treeclimbR

treeclimbR analyzes hierarchical trees of entities, such as phylogenies or cell types, at different resolutions to pinpoint branches or leaves that contain features of interest in a data-driven way.

Key Features:

• Optimal resolution selection: Selects optimal resolution levels that may differ across different parts of the tree, rather than cutting the tree at an arbitrary level.
• Candidate generation: Uses a tuning parameter to generate multiple candidate resolutions that capture latent signal.
• Data-driven pinpointing: Identifies branches or leaves containing features of interest through thorough inspection of entities across resolutions.

Scientific Applications:

• Microbiome surveys: Analyzes hierarchical relationships in microbiome data to identify biologically relevant resolution levels.
• microRNA surveys: Applies multi-resolution analysis to microRNA datasets to uncover biological associations.
• Single-cell cytometry: Pinpoints cell populations of interest across different resolution levels in cytometry data.
• Single-cell RNA-seq: Examines hierarchical cell type relationships in single-cell RNA-seq datasets at data-dependent resolutions.

Methodology:

treeclimbR uses a tuning parameter to generate candidate resolutions and from these selects the optimal one.