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

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).

Fig. 1

A flow chart for microarray experiment and data analysis. A microarray experiment involves platform selection, sample size calculation, adequate, design, data collection and processing, and normalization of gene chips. Statistical significance in levels of differentially expressed among treatment groups is commonly determined by a combination of p-value and the false discovery rate. Results of microarray studies are normally verified by real-time RT-PCR analysis.

Fig. 2

Isotope-labeled quantification in proteomics analysis. The x-axis contains the scan number while the y-axis contains the observed intensities for the light isotope (red) and the heavy isotope (blue). Dark red areas indicate overlapping bars. The estimated protein expression ratio is simply the sum of the red bars divided by the sum of the blue bars.

Fig. 3

Idealized version of RelEx. The bottom panes of the figure show the observed light (red) and heavy (blue) ion intensities. The intensities used are colored. The points in the main pane are the light and heavy intensities observed for each scan plotted against each other. The slope of the regression line (determined without an intercept) estimates the expression ratio while the correlation of the points (how well the peaks align) is a measure of confidence in the estimated expression ratio. The full implementation of RelEx adds various smoothers and other enhancements.