propr

propr implements proportionality measures for analyzing compositional (relative abundance) data to identify pairwise proportional relationships in genomic and microbiome datasets.

Key Features:

• Proportionality analysis: Focuses on proportionality rather than correlation to assess pairwise associations in compositional (relative abundance) data.
• Multiple measures of proportionality: Implements three distinct measures of proportionality to provide alternative metrics for assessing proportional relationships.
• Computational implementation: Implemented as an R package and reported to be computationally efficient for large-scale genomic and microbiome datasets.

Scientific Applications:

• Genomics: Detects proportional relationships among gene expression features and other relative abundance measurements in genomic studies.
• Microbiome studies: Identifies proportionally abundant microbial taxa and pairwise associations in microbial abundance data.
• Relative-abundance studies: Applies to any analyses relying on relative abundance measurements where correlation-based methods may be inappropriate.

Methodology:

Computes three distinct measures of proportionality grounded in the mathematical principles of compositional data analysis and implemented as computational routines in R.