Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data.

Area of Science:

• Microbiology
• Bioinformatics
• Data Science

Background:

• High-throughput DNA sequencing generates complex microbiome data.
• The microbiome plays a critical role in health and disease.
• Understanding microbiome alterations is vital for various interventions.

Purpose of the Study:

• To highlight compositional data analysis (CDA) methods for microbiome data.
• To emphasize the importance of exploratory data analysis (EDA) in microbiome research.
• To review new analytical methods and their rationale.

Main Methods:

• Workshop presentation on CDA principles and rationale.
• Demonstration of CDA methods using two microbiome datasets.
• Review of bioinformatics methodologies for microbiome analysis.

Main Results:

• CDA provides a framework for analyzing compositional microbiome data.
• EDA is essential for exploring and understanding microbiome datasets.
• Careful analysis is required due to the complexity of microbiome data.

Conclusions:

• Compositional data analysis is a key methodology for microbiome research.
• A strong understanding of bioinformatics and data types is necessary.
• Accurate analysis of microbiome data is critical for health and disease studies.