Logistic PCA explains differences between genome-scale metabolic models in terms of metabolic pathways

• DOI: 10.1371/journal.pcbi.1012236
• Publication Date: 2024-06-24T19:40:04Z
• Authors: Leopold Zehetner; Diana Széliová; Barbara Kraus; Juan A. Hernandez...; Jürgen Zanghellini
• Keywords: Escherichia; 104027 Computational chemistry; Principal Component Analysis; Phylogenetic analysis; Genome; QH301-705.5; Principal component analysis; Computational Biology; Models, Biological; 104027 Computational Chemistry; Phylogenetics; Renal cancer; 106005 Bioinformatik; Metabolic pathways; Escherichia coli; Humans; Cluster Analysis; Biology (General); 106005 Bioinformatics; Cancers and neoplasms; Metabolic Networks and Pathways; Algorithms; Phylogeny; Research Article

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.