iterClust

iterClust performs iterative clustering to identify hierarchical population structure by separating major group differences (e.g., populations A and B) and revealing subpopulation distinctions (e.g., B1 and B2).

Key Features:

• Iterative Clustering Approach: Employs repeated rounds of clustering that first isolate pronounced differences between major population groups and then target subtler variation within subpopulations.
• Comprehensive Clustering Trajectory: Produces a refined, multi-level clustering trajectory that captures both large-scale and fine-scale population structure.

Scientific Applications:

• Population Genetics: Distinguishes closely related subpopulations and resolves hierarchical population structure in genetic studies.
• Bioinformatics Research: Supports analyses in genomic and transcriptomic studies requiring multi-level clustering of samples.

Methodology:

Applies an iterative process that initially focuses on larger population distinctions and then progressively refines clusters to detect finer-scale subpopulation differences.