The Analysis of Human Serum Albumin Proteoforms Using Compositional Framework

Abstract

It has been known since the days of Karl Pearson that ratios of pairwise independent random variables are correlated. However, recognition of the unit sum constraint and hence appropriate methods for analysis of relative abundance have been slow to emerge. Analysis of the relative abundance of multiple components is a characteristic of compositional data. In this thesis, we demonstrate that the compositional data analysis framework is ideally suited to exploring and analyzing the relative abundance of proteoforms measured using Mass Spectrometric Immuno Assays (MSIA). We will introduce basic concepts of compositional data and associated analysis methods. We demonstrate the application of these concepts by exploring the association of human serum albumin’s post translational modifications and kidney function in patients with Type 2 diabetes mellitus. Finally, we discuss the pitfalls of ignoring the compositional nature of such data, and highlight emerging applications demonstrating the generality of the framework.