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Another advantage of the approach is that it bypasses the problem of choosing linkage rules and dissimilarity measures, the choice of which cannot always be traced back to theoretical economic criteria.
We will only consider inversion-free linkage rules here.
Hierarchical cluster diagrams were built using a Pearson centered distance measure under average linkage rules.
After standardizing the data, cluster analysis was performed using squared Euclidean distances and Ward's method linkage rules (Ward 1963).
Given the high number of clustering and classification algorithms, it remains difficult to select an appropriate algorithm based on distance measures, linkage rules and many other parameters.
Unsupervised hierarchical clustering using average linkage rules and a Pearson centered distance metric was performed to assess the overall degree of gene expression similarity among samples [ 18].
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Clusters were linked using the Ward's linkage rule that minimizes intra-cluster variance.
Therefore, no linkage rule is required in seriation in addition to the distance metric used to define similarities.
Trees were re-sampled by jackknifing with 1,000 iterations with Pearson correlation as the distance matric and complete linkage rule.
In contrast, following seriation analysis that does not require a linkage rule, contigs of genes with high pairwise correlation coefficients are revealed by reordering.
Due to this fact, following clustering analysis of gene expression datasets, all genes are assigned to the most appropriate cluster based on a generic linkage rule.
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