An alignment-agnostic methodology for the analysis of designed separations data

TL;DR

This paper introduces a frequency domain approach for analyzing chemical separation data, effectively handling retention time drift and improving data interpretation over traditional time domain methods.

Contribution

It presents a novel frequency domain methodology that generalizes permutation testing and visualization in ASCA for complex matrices, addressing retention time variability.

Findings

Frequency domain analysis effectively accounts for peak offsets.

The method improves interpretation of synthetic and real datasets.

Enhanced visualization aids in identifying significant factors.

Abstract

Chemical separations data are typically analysed in the time domain using methods that integrate the discrete elution bands. Integrating the same chemical components across several samples must account for retention time drift over the course of an entire experiment as the physical characteristics of the separation are altered through several cycles of use. Failure to consistently integrate the components within a matrix of $M \times N$ samples and variables create artifacts that have a profound effect on the analysis and interpretation of the data. This work presents an alternative where the raw separations data are analysed in the frequency domain to account for the offset of the chromatographic peaks as a matrix of complex Fourier coefficients. We present a generalization of the permutation testing, and visualization steps in ANOVA-Simultaneous Component Analysis (ASCA) to handle complex matrices, and use this method to analyze a synthetic dataset with known significant factors and compare the interpretation of a real dataset via its peak table and frequency domain representations.