Metabolomics 2023: Spectra processing, functional integration and covariate adjustment of global metabolomics data using MetaboAnalyst 5.0
For this workshop, we will be covering three workflows:
- Auto-Optimized LC-MS spectra processing and peak annotation;
- Statistical Analysis of One-factor experimental design;
- Functional analysis from LC-MS peaks;
- Exploratory statistical analysis with complex metadata.
Details for each workflow are below.
Before you start, please download our Protocols for MetaboAnalyst 5.0 here as a reference.
The aim of this workflow is to use LC-MS Spectra Processing module in MetaboAnalyst to analyze the raw spectra data from a real-world experiments. This section includes raw spectral data format conversion and centroiding. Then we will show a quick demon on how to use MetaboAnalyst for raw data processing in the auto-optimized mode. The data was obtained using untargeted metabolomics (Q-Exactive Plus Orbitrap MS in positive ion mode) of blood samples from 6 malaria semi-immune patients and 6 naive controls. 6 pooled QC samples are also included. In this section, students will learn how to run the auto-optimized raw spectra processing workflow.
(Optionally) To practice raw spectral data conversion and centroiding, users could download some raw thermo-fisher spectral data (*.raw) from this link.
For this workflow, users could use the 2nd example directly from the module page or optionally download here. For the learning purpose, you are strongly encouraged to use the 1st example directly to run the whole process quickly.
The tutorial of this module is available here for further reference. Watch this video to see a live demo of raw spectra data processing with MetaboAnalyst.
The aim of this workflow is the use the Statistical Analysis [One Factor] module in MetaboAnalyst to analyze a concentration table with a simple experimental design. By simple experimental design, we mean a dataset with a single, categorical metadata (ie. Treatment: Control, Drug A, Drug B). This section includes outlier and batch effect detection, missing value imputation, filtering, normalization, transformation, and statistical analysis. Then we will show a quick demo with Example Data 3 to show how the pipeline works.
The aim of this workflow is to introduce the pathway analysis of untargeted metabolomics data using the Functional Analysis module. The theory and processing steps of mummichog is described. Users can use the first examples directly from the module page for practicing. The tutorial of this module is available here for further reference. Watch this video to see a live demo of functional analysis with MetaboAnalyst.
The aim of this workflow is to perform statistical analysis based on complex metadata. Covariate analysis will be used to deal with a metabolomics data, which is highly affected by multiple metadata factors. This study mainly focus on a peak intensity table from a COVID-19 study (Example Data 1) for covariate analysis. The data consists of untargeted metabolomics (a peak table) of 2054 metabolites and a metadata table. The data are from 59 individuals (20 healthy, 39 with COVID-19), and there are metadata values for age, sex, diagnosis, and treatment.
To understand more background knowledge on metabolomics data analysis further, you can follow these documents/protocols below.
Using MetaboAnalyst for Metabolomics Data Analysis
General Concepts & Workflow in Omics Data Analysis
Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis