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Models for genetic clustering also vary by algorithms and programs used to process the data. Most sophisticated methods for determining clusters can be categorized as model-based clustering methods (such as the algorithm STRUCTURE [12]) or multidimensional summaries (typically through principal component analysis).
The distance between each gene in the gene cluster can vary. The DNA found between each repeated gene in the gene cluster is non-conserved. [10] Portions of the DNA sequence of a gene is found to be identical in genes contained in a gene cluster. [5] Gene conversion is the only method in which gene clusters may become homogenized. Although the ...
Cluster analysis or clustering is the task of grouping a set of objects in such a way that ... The similarity of genetic data is used in clustering to infer ...
WGCNA can be used as a data reduction technique (related to oblique factor analysis), as a clustering method (fuzzy clustering), as a feature selection method (e.g. as gene screening method), as a framework for integrating complementary (genomic) data (based on weighted correlations between quantitative variables), and as a data exploratory ...
For EST data, clustering is important to group sequences originating from the same gene before the ESTs are assembled to reconstruct the original mRNA. Some clustering algorithms use single-linkage clustering , constructing a transitive closure of sequences with a similarity over a particular threshold.
Clustering data is a tool used to simplify statistical analysis of a genomic sample. For example, in [9] the authors developed a tool (BiG-SCAPE) to analize sequence similarity networks of biosynthetic gene clusters (BGC).
Another statistical analysis tool is Rank Sum Statistics for Gene Set Collections (RssGsc), which uses rank sum probability distribution functions to find gene sets that explain experimental data. [30] A further approach is contextual meta-analysis, i.e. finding out how a gene cluster responds to a variety of experimental contexts.
For a clustering example, suppose that five taxa (to ) have been clustered by UPGMA based on a matrix of genetic distances.The hierarchical clustering dendrogram would show a column of five nodes representing the initial data (here individual taxa), and the remaining nodes represent the clusters to which the data belong, with the arrows representing the distance (dissimilarity).