spathial

spathial identifies genes that drive biological state transitions in high-dimensional genomics data using the Principal Path algorithm.

Key Features:

• R package implementation: Provided as an R package for integration into R-based analysis workflows.
• Principal Path algorithm: Employs the Principal Path algorithm, a topological method that enables local navigation on data manifolds to identify evolutionary paths or transitions.
• Interpretation functions: Includes high-level functions to extract and interpret genes that are important for transitions between biological states.
• Supported data types: Applied to RNA-Seq and single-cell datasets for high-throughput genomics analysis.
• Comparative analysis: Benchmarks identified genes against results from edgeR and monocle3 in tumor progression studies.

Scientific Applications:

• Tumor progression analysis: Identification of genes involved in tumor progression using RNA-Seq and single-cell data.
• Pseudo-temporal and evolutionary inference: Inferring temporal or pseudo-temporal evolution and transitions in biological processes.
• High-throughput genomics: Analysis of high-dimensional genomics experiments to uncover genes underlying complex biological phenomena.

Methodology:

Computational steps explicitly include application of the Principal Path algorithm (a topological method for local navigation on data manifolds), downstream high-level functions to extract genes associated with transitions, and comparative analyses against edgeR and monocle3 on RNA-Seq and single-cell datasets.