Integrative Analysis of Nontargeted LC-HRMS and High-Throughput Metabarcoding Data for Aquatic Environmental Studies Using Combined Multivariate Statistical Approaches

Abstract

Significant progress in high-throughput analytical techniques has paved the way for novel approaches to integrating data sets from different compartments. This study leverages nontarget screening (NTS) via liquid chromatography-high-resolution mass spectrometry (LC-HRMS), a crucial technique for analyzing organic micropollutants and their transformation products, in combination with biological indicators. We propose a combined multivariate data processing framework that integrates LC-HRMS-based NTS data with other high-throughput data sets, exemplified here by 18S V9 rRNA and full-length 16S rRNA gene metabarcoding data sets. The power of data fusion is demonstrated by systematically evaluating the impact of treated wastewater (TWW) over time on an aquatic ecosystem through a controlled mesocosm experiment. Highly compressed NTS data were compiled through the implementation of the region of interest-multivariate curve resolution-alternating least-squares (MCR-ALS) method, known as ROIMCR. By integrating ANOVA-simultaneous component analysis with structural learning and integrative decomposition (SLIDE), the innovative SLIDE-ASCA approach enables the decomposition of global and partial common, as well as distinct variation sources arising from experimental factors and their possible interactions. SLIDE-ASCA results indicate that temporal variability explains a much larger portion of the variance (74.6%) than the treatment effect, with both contributing to global shared space variation (41%). Design structure benefits include enhanced interpretability, improved detection of key features, and a more accurate representation of complex interactions between chemical and biological data. This approach offers a greater understanding of the natural and wastewater-influenced temporal patterns for each data source, as well as reveals associations between chemical and biological markers in an exemplified perturbed aquatic ecosystem.

1

ASCA score plots for time effect (factor β) for NTS (A), 16S rRNA (B), 18S V9 rRNA (C), and global common space of three data blocks using SLIDE-ASCA (D), respectively.

2

ASCA score plots for PC1 of treatment effect for individual data blocks and global common space of three data blocks using SLIDE-ASCA.

3

Heatmap analysis of key bio/chemical features associated with the time effect (β submodel) through SLIDE-ASCA modeling of NTS, 16S rRNA, and 18S V9 rRNA. The color scale represents time-associated changes, with red (up to +3) and blue (down to −3) for strong positive and negative associations, respectively.

4

Correlation circle plot for integrated modeling of NTS, 16S rRNA, and 18S V9 rRNA with DIABLO, for a 4-class classification problem.