iSLS9 Workshop Lipidomics Data Analysis

Sphingolipids and T2DM Risk

Summary

This repository contains datasets and R codes used for 9th International Singapore Lipid Symposium iSLS9 workshop held March 4th, 2021. We will be using the data published in Chew et al., 2019, JCI Insight 5(13) 10.1172/jci.insight.126925 as an example dataset for this workshop.

In the first part, we will look at the structure of lipidomics datasets and at issues and strategies in dealing with them. The practical part includes importing lipidomics datasets and metadata into R, parsing them to different formats for further processing, calculations, QC and visualizations. We will be mostly using tidyverse for this. Furthermore, we will show how to convert lipid species names to the newest LIPID MAPS nomenclature. At the end, we will explore strategies on how to perform repeated statistical tests on all species of a dataset.

In the second part, we practice how to reorganize and inspect the overall data trends from both sample meta data and lipidomics data via visualization and dimension reduction. We probe the correlation structure between the sphingolipids and other competing risk factors collected at baseline, and test associations between the lipids and the risk of DM incidence, using logistic regression (binary outcome) and Cox regression analysis (time-to-event analysis).

Prerequisites

Download the R Project containing the scripts and data used in this workshop from this repository. Alternatively you can clone this repository in RStudio/git. Following packages are needed:

Part 1: Following packages will be used: tidyverse, broom. Furthermore, rgoslin is used to convert lipid nomenclatures. It is only available via github (https://github.com/lifs-tools/rgoslin). See the R notebook (.Rmd) in folder Part_1 on how to install it. Please note that on Windows, you will need an installation of rtools, and on macOS of XCode in order to install this package.
We suggest you to use the currently newest version of RStudio (1.4 or later), which offers a visual RMarkdown/Rnotebook editor.
Part 2: Following CRAN packages will be used: scales, gplots, survival. Additionally, the Bioconductor packages mixOmics and qvalue will be used.

Authors

Bo Burla - Singapore Lipidomics Incubator @ National University of Singapore
Hyungwon Choi - Computational & Statistical Systems Biology Laboratory @ National University of Singapore

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments