An Intuitive Correspondence Measure for Compositional Data with Applications in Understanding Metagenomic Systems

• Activity Number: 349
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Z. John Daye*+ and Lingling An
• Companies: University of Arizona and University of Arizona
• Keywords: Compositional Data ; Correlation ; Dependency Structure ; Metagenomics ; Networks ; Proportional Data

Abstract

Metagenomic data are often presented in terms of taxonomic compositions, i.e. the relative proportions of microbes in a biological sample. Compositional or proportional data have long being known as statistically challenging to model, due to constraints resulting from each composition being defined from counts of all microbes. In particular, no proper measures are available for determining dependency between co-existing members in metagenomic systems. In this talk, we will provide an intuitive measure of correspondences between compositions. It is robust against technical issues commonly associated with compositional data analysis, such as correlation negativity and subcomposition inconsistency. The procedure will be applied to identify complex interactions leading to biological diseases. This is a joint work with Lingling An.