Independent Component Analysis for Compositional Data

TL;DR

This paper explores how to adapt independent component analysis for compositional data, addressing the unique challenges of analyzing ratios rather than absolute values, and demonstrates its effectiveness on metabolomic data.

Contribution

It introduces a method for applying independent component analysis to compositional data while respecting its ratio-based nature, which is a novel approach.

Findings

Effective separation of independent components in compositional data

Improved analysis of metabolomic datasets using the proposed method

Reduction of spurious results from standard analysis techniques

Abstract

Compositional data represent a specific family of multivariate data, where the information of interest is contained in the ratios between parts rather than in absolute values of single parts. The analysis of such specific data is challenging as the application of standard multivariate analysis tools on the raw observations can lead to spurious results. Hence, it is appropriate to apply certain transformations prior further analysis. One popular multivariate data analysis tool is independent component analysis. Independent component analysis aims to find statistically independent components in the data and as such might be seen as an extension to principal component analysis. In this paper we examine an approach of how to apply independent component analysis on compositional data by respecting the nature of the former and demonstrate the usefulness of this procedure on a metabolomic data set.