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Related Experiment Video

Updated: Jul 8, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Using repeated measurements to validate hierarchical gene clusters.

Laurent Bréhélin¹, Olivier Gascuel, Olivier Martin

¹Méthodes et Algorithmes pour la Bioinformatique, LIRMM, CNRS - University Montpellier II, France. brehelin@lirmm.fr

Bioinformatics (Oxford, England)

|January 22, 2008

Summary

This study introduces a new method to assess the stability of gene clusters found using hierarchical clustering, especially when dealing with repeated experiments. The approach helps identify reliable gene clusters and avoid misleading interpretations in gene expression data analysis.

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Published on: February 15, 2017

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Hierarchical clustering is a standard unsupervised method for analyzing gene and protein expression data.
Experimental data often includes repetitions due to biological and technical variability.
Evaluating cluster stability is crucial for reliable interpretation of coordinated gene or protein expression patterns.

Purpose of the Study:

To develop and validate a novel approach for assessing the stability of clusters generated by hierarchical clustering.
To account for repeated measurements in experimental data to improve cluster reliability.
To differentiate between stable and dubious clusters in gene expression analysis.

Main Methods:

Utilizes the bootstrap technique to generate pseudo-hierarchies from resampled gene expression datasets.
Employs a dynamic programming algorithm to compare original hierarchies with pseudo-hierarchies.
Incorporates a shuffling procedure to assess the statistical significance of observed cluster stabilities.

Main Results:

The proposed method effectively evaluates cluster stability by considering repeated measurements.
Demonstrated utility on simulated data and two real-world microarray datasets.
Successfully distinguishes between stable and potentially unreliable gene clusters, enhancing data interpretation.

Conclusions:

The developed approach provides a robust framework for evaluating hierarchical clustering stability in the presence of experimental repetitions.
This method aids researchers in avoiding misleading conclusions drawn from unstable clusters.
The implemented programs are available in C and R.