Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics...

Area of Science:

• Computational Biology
• Bioinformatics
• Data Science

Background:

• Lipidomics and metabolomics generate large datasets requiring advanced data exploration skills.
• Identifying statistically significant trends and biological differences necessitates specialized visualization techniques.
• Existing tailored methods are lab-specific, highlighting a need for accessible, standardized tools.

Purpose of the Study:

• To review and compile freely accessible R and Python tools for exploratory data analysis and visualization of omics data.
• To guide researchers, particularly beginners, in developing skills for analyzing and visualizing complex biological datasets.
• To promote the use of R and Python for robust and reproducible chemometric analysis in omics research.

Main Methods:

• Compilation of existing, freely available R and Python libraries for data analysis and visualization.
• Inclusion of methods for descriptive statistics, hypothesis testing, and various plot types (e.g., box plots, volcano plots, heat maps).
• Guidance on unsupervised and supervised dimensionality reduction techniques and hierarchical clustering (dendrograms).

Main Results:

• A curated selection of R and Python tools for essential omics data exploration tasks is presented.
• The review covers preparation of descriptive statistics, annotated box plots, hypothesis testing, volcano plots, lipid maps, and fatty acyl chain plots.
• Guidance is provided on unsupervised and supervised dimensionality reduction, dendrograms, and heat maps for comprehensive data visualization.

Conclusions:

• Freely accessible R and Python tools offer robust solutions for analyzing and visualizing omics data.
• This guide empowers researchers to perform reproducible chemometric analysis and generate publication-ready graphics.
• The associated GitBook repository provides step-by-step instructions for practical application of these data analysis techniques.