WITHDRAWN Exudate Chemodiversity in Root Economics Spectrum: Multi-Matrix PCA with Uncertainty Quatification

Music City Center 

Traditional root economics spectrum analysis reduces morphological traits (e.g., diameter, SRL) via PCA but excludes critical chemical dimensions of root exudates due to analytical challenges. We address two gaps: (1) integrating high-dimensional metabolomic data (>1000 compounds per sample) into PCA when compounds must first be characterized by chemical features (e.g., aromaticity, redox potential); (2) handling isomer uncertainty, where identical compound names mask divergent properties. We propose a nested matrix factorization approach: converting raw metabolomic matrices (compounds × mass) into feature-based matrices using quantum-chemical descriptors (e.g., logP, H-bond donors) via PaDEL-Descriptor; using STATIS co-inertia analysis to jointly project morphological traits (root × morphology) and chemical feature blocks (root × compound × feature) into a unified PCA space; and computing probabilistic chemical descriptors as weighted averages across isomers, with weights estimated via Bayesian multinomial regression using PubChem priors. Our probabilistic multi-matrix PCA advances functional trait ecology by addressing high-dimensionality and ambiguity, improving RES analysis.

Root economics spectrum

Chemical descriptor

Multi-block PCA integration

Isomer uncertainty propagation 

Section on Statistics and the Environment