WS2 - Clinical Lipidomics Data Analysis

iSLS11 Workshop on Data Processing and Statistical Analysis

Summary

This repository contains datasets and R codes used for 11th International Singapore Lipid Symposium iSLS11 workshop held on March 19, 2023, 2:00 - 6:00 PM (SGT).

We will be using the data published in Tan et al., Variability of the Plasma Lipidome and Subclinical Coronary Atherosclerosis, Atheroscler Thromb Vasc Biol, 2021 DOI: 10.1161/atvbaha.121.316847 as an example dataset for this workshop.

In the first part, we will inspect and process a targeted MS-based plasma lipidomics raw dataset, starting from peak areas. In particular, we will be looking at import of analytical data and metadata, internal standard-based normalization and quantification, diverse analytical and data quality control assessments and plots, batch/drift-correction, lipid nomenclature, QC-based feature filtering and reporting. We will go through the processing workflow step-by-step using R scripts/notebooks with relevant R packages.

In the second part, we will inspect the overall data trends from both sample meta data and lipidomics data via visualization and dimension reduction. The data set comes from a lipidomics study of individuals at high risk of cardiovascular diseases, where the participants were invited monthly for blood sampling up to five times and their coronary artery plaque burden was assessed using computed tomography coronary angiography (CTCA) at the end of the follow-up. Using custom R code, we practice synchronizing the quantitative lipidomic data and their plaque burden data (outcome) and cluster subjects by different plaque types. Using the repeated measure data and linear mixed effects model, we compute population-level properties of lipid species such as within-individual and between-individual variability (coefficients of variation). Finally, we search for lipid species whose visit-to-visit variability is associated with different plaque types.

Preparing for the workshop

Please ensure you install R, RStudio and used R packages before the start of the workshop, as detailed below.

R and RStudio

• R (version 4.1 (or higher). Download from https://cloud.r-project.org/. Check your R version by running following command in your console: R.Version()$version.string

• RStudio (newest version, at least 2022.02). Download from https://posit.co/download/rstudio-desktop/. Check your RStudio version by either looking clicking About RStudio under the menu Help.

R packages

Please install following packages before the start of the workshop by copy/paste/enter following lines into your R console:

# CRAN packages

install.packages(c("here", "tidyverse", "broom", "ggrepel", "ggpmisc", "circlize", "scales", "devtools"))

# Bioconductor packages

if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") 
BiocManager::install(c("impute", "ComplexHeatmap", "rgoslin"))

Let us know if you have any issues!

Download the R scripts

Download the R Project containing the scripts and data used in this workshop from this repository (click on the green button "<>Code" and then "Download ZIP"). Alternatively, you can clone this repository using RStudio or Git.

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Do not hesitate to contact us if you have any questions.

Authors

• Bo Burla - Singapore Lipidomics Incubator @ NUS

• Hyungwon Choi - Computational & Statistical Systems Biology Laboratory @ NUS

Acknowledgments

• Members of SLING

• Participants of the SLING R-sessions

• Authors of the dataset used in this workshop: Tan et al., Atheroscler Thromb Vasc Biol, 2021 DOI: 10.1161/atvbaha.121.316847

License

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