Guidelines to Statistical Analysis of Microbial Composition Data Inferred from Metagenomic Sequencing

Area of Science:

• Microbiology
• Bioinformatics
• Statistics

Background:

• Metagenomics uses high-throughput DNA sequencing to analyze microbial communities in diverse environments.
• Primary analysis yields semi-quantitative compositional data, essential for understanding community structure.
• These data have unique statistical properties that impact analysis and interpretation.

Purpose of the Study:

• To introduce researchers to the statistical properties of microbial compositional data in metagenomics.
• To review available software tools designed for analyzing such data.
• To provide recommendations for the appropriate application of statistical methods.

Main Methods:

• Review of statistical properties of compositional data.
• Survey of publicly available bioinformatics software.
• Development of recommendations for data analysis.

Main Results:

• Compositional data in metagenomics exhibit specific statistical characteristics.
• Numerous software tools exist for analyzing metagenomic compositional data.
• Guidelines for appropriate statistical method application are proposed.

Conclusions:

• Proper statistical consideration of metagenomic compositional data is vital for reliable scientific conclusions.
• Researchers need awareness of these data's unique properties and available tools.
• Adherence to recommended methods ensures robust interpretation of microbial community surveys.