Lipidomics LC-MS analysis support tools for outlier detection
Contributors
Description
Identification of features with high levels of confidence in liquid chromatography-mass spectrometry (LC MS) lipidomics research is an essential part of biomarker discovery, but existing software platforms can give inconsistent results, even from identical spectral data. This poses a clear challenge for reproducibility in bioinformatics work, and highlights the importance of data-driven outlier detection in assessing spectral outputs – here demonstrated using a machine learning approach based on support vector machine regression combined with leave-one-out cross validation – as well as manual curation, in order to identify software-driven errors driven by closely related lipids and by co-elution issues.
The lipidomics case study dataset used in this work analysed a lipid extraction of a human pancreatic adenocarcinoma cell line (PANC-1, Merck, UK, cat no. 87092802) analysed using an Acquity M-Class UPLC system (Waters, UK) coupled to a ZenoToF 7600 mass spectrometer (Sciex, UK). Raw output files are included alongside processed data using MS DIAL (v4.9.221218) and Lipostar (v2.1.4) and a Jupyter notebook with Python code to analyse the outputs for outlier detection.
Files
inputfile_lipostar.csv
Files
(569.2 MB)
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Additional details
Funding
- The "SEISMIC" facility for Spatially rEsolved sIngle and Sub-cellular oMICs BB/W019116/1
- Biotechnology and Biological Sciences Research Council
- Ion Beam Analysis for the 2020's and Beyond: An Integration of Elemental Mapping and 'omics' EP/R031118/1
- Engineering and Physical Sciences Research Council
- UK National Ion Beam Centre EP/X015491/1
- Engineering and Physical Sciences Research Council
- Core Equipment Award 2022 EP/X034933/1
- Engineering and Physical Sciences Research Council