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The criterion of minimal total variance yields the most closed clusters.
Genes from closed clusters (n = 122) indeed had higher tissue specificity and thus tighter transcriptional control than their counterparts from open clusters (n = 72) as their expression breadth metric tau[ 20] was significantly higher (p < 10-8, t-test and U-test).
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The phylogenetic groups of phyla and class levels are shown aside the close clusters.
Clusters are updated at regular intervals of the algorithm to allow merging of close clusters approaching the same local minima.
In the post-monsoon session, the closest clusters generated were that of calcium-total hardness and sulfate-chloride.
The cluster-size-based scaling improves the clustering by increasing the separability of close clusters, especially when they are of disparate size.
The two closest clusters are then merged in a recursive manner, with the new cluster adopting the mean similarity from all cluster members.
The aggregation is performed according to the hierarchical relationship between clusters, such that hierarchically closer clusters are less aggregated and further away clusters are progressively more aggregated.
We randomly pick a first cluster and randomly sample from one of the top four closest clusters based on Euclidian distance.
For selecting template representatives, we use the agglomerative clustering algorithm to further cluster the templates in each tied triphone cluster at a PDT leaf node, which recursively merges two closest clusters into one cluster until only one cluster is left.
The general principle of agglomerative hierarchical clustering is to iteratively merge the closest clusters, until we get one cluster containing all the data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com