MetaboLINK is a novel algorithm for unveiling cell-specific metabolic pathways in longitudinal datasets
IntroductionIn the rapidly advancing field of ‘omics research, there is an increasing demand for sophisticated bioinformatic tools to enable efficient and consistent data analysis. As biological datasets, particularly metabolomics, become larger and more complex, innovative strategies are essential...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2024.1520982/full |
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| Summary: | IntroductionIn the rapidly advancing field of ‘omics research, there is an increasing demand for sophisticated bioinformatic tools to enable efficient and consistent data analysis. As biological datasets, particularly metabolomics, become larger and more complex, innovative strategies are essential for deciphering the intricate molecular and cellular networks.MethodsWe introduce a pioneering analytical approach that combines Principal Component Analysis (PCA) with Graphical Lasso (GLASSO). This method is designed to reduce the dimensionality of large datasets while preserving significant variance. For the first time, we applied the PCA-GLASSO algorithm (i.e., MetaboLINK) to metabolomics data derived from Nuclear Magnetic Resonance (NMR) spectroscopy performed on neural cells at various developmental stages, from human embryonic stem cells to neurons.ResultsThe MetaboLINK analysis of longitudinal metabolomics data has revealed distinct pathways related to amino acids, lipids, and energy metabolism, uniquely associated with specific cell progenies. These findings suggest that different metabolic pathways play a critical role at different stages of cellular development, each contributing to diverse cellular functions.DiscussionOur study demonstrates the efficacy of the MetaboLINK approach in analyzing NMR-based longitudinal metabolomic datasets, highlighting key metabolic shifts during cellular transitions. We share the methodology and the code to advance general ‘omics research, providing a powerful tool for dissecting large datasets in neurobiology and other fields. |
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| ISSN: | 1662-453X |