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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
Multilevel comparison of dendrograms: a new method with an application for genetic classifications
János Podani1, Attila Engloner, Agnes Major
1Eotvos University. podani@ludens.elte.hu
Summary
We developed a new method to compare hierarchical clustering dendrograms, finding maximum agreement between them. This approach reveals hidden genetic information and is useful across scientific fields using clustering.
Area of Science:
- * Bioinformatics
- * Computational Biology
- * Population Genetics
Background:
- * Hierarchical classifications and dendrograms are used to represent data structures.
- * Existing cluster validity tests compare partitions to a reference, but may miss nuanced agreements.
- * Detecting clones and population structures requires robust comparison of hierarchical clustering results.
Purpose of the Study:
- * To propose an exhaustive search procedure for comparing two dendrograms.
- * To identify hierarchical levels with maximum agreement between dendrograms.
- * To assess the utility of the method for genetic data and general scientific applications.
Main Methods:
- * Developed an exhaustive search algorithm to compare all partitions from two dendrograms.
- * Applied the method to Random Amplified Polymorphic DNA (RAPD) and microsatellite data for clone detection in reed populations.
- * Utilized artificial, actual, and simulated dendrograms to evaluate performance.
Main Results:
- * The proposed method successfully detects hierarchical levels of maximum agreement between dendrograms.
- * The approach revealed additional information in genetic datasets that would otherwise remain hidden.
- * Simulations indicated that classical partition dissimilarity coefficients agree when group structure is meaningful, but disagree otherwise.
Conclusions:
- * The novel exhaustive search procedure offers a powerful tool for comparing hierarchical classifications.
- * This method enhances the interpretation of genetic data, particularly for clone and population structure analysis.
- * The approach is broadly applicable in any scientific domain employing hierarchical clustering for result comparison.
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