Mining Lipidomics for Biological Insight

Abstract:

Lipidomics is the study of all lipids that make up cells and organisms. Abnormal lipid metabolism is associated with many cardiovascular risk factors. The Baker Heart and Diabetes Institute has generated lipidomic datasets for several population studies. During lipidomic analysis unwanted variation can arise due to variation in laboratory processing and handling, which unwanted variation removal algorithms aim to minimise. This project aims to develop multivariate methodologies for dealing with unwanted variation in lipidomic datasets and modelling the metabolic associations between groups of lipid species and participant characteristics. In this project we are using lipid set enrichment analysis and eigenlipids to explore lipid biology associated with cardiovascular disease. To identify technical sources of unwanted variation in the plasma lipidome during laboratory processing we performed a laboratory experiment and utilised the Remove Unwanted Variation-III (RUV-III) algorithm to remove these sources of unwanted variation from the lipidomic dataset we acquired. We intend to use this as a basis to identify negative control lipids to remove similar sources of unwanted variation in population lipidomic datasets using RUV-III.

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