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Omics data visualization in R and Python
  • Introduction
    • From Authors
    • Virtual environments - let's begin
    • Getting started with Python
    • Getting started with R
    • Example data sets
  • PERFORMING FUNDAMENTAL OPERATIONS ON OMICs DATA USING R
    • Fundamental data structures
    • Loading data into R
    • Preferred formats in metabolomics and lipidomics analysis
    • Preprocess data type using Tidyverse package
    • Useful R tricks and features in OMICs mining
      • Application of pipe (%>%) functions
      • Changing data frames format with pivot_longer()
      • Data wrangling syntaxes useful in OMICs mining
      • Writing functions in R
      • The 'for' loop in R (advanced)
  • PERFORMING FUNDAMENTAL OPERATIONS ON OMICs DATA USING PYTHON
    • Fundamental data structures
    • Loading data into Python
  • Missing values handling in R
    • Missing values – Introduction
    • Detecting missing values (DataExplorer R package)
    • Filtering out columns containing mostly NAs
    • Data imputation by different available R libraries
      • Basic data imputation in R with dplyr and tidyr (tidyverse)
      • Data imputation using recipes library (tidymodels)
      • Replacing NAs via k-nearest neighbor (kNN) model (VIM library)
      • Replacing NAs via random forest (RF) model (randomForest library)
  • Missing values handling in Python
    • Detecting missing values
    • Filtering out columns containing mostly NAs
    • Data imputation
  • Data transformation, scaling, and normalization in R
    • Data normalization in R - fundamentals
    • Data normalization to the internal standards (advanced)
    • Batch effect corrections in R (advanced)
    • Data transformation and scaling - introduction
    • Data transformation and scaling using different available R packages
      • Data transformation and scaling using mutate()
      • Data transformation and scaling using recipes R package
      • Data Normalization – bestNormalize R package
  • Data transformation, scaling, and normalization in Python
    • Data Transformation and scaling in Python
  • Metabolites and lipids descriptive statistical analysis in R
    • Computing descriptive statistics in R
    • Using gtsummary to create publication-ready tables
    • Basic plotting in R
      • Bar charts
      • Box plots
      • Histograms
      • Density plots
      • Scatter plots
      • Dot plots with ggplot2 and tidyplots
      • Correlation heat maps
    • Customizing ggpubr and ggplot2 charts in R
    • Creating interactive plots with ggplotly
    • GGally for quick overviews
  • Metabolites and lipids descriptive statistics analysis in Python
    • Basic plotting
    • Scatter plots and linear regression
    • Correlation analysis
  • Metabolites and lipids univariate statistics in R
    • Two sample comparisons in R
    • Multi sample comparisons in R
    • Adjustments of p-values for multiple comparisons
    • Effect size computation and interpretation
    • Graphical representation of univariate statistics
      • Results of tests as annotations in the charts
      • Volcano plots
      • Lipid maps and acyl-chain plots
  • Metabolites and lipids univariate statistical analysis in Python
    • Two sample comparisons in Python
    • Multi-sample comparisons in Python
    • Statistical annotations on plots
  • Metabolites and lipids multivariate statistical analysis in R
    • Principal Component Analysis (PCA)
    • t-Distributed Stochastic Neighbor Embedding (t-SNE)
    • Uniform Manifold Approximation and Projection (UMAP)
    • Partial Least Squares (PLS)
    • Orthogonal Partial Least Squares (OPLS)
    • Hierarchical Clustering (HC)
      • Dendrograms
      • Heat maps with clustering
      • Interactive heat maps
  • Metabolites and lipids multivariate statistical analysis in Python
    • Principal Component Analysis
    • t-Distributed Stochastic Neighbor Embedding
    • Uniform Manifold Approximation and Projection
    • PLS Discriminant Analysis
    • Clustered heatmaps
  • OMICS IN MACHINE LEARNING APPROACHES IN R AND PYTHON
    • Application of selected models to OMICs data
    • OMICs machine learning – Examples
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  1. Introduction

From Authors

Before you dive into this book

NextVirtual environments - let's begin

Last updated 1 month ago

Welcome!

Welcome to the first version of Omics Data Visualization in R and Python! This GitBook is designed to be your go-to resource for mastering data visualization, statistical computations, and best practices in R and Python, with a focus on lipidomics and metabolomics datasets.

Our goal is simple: to bring together the most effective code snippets, visualizations, and computational approaches—all presented in a clear and accessible way. R and Python have become essential tools for omics data analysis, offering powerful, free-of-charge solutions for generating high-quality visual insights and conducting complex statistical analyses.

We believe that learning these tools should be accessible to everyone. That’s why we created this GitBook—a comprehensive collection of everything a young lipidomics or metabolomics researcher needs to analyze bulk omics data efficiently.

Let’s dive in and explore the world of omics data visualization together! 🚀

The authors

Getting Started with R and Python

For Beginners:

If you're just starting your journey with R or Python, we recommend following these steps: 1️⃣ Before modifying the code for your own data, first work through the examples in this GitBook using the dataset we’ve provided. 2️⃣ Reproduce the visualizations on your computer and ensure you understand each step of the code. 3️⃣ Once you're comfortable with the process, start adapting the code snippets to your own data.

You can download the general dataset from Introduction: Example Data Sets (always start with dataset no. 1). If a different dataset is used in a specific chapter, we’ll always provide the source.

For Those with Some Experience:

If you already have a basic understanding of R and Python but want to test specific code snippets, you can download the example dataset from Introduction: Example Data Sets (dataset no. 1).

Happy coding! 🚀

Spotted an Issue? Here’s How to Report It

Dear GitBook reader, if you encounter any inconsistencies or errors in the R or Python code, please let us know at lipidomicsrpython@kuleuven.be. Please use the example data set no. 1 (from Introduction: Example data sets) to present why the code does not work.

Is your favorite R or Python library missing? Let us know if you think it should be included! We will gladly update the repository so everyone can learn R and Python freely!

Last update of the GitBook: 20.03.2025

Authors and Affiliations

Jakub Idkowiak

  • University of Pardubice, Faculty of Chemical Technology, Department of Analytical Chemistry, Czech Republic;

  • Laboratory of Lipid Metabolism and Cancer, KU Leuven and Leuven Cancer Institute (LKI), Belgium.

Jonas Dehairs

  • Laboratory of Lipid Metabolism and Cancer, KU Leuven and Leuven Cancer Institute (LKI), Belgium.

Jana Schwarzerová

  • Brno University of Technology, Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering, Czech Republic;

  • Molecular Systems Biology (MOSYS), Department of Functional and Evolutionary Ecology, Faculty of Life Sciences, University of Vienna, Austria;

  • Department of Molecular and Clinical Pathology and Medical Genetics, University Hospital Ostrava, Czech Republic.

Dominika Olešová

  • Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Slovakia;

  • Institute of Neuroimmunology, Slovak Academy of Sciences, Slovakia.

Jacob X.M. Truong

  • South Australian Health and Medical Research Institute (SAHMRI), Australia;

  • South Australian immunoGENomics Cancer Institute (SAiGENCI) & Freemasons Centre for Male Health and Well-Being, The University of Adelaide Medical School, Australia.

Aleš Kvasnička

  • Laboratory for Inherited Metabolic Disorders, Department of Clinical Biochemistry, University Hospital Olomouc and Faculty of Medicine and Dentistry, Palacký University, Czech Republic;

  • Department of Medical Biochemistry, Oslo University Hospital, Norway.

Marios Eftychiou

  • Laboratory for Integrative Cancer Genomics, VIB-KU Leuven Center for Cancer Biology, Belgium;

  • VIB Center for AI & Computational Biology, Belgium;

  • Department of Oncology, KU Leuven, Belgium;

  • Laboratory of Multi-Omic Integrative Bioinformatics, Department of Human Genetics, KU Leuven, Belgium.

Ruben Cools

  • Laboratory for Integrative Cancer Genomics, VIB-KU Leuven Center for Cancer Biology, Belgium;

  • VIB Center for AI & Computational Biology, Belgium;

  • Department of Oncology, KU Leuven, Belgium.

Xander Spotbeen

  • Laboratory of Lipid Metabolism and Cancer, KU Leuven and Leuven Cancer Institute (LKI), Belgium.

Robert Jirásko

  • University of Pardubice, Faculty of Chemical Technology, Department of Analytical Chemistry, Czech Republic.

Vullnet Veseli

  • University of Pardubice, Faculty of Chemical Technology, Department of Analytical Chemistry, Czech Republic.

Marco Giampà

  • Laboratory of Applied Mass Spectrometry, Department of Cellular and Molecular Medicine, KU Leuven, Belgium;

  • Metabolomics Core Facility, VIB KU Leuven Center for Cancer Biology, Belgium.

Vincent de Laat

  • Laboratory of Lipid Metabolism and Cancer, KU Leuven and Leuven Cancer Institute (LKI), Belgium.

Lisa M. Butler

  • South Australian Health and Medical Research Institute (SAHMRI), Australia;

  • South Australian immunoGENomics Cancer Institute (SAiGENCI) & Freemasons Centre for Male Health and Well-Being, The University of Adelaide Medical School, Australia.

Wolfram Weckwerth

  • Molecular Systems Biology (MOSYS), Department of Functional and Evolutionary Ecology, Faculty of Life Sciences, University of Vienna, Austria;

  • Vienna Metabolomics Center (VIME), University of Vienna, Austria.

David Friedecký

  • Laboratory for Inherited Metabolic Disorders, Department of Clinical Biochemistry, University Hospital Olomouc and Faculty of Medicine and Dentistry, Palacký University, Czech Republic.

Jonas Demeulemeester

  • Laboratory for Integrative Cancer Genomics, VIB-KU Leuven Center for Cancer Biology, Belgium;

  • VIB Center for AI & Computational Biology, Belgium;

  • Department of Oncology, KU Leuven, Belgium.

Karel Hron

  • Palacký University Olomouc, Faculty of Science, Department of Mathematical Analysis and Applications of Mathematics, Czech Republic.

Johannes V. Swinnen

  • Laboratory of Lipid Metabolism and Cancer, KU Leuven and Leuven Cancer Institute (LKI), Belgium.

Project supervision:

Michal Holčapek

  • University of Pardubice, Faculty of Chemical Technology, Department of Analytical Chemistry, Czech Republic.

Cover design and preparation - Bogna Idkowiak.