Lipidome visualisation, comparison, and analysis in a vector space
A shallow neural network was used to embed lipid structures in a 2- or 3-dimensional space with the goal that structurally similar species have similar vectors. Tests on complete lipid databanks show that the method automatically produces distributions which follow conventional lipid classifications. The embedding is accompanied by the web-based software, Lipidome Projector. This displays user lipidomes as 2D or 3D scatterplots for quick exploratory analysis, quantitative comparison and interpretation at a structural level. Examples of published data sets were used for a qualitative comparison with literature interpretation.
Authors
- Andrew E. Torda (ORCID: https://orcid.org/0000-0002-6076-709X)
- Dominik Schwudke (ORCID: https://orcid.org/0000-0002-1379-9451)
- Daniel M. Krause (ORCID: https://orcid.org/0000-0002-7440-6201)
- Lukas Müller (ORCID: https://orcid.org/0000-0002-3790-6346)
- Timur Olzhabaev
Institutions
- Universität Hamburg (DE)
- German Center for Infection Research (DE)
- Research Center Borstel - Leibniz Lung Center (DE)
- German Center for Lung Research (DE)
Publication Details
- Journal
- PLoS Computational Biology
- Published
- 2025-04-15
- DOI
- https://doi.org/10.1371/journal.pcbi.1012892
- Primary Topic
- Metabolomics and Mass Spectrometry Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- German Network for Bioinformatics Infrastructure