wenjiang J. Fu, Arnold J. Stromberg, Kert Viele, Raymond J. Carroll, Guoyao Wu

Research output: Contribution to journalReview articlepeer-review

80 Scopus citations

Abstract

Over the past 2 decades, there have been revolutionary developments in life science technologies characterized by high throughput, high efficiency, and rapid computation. Nutritionists now have the advanced methodologies for the analysis of DNA, RNA, protein, low-molecular-weight metabolites, as well as access to bioinformatics databases. Statistics, which can be defined as the process of making scientific inferences from data that contain variability, has historically played an integral role in advancing nutritional sciences. Currently, in the era of systems biology, statistics has become an increasingly important tool to quantitatively analyze information about biological macromolecules. This article describes general terms used in statistical analysis of large, complex experimental data. These terms include experimental design, power analysis, sample size calculation, and experimental errors (Type I and II errors) for nutritional studies at population, tissue, cellular, and molecular levels. In addition, we highlighted various sources of experimental variations in studies involving microarray gene expression, real-time polymerase chain reaction, proteomics, and other bioinformatics technologies. Moreover, we provided guidelines for nutritionists and other biomedical scientists to plan and conduct studies and to analyze the complex data. Appropriate statistical analyses are expected to make an important contribution to solving major nutrition-associated problems in humans and animals (including obesity, diabetes, cardiovascular disease, cancer, ageing, and intrauterine growth retardation).

Original languageEnglish
Pages (from-to)561-572
Number of pages12
JournalJournal of Nutritional Biochemistry
Volume21
Issue number7
DOIs
StatePublished - Jul 2010

Bibliographical note

Funding Information:
This work was supported, in part, by grants from National Institutes of Health (P20RR16481, 2P42 ES007380-09, P20RR020145-01, 1R21 HD049449, and CA57030), King Abdullah University of Science and Technology (KUS-CI-016-04), National Research Initiative Competitive Grants from the Animal Reproduction Program (2008-35203-19120) and Animal Growth & Nutrient Utilization Program (2008-35206-18764) of the USDA National Institute of Food and Agriculture, American Heart Association (#0755024Y), and Texas AgriLife Research (H-8200).

Keywords

  • Bioinformatics
  • Nutrition research
  • Statistical analysis
  • Systems biology

ASJC Scopus subject areas

  • Endocrinology, Diabetes and Metabolism
  • Biochemistry
  • Molecular Biology
  • Nutrition and Dietetics
  • Clinical Biochemistry

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