Issues in the Statistical Analysis in Metabolomics Data with Application to Pressure-Overloaded and Infarcted Mouse Hearts

• Activity Number: 555
• Type: Contributed
• Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Jasmit Shah* and Guy N. Brock and Shesh N. Rai
• Companies: and University of Louisville and University of Louisville
• Keywords: metabolomics ; missing values ; mass spectrometry

Abstract

High throughput technology makes it possible to monitor metabolites on different experiments and has been widely used to detect differences in metabolites in many areas of biomedical research. Mass spectrometry has become one of the main analytical techniques for profiling a wide array of compounds in the biological samples. Extracting relevant biological information from large datasets is one of the challenges. Missing values in metabolomics dataset occur widely and can arise from different sources, including both technical and biological reasons. Mostly the missing value is substituted by the minimum value, and this substitute may lead to different results in the downstream of the analysis. Other issues that arise are normalization of the data. Different methods tend to give different results. In this study we summarize the statistical analysis of metabolomics data with no missing values and with missing values. With the missing values, we compare the different methods and examine the outcomes based on each method. We also look at the different normalization methods and examine the outcomes.