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• W2040209571 startingPage "4290" @default.
• W2040209571 abstract "Hierarchical clustering is a common procedure for identifying structure in a dataset, and this is frequently used for organizing genomic data. Although more advanced clustering algorithms are available, the simplicity and visual appeal of hierarchical clustering have made it ubiquitous in gene expression data analysis. Hence, even minor improvements in this framework would have significant impact. There is currently no simple and systematic way of assessing and displaying the significance of various clusters in a resulting dendrogram without making certain distributional assumptions or ignoring gene-specific variances. In this work, we introduce a permutation test based on comparing the within-cluster structure of the observed data with those of sample datasets obtained by permuting the cluster membership. We carry out this test at each node of the dendrogram using a statistic derived from the singular value decomposition of variance matrices. The p-values thus obtained provide insight into the significance of each cluster division. Given these values, one can also modify the dendrogram by combining non-significant branches. By adjusting the cut-off level of significance for branches, one can produce dendrograms with a desired level of detail for ease of interpretation. We demonstrate the usefulness of this approach by applying it to illustrative datasets." @default.
• W2040209571 created "2016-06-24" @default.
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• W2040209571 creator A5058596390 @default.
• W2040209571 creator A5081285392 @default.
• W2040209571 date "2009-10-01" @default.
• W2040209571 modified "2023-09-27" @default.
• W2040209571 title "A permutation test for determining significance of clusters with applications to spatial and gene expression data" @default.
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• W2040209571 doi "https://doi.org/10.1016/j.csda.2009.05.031" @default.
• W2040209571 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3023458" @default.
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