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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

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RhierBAPS: An R implementation of the population clustering algorithm hierBAPS.

Gerry Tonkin-Hill¹, John A Lees², Stephen D Bentley¹

¹Parasites and Microbes, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, UK.

Wellcome Open Research

|October 23, 2018

Summary

This study introduces an R implementation of the hierBAPS algorithm, simplifying population structure analysis in genomics. The new package enhances usability, reproducibility, and visualization of genomic data clustering.

Keywords:

R clustering population structure

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Identifying population structure in genomic sequence data is crucial for evolutionary and ecological studies.
The hierBAPS algorithm is a widely used tool for this purpose but has historically been limited by its MATLAB-only, binary format.
This limitation hindered accessibility and integration into broader bioinformatics workflows.

Purpose of the Study:

To develop and release an accessible R implementation of the hierBAPS algorithm.
To automate the population structure analysis pipeline for haploid genomes.
To enhance the usability, performance, and visualization capabilities of hierBAPS.

Main Methods:

Developed an R package providing a user-friendly interface for the hierBAPS algorithm.
Automated the entire population structure identification pipeline.
Integrated parallel processing capabilities for improved computational efficiency.
Incorporated an interface with the ggtree library for enhanced visualization of clustering results.

Main Results:

The R package offers a simplified installation and usage process compared to the original MATLAB binary.
The implementation supports multiprocessing, significantly speeding up analyses.
The integration with ggtree allows for clear and informative illustration of population structure.
The package improves the reproducibility of population structure analyses.

Conclusions:

The new R package democratizes access to the hierBAPS algorithm for population structure analysis.
It streamlines the workflow, enhances computational performance, and improves the interpretability of results.
This contributes to advancing genomic data analysis and reproducibility in population genetics.