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.

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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

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Abstract

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.

PLoS Computational BiologyVol. 21(4)
Universität Hamburg (DE), German Center for Infection Research (DE), Research Center Borstel - Leibniz Lung Center (DE), German Center for Lung Research (DE)
German Network for Bioinformatics Infrastructure
Openalex Percentile: Top 93%
Metabolomics and Mass Spectrometry Studies
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