biosvd

biosvd performs dimensionality reduction of high-throughput genomic and molecular biology datasets using Singular Value Decomposition (SVD) to extract eigenfeatures and eigenassays for downstream analyses.

Key Features:

• Dimensionality Reduction: Transforms the original feature-by-assay matrix into a diagonalized eigenfeature × eigenassay space to reduce complexity while preserving signal.
• Orthonormal Superpositions: Produces eigenfeatures and eigenassays as orthonormal superpositions of the original features and assays, yielding independent, non-redundant components.
• Visualization Outputs: Generates heatmaps of the eigenfeature × assay matrix and bar plots of eigenexpression fractions to assess patterns and component contributions.
• Bioconductor Integration: Integrated with the Bioconductor ecosystem to enable interoperability with other Bioconductor packages for genomic data analysis.

Scientific Applications:

• High-throughput genomics: Applied to genomic and molecular biology datasets to reduce dimensionality and reveal dominant patterns.
• Downstream analysis support: Facilitates clustering, classification, and pattern recognition on reduced-dimension representations.
• Component interpretation: Enables assessment of relative contributions of eigenfeatures via eigenexpression fractions for biological interpretation.

Methodology:

Uses Singular Value Decomposition (SVD) to decompose the input data matrix into three matrices representing the eigenfeatures, the singular values, and the eigenassays, yielding orthonormal component vectors and associated singular-value–based contributions.