Comparison of similarity coefficients used for cluster analysis with dominant markers in maize (Zea mays L)

By A. Meyer, Antonio Augusto F. Garcia, Anete P. Souza, C. L. Souza. 2004

162 citations recorded by the source corpus.

The objective of this study was to evaluate whether different similarity coefficients used with dominant markers can influence the results of cluster analysis, using eighteen inbred lines of maize from two different populations, BR-105 and BR-106. These were analyzed by AFLP and RAPD markers and eight similarity coefficients were calculated: Jaccard, Sorensen-Dice, Anderberg, Ochiai, Simple-matching, Rogers and Tanimoto, Ochiai II and Russel and Rao. The similarity matrices obtained were compared by the Spearman correlation, cluster analysis with dendrograms (UPGMA, WPGMA, Single Linkage, Complete Linkage and Neighbour-Joining methods), the consensus fork index between all pairs of dendrograms, groups obtained through the Tocher optimization procedure and projection efficiency in a two-dimensional space. The results showed that for almost all methodologies and marker systems, the Jaccard, Sorensen-Dice, Anderberg and Ochiai coefficient showed close results, due to the fact that all of them exclude negative co-occurrences. Significant alterations in the results for the Simple Matching, Rogers and Tanimoto, and Ochiai II coefficients were not observed either, probably due to the fact that they all include negative co-occurrences. The Russel and Rao coefficient presented very different results from the others in almost all the cases studied and should not be used, because it excludes the negative co-occurrences in the numerator and includes them in the denominator of their expression. Due to the fact that the negative co-occurrences do not necessarily mean that the regions of the DNA are identical, the use of coefficients that do not include negative co-occurrences was suggested.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/48f3947a89bda371df875b2672da2f5a1c151d04

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Rui Abreu
2. A. V. Gemund
3. Kotagiri Ramamohanarao
4. Peter Zoeteweij

In Plant Science

1. Rui Abreu
2. A. V. Gemund
3. Peter Zoeteweij

Citations in the corpus

• A model for spectra-based software diagnosis (2011, methodology): “These results are reasonably consistent with previous empirical studies comparing Ochiai, Jaccard, Tarantula and Ample for software diagnosis [Abreu et al. 2007] and Ochiai, Simple Matching and Rogers and Tanimoto for another domain [Meyer et al. 2004].”
• Invasive species of Heracleum in Europe: an insight into genetic relationships and invasion history (2007, unknown): “Genetic similarity among individuals was estimated from the number of shared amplified fragments using Dice’s (1945) and Jaccard’s (1908) similarity coefficients as this approach is appropriate for analysis of dominant markers (Meyer et al., 2004).”
• The DStar Method for Effective Software Fault Localization (2014, background): “Moreover, we prove that Harmonic Mean and Rogot2 are equivalent (as Rogot2 equals ) even though this has not been previously shown in [10], [19], [27].”
• On the Accuracy of Spectrum-based Fault Localization (2007, methodology): “We experimentally identified the latter as giving the best results among all eight coefficients used in a data clustering study in molecular biology [7], which also included the Jaccard coefficient.”
• An Evaluation of Similarity Coefficients for Software Fault Localization (2006, methodology): “Several other similarity coefficients are used in data clustering (see [4] for a study in the context of molecular biology).”