Multivariate curve resolution-based data fusion approaches applied in 1H NMR metabolomic analysis of healthy cohorts.

BACKGROUND: Metabolomics plays a critical role in deciphering metabolic alterations within individuals, demanding the use of sophisticated analytical methodologies to navigate its intricate complexity. While many studies focus on single biofluid types, simultaneous analysis of multiple matrices enhances understanding of complex biological mechanisms. Consequently, the development of data fusion methods enabling multiblock analysis becomes essential for comprehensive insights into metabolic dynamics. RESULTS: This study introduces a novel guideline for jointly analyzing diverse metabolomic datasets (serum, urine, metadata) with a focus on metabolic differences between groups within a healthy cohort. The guideline presents two fusion strategies, 'Low-Level data fusion' (LLDF) and 'Mid-Level data fusion' (MLDF), employing a sequential application of Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS), linking the outcomes of successive analyses. MCR-ALS is a versatile method for analyzing mixed data, adaptable at various stages of data processing-encompassing resonance integration, data compression, and exploratory analysis. The LLDF and MLDF strategies were applied to 1H NMR spectral data extracted from urine and serum samples, coupled with biochemical metadata sourced from 145 healthy volunteers. SIGNIFICANCE: Both methodologies effectively integrated and analysed multiblock datasets, unveiling the inherent data structure and variables associated with discernible factors among healthy cohorts. While both approaches successfully detected sex-related differences, the MLDF strategy uniquely revealed components linked to age. By applying this analysis, we aim to enhance the interpretation of intricate biological mechanisms and uncover variations that may not be easily discernible through individual data analysis.

Fusing data of different orders for environmental monitoring.

In the present work a novel application of data fusion to an environmental monitoring study is proposed. This paper involves the joint analysis of zeroth-, first- and second-order data measured on a particular environmental system. The main advantage of this methodology is the possibility of analyzing the relationships of the different order data provided by several analytical techniques. This approach enables to achieve new knowledge, in a way that would be not accessible if considering the information individually. Environmental monitoring databases usually generate large amount of data. Multivariate statistical techniques are necessary to process all this information and obtain a correct interpretation. The Ludueña Stream located in Argentina was chosen as the study system. Samples from different sites of the basin were taken periodically. Conductivity and pH (zeroth-order data) were fused with near-infrared (NIR) spectra of suspended particulate material (first-order data) and with fluorescence emission-excitation matrices of dissolved organic matter (second-order data). Different chemometric algorithms made it possible to extract and merge all the information in a new database, enabling its later analysis as a whole. This methodology allowed to successfully studying the behavior of dissolved organic matter together with suspended particulate material and other specific variables, showing links between them. Their distributions along the basin and their evolutions over time were possible to obtain. Therefore, a simpler interpretation to evaluate the system status was achieved. This model allowed differentiating the variables affected by anthropic activities from those with a natural origin.