scECODA

scECODA provides a complete workflow for the analysis and visualization of compositional data, primarily focusing on cell type proportions derived from single-cell data.

Key Features:

• Compositional Data Analysis: Implements specialized methods such as the Centered Log-Ratio (CLR) transformation to properly analyze proportional data while avoiding biases introduced by the compositional constraint.
• Data Management: Encapsulates data management, transformation, and analysis into a single SummarizedExperiment object.
• Dimensionality Reduction: Offers downstream tools for dimensionality reduction via PCA.
• Sample Grouping Quality Metrics: Calculates critical metrics including the Adjusted Rand Index (ARI) and Modularity to quantify sample grouping quality.
• Visualization: Generates high-quality visualizations including heatmaps and scatter plots.

Scientific Applications:

• Single-Cell Cell Type Proportion Analysis: Analyzes cell type proportions derived from single-cell data while accounting for compositional constraints.
• Compositional Data Exploration: Enables exploratory analysis of proportional datasets where relative abundances rather than absolute counts are the primary focus.

Methodology:

scECODA applies Centered Log-Ratio (CLR) transformation for compositional data analysis, performs dimensionality reduction via PCA, and calculates the Adjusted Rand Index (ARI) and Modularity for quantifying sample grouping quality.