Title
Logistic PCA explains differences between genome-scale metabolic models in terms of metabolic pathways
Author
Barbara Kraus
Gene Therapy Process Development, Baxalta Innovations GmbH, a Part of Takeda Companies
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Abstract
Genome-scale metabolic models (GSMMs) offer a holistic view of biochemical reaction networks, enabling in-depth analyses of metabolism across species and tissues in multiple conditions. However, comparing GSMMs Against each other poses challenges as current dimensionality reduction algorithms or clustering methods lack mechanistic interpretability, and often rely on subjective assumptions. Here, we propose a new approach utilizing logisitic principal component analysis (LPCA) that efficiently clusters GSMMs while singling out mechanistic differences in terms of reactions and pathways that drive the categorization. We applied LPCA to multiple diverse datasets, including GSMMs of 222 Escherichia-strains, 343 budding yeasts (Saccharomycotina), 80 human tissues, and 2943 Firmicutes strains. Our findings demonstrate LPCA’s effectiveness in preserving microbial phylogenetic relationships and discerning human tissue-specific metabolic profiles, exhibiting comparable performance to traditional methods like t-distributed stochastic neighborhood embedding (t-SNE) and Jaccard coefficients. Moreover, the subsystems and associated reactions identified by LPCA align with existing knowledge, underscoring its reliability in dissecting GSMMs and uncovering the underlying drivers of separation.
Keywords
EscherichiaPhylogeneticsPrincipal component analysisPhylogenetic analysisEscherichia coliCancers and neoplasmsMetabolic pathwaysRenal cancer
Object type
Language
English [eng]
Persistent identifier
https://phaidra.univie.ac.at/o:2090591
Appeared in
Title
PLOS Computational Biology
Volume
20
Issue
6
ISSN
1553-7358
Issued
2024
Publisher
Public Library of Science (PLoS)
Date issued
2024
Access rights
Rights statement
© 2024 Zehetner et al
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