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Student research project

Supervisors: Professor Peter Meikle and Dr Alex Smith

Research focus

The Metabolomics laboratory uses state-of-the-art mass spectrometry to measure high-quality lipidomes in biological samples, with currently 700+ lipid species measured. This opens up novel possibilities of investigating pathology-relevant associations between the lipids themselves.

Project summary

Until recently, the analysis of lipidomics data was restricted to investigating associations between the measured lipid levels and pathological outcomes of interest. However, with technological improvements leading to the higher precision measurement of more numerous, structurally resolved lipids, it has become possible to investigate the associations between the lipids themselves, or as groups at a higher level of abstraction.

We hypothesise that, as seen in other omics datasets1, these sorts of data analyses can highlight the metabolic or disease pathways at play beneath the observed lipidomes, providing novel biological insight, more approachable data summarisations, and new ways to select biomarker lipids to prioritise for measurement in the clinical setting.

The student will contribute to various facets of this ongoing research to a level in adequacy with their skills and interests. The overarching aims include:

  • Investigate the existence and subsequent removal of technical artefacts that may bias lipid-lipid association analyses in real cohort lipidomics datasets.
  • Investigate multivariate statistical procedures for detecting or summarising lipid-lipid associations (e.g. correlation analysis, clustering, principal component analysis).
  • Investigate the statistical association between such summaries and biological outcomes in the lipidomics cohorts available in the lab, such as cardiovascular disease, diabetes, obesity, and Alzheimer’s Disease, and compare these to associations obtained with the individual lipids.
  • Investigate the use of such summaries for reducing or imputing lipidomics datasets.
  • Contribute to the resulting papers.
  • If available, interface with our other student project on annotation enrichment analyses in groups of lipids.

This project is suitable for a Masters, Honours or PhD student and the student must be comfortable with the general notions behind frequentist multivariate statistics, and have some experience working in the statistical programming language R. Basic knowledge in organic chemistry and metabolism would be very helpful.

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References

  1. WGCNA: an R package for weighted correlation network analysis BMC Bioinformatics 2008.

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