Lukas Kopecky

MAMSI: Integration of Multiassay Liquid Chromatography–Mass Spectrometry Metabolomics Data Using Multiview Machine Learning Liquid chromatography–mass spectrometry (LC-MS) is a commonly used analytical technique in untargeted metabolomics. However, the diverse chemical and physical properties of metabolites often require the use of several different analytical assays for broad metabolome coverage. Conventionally, each assay is analyzed separately, but this fails to capture interassay relationships, making multiassay biomarker discovery and data interpretation difficult. Here we propose a workflow to integrate multiassay metabolomics data, designed to enable biomarker discovery and elucidation of unknown metabolites. We employ a multiblock–partial least-squares model (MB-PLS) coupled with multiblock variable importance in projection to estimate the importance of predictors to the outcome variable. Then we cluster the selected predictors and compare them to groups defined by their structural properties based on retention time and mass-to-charge ratio. To demonstrate and evaluate the approach, we used three multiassay data sets predicting biological sex, Alzheimer’s disease status, and blood bilirubin levels as the outcomes of interest. The MB-PLS models outperformed single-assay models in both classification and regression tasks, indicating that modeling interblock relationships enabled an improved estimate of phenotypic outcome. Additionally, the MB-PLS models shed valuable insight into each data block’s contribution to the predicted outcome. Our workflow enabled us to determine a set of potential cross-assay biomarkers. Following putative annotation, the majority of these and their signs of association agreed with results previously reported in the literature. Our workflow has the potential to benefit the metabolomics community and beyond as it offers interpretable integrative analysis of multiassay LC-MS data and facilitates discovery of potential biomarkers.

I’m happy to share that our paper on the integration of multi-assay LC-MS data has just been published!

pubs.acs.org/doi/10.1021/...

#MAMSI #LCMS #Multiview #MachineLearning #Metabolomics

Home Imperial College London is a world-leading university for science, technology, engineering, medicine and business (STEMB). Across our London campuses, and throughout our international network, we use ...

Calling computational #metabolomics people out there! Are you interested in working with #EU colleagues to analyse and model metabolomic data? We are looking for an enthusiastic and knowledgeable individual to join the #BiACEM
project: www.imperial.ac.uk/jobs/search-...

Glass of milk a day cuts bowel cancer risk - study Dark leafy greens, bread and non-dairy milks containing calcium were also found to offer protection.

Glass of milk a day cuts bowel cancer risk – UK study

Andrew Prentice tells BBC News findings raise possibility calcium supplements might protect against #BowelCancer but "the jury is still out" on this.🥛 #PublicHealth

www.bbc.co.uk/news/article...

Congratulations!

Description Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities ment...

We're hiring! Are you a #metabolomics scientist interested in working with #EU colleagues to analyse and model metabolomic data? We are looking for an enthusiastic and knowledgeable individual to join the #BiACEM project: www.imperial.ac.uk/jobs/search-...

THe #metabolomics starter pack is now up to 75 members. Again, if you would like to be added let me know. Equally if I have put you on the list by mistake let me know. go.bsky.app/J3VPYKm

Lukas Kopecky from Imperial telling us how to integrate mutimodal #massspectrometry data in #metabolomics and beyond #metabomeeting24 #CompMetabolomics