Chapter 1 Introduction

1.1 What is metabolomics?

Metabolomics is the large-scale study of small molecules, commonly known as metabolites, within cells, biofluids, tissues or organisms. Collectively, these small molecules and their interactions within a biological system are known as the metabolome.

Overview of the four major omics fields, from genomics to metabolomics

Figure 1.1: Overview of the four major omics fields, from genomics to metabolomics

Just as genomics is the study of DNA and genetic information within a cell, and transcriptomics is the study of RNA and differences in mRNA expression; metabolomics is the study of substrates and products of metabolism, which are influenced by both genetic and environmental factors.

Metabolomics is a powerful approach because metabolites and their concentrations, unlike other “omics” measures, directly reflect the underlying biochemical activity and state of cells / tissues. Thus metabolomics best represents the molecular phenotype.

1.2 Workflow of metabolomics data analysis

In this tutorial, two modules would be introduced:

  • Statistical Analysis

  • Functional Analysis

In Statistical Analysis module, you will learn to preprocess metabolomics data and use different statistical tests to find potential metabolomics biomarkers. After that, you could explore the possible functions of the identified metabolomics biomarkers through Functional analysis (including Enrichment Analysis and Pathway Analysis).

Below we showed the details of the workflow:

Workflow in Data Analysis on metabolomics

Figure 1.2: Workflow in Data Analysis on metabolomics

1.2.1 Statistical Analysis

Due to terrible experience on Statistical Analysis in Metabolomics via MetaboAnalystR R package, we try to provide a reproducible and easy-to-use template for visualization, pre-processing, exploration, and statistical analysis on metabolomic data by other packages and scripts. Here, the template comprises the following procedures:

  1. Data Processing

    • Data Checking

    • Data Filtering

    • Missing Value Imputation

    • Data Normalization

  2. Cluster Analysis

    • Hierarchical Clustering

    • Partitional Clustering

  3. Chemometrics Analysis

    • Principal Component Analysis (PCA)

    • Partial Least Squares-Discriminant Analysis (PLS-DA)

    • Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA)

  4. Univariate Analysis

    • Fold Change Analysis

    • T Tests

    • Wilcoxon Test

    • Limma Test

    • Wilcoxon Test

    • Volcano plot

    • Correlation Heatmaps

    • glasso

  5. Feature selection

    • Lasso

    • Ridge

    • Elasticnet

  6. Classification

    • Random Forest

  7. Network Analysis

    • SPRING

    • Spearman

    • SparCC

    • Network comparison

1.2.2 Functional Analysis

Following two chapters would focus on the Enrichment Analysis and Pathway Analysis of metabolomic data. Enrichment Analysis includes three sections (i.e., ORA, SSP and QEA) and Pathway Analysis only includes ORA and QEA.

The main difference between Enrichment Analysis and Pathway Analysis are the data set that input metabolites are enriched to. In Enrichment Analysis, input metabolites are enriched to pre-defined metabolite sets while in Pathway Analysis, metabolites are enriched to pathways in KEGG.

Workflow of Enrichment analysis and Pathway analysis is attached below. Users can choose analysis module according to their data type or interest.
Flowchart

  1. Enrichment Analysis

    • Single Sample Profiling

    • Over representation analysis

    • Quantitative Enrichment Analysis

  2. Pathway Analysis

    • Over representation analysis

    • Quantitative Enrichment Analysis

1.3 Software

1.4 Reference