Icicle Plots: Better Displays for Hierarchical Clustering

Kruskal, Joseph B.; Landwehr, James M.

doi:10.1080/00031305.1983.10482733

Cited by 129 publications

(39 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The associated hierarchical clustering can be visualized using an icicle representation (15), wherein the width of each peak represents the number of its components at a given similarity threshold (Figure 1A). The circularization of the icicle and the use of a power scale for the similarity measure both magnify the useful region of the plot containing clusters of genes with highly similar expression profiles.…”

Section: Resultsmentioning

confidence: 99%

“…MiSTIC offers a simultaneous view of all gene correlations in a set of transcriptomes through the use of minimum spanning trees (14) and a radial projection of the associated hierarchical clustering using an icicle representation (15). Further, MiSTIC was designed to easily navigate back and forth between representations of gene correlation at the level of a global dataset (icicle view), of a gene cluster (by zooming in on individual clusters in the icicle) or of individual genes or transcripts (single gene correlations and pair-wise scatterplots), enabling both global and targeted analyses.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MiSTIC, an integrated platform for the analysis of heterogeneity in large tumour transcriptome datasets

Lemieux

Sargeant

Laperrière

et al. 2017

Nucleic Acids Research

View full text Add to dashboard Cite

Genome-wide transcriptome profiling has enabled non-supervised classification of tumours, revealing different sub-groups characterized by specific gene expression features. However, the biological significance of these subtypes remains for the most part unclear. We describe herein an interactive platform, Minimum Spanning Trees Inferred Clustering (MiSTIC), that integrates the direct visualization and comparison of the gene correlation structure between datasets, the analysis of the molecular causes underlying co-variations in gene expression in cancer samples, and the clinical annotation of tumour sets defined by the combined expression of selected biomarkers. We have used MiSTIC to highlight the roles of specific transcription factors in breast cancer subtype specification, to compare the aspects of tumour heterogeneity targeted by different prognostic signatures, and to highlight biomarker interactions in AML. A version of MiSTIC preloaded with datasets described herein can be accessed through a public web server (http://mistic.iric.ca); in addition, the MiSTIC software package can be obtained (github.com/iric-soft/MiSTIC) for local use with personalized datasets.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MiSTIC, an integrated platform for the analysis of heterogeneity in large tumour transcriptome datasets

Lemieux

Sargeant

Laperrière

et al. 2017

Nucleic Acids Research

View full text Add to dashboard Cite

show abstract

“…These solutions can be visualized as an icicle plot [41] such as that seen in Figure 5, in which the numbers on the top of the plot are labels for each clique. Each row shows a cluster solution with a different number of clusters.…”

Section: Methodsmentioning

confidence: 99%

Clustering cliques for graph-based summarization of the biomedical research literature

et al. 2013

View full text Add to dashboard Cite

BackgroundGraph-based notions are increasingly used in biomedical data mining and knowledge discovery tasks. In this paper, we present a clique-clustering method to automatically summarize graphs of semantic predications produced from PubMed citations (titles and abstracts).ResultsSemRep is used to extract semantic predications from the citations returned by a PubMed search. Cliques were identified from frequently occurring predications with highly connected arguments filtered by degree centrality. Themes contained in the summary were identified with a hierarchical clustering algorithm based on common arguments shared among cliques. The validity of the clusters in the summaries produced was compared to the Silhouette-generated baseline for cohesion, separation and overall validity. The theme labels were also compared to a reference standard produced with major MeSH headings.ConclusionsFor 11 topics in the testing data set, the overall validity of clusters from the system summary was 10% better than the baseline (43% versus 33%). While compared to the reference standard from MeSH headings, the results for recall, precision and F-score were 0.64, 0.65, and 0.65 respectively.

show abstract

“…As in the case of square treemaps, information is conveyed by the size of a rectangle representing a node; an individual with a large number of child nodes is represented by a larger node than one with a small number of dominating individuals, and the thickness of each layer decreases to show the diminishing importance of each subsequent Pareto shell. We note that the construction of these visualisations is similar to the icicle plot [29], which arranges clusters of nodes together so that they descend, in a similar way to how nodes here are arranged inwards. Both methods provide a similar view on the data; the circular design used herein is preferred as it keeps the extent of the visualisation constrained to a smaller space.…”

Section: Multi-objective Populations: Treemapsmentioning

confidence: 99%

Visualisation with treemaps and sunbursts in many-objective optimisation

Walker

2018

Genet Program Evolvable Mach

View full text Add to dashboard Cite

Visualisation is an important aspect of evolutionary computation, enabling practitioners to explore the operation of their algorithms in an intuitive way and providing a better means for displaying their results to problem owners. The presentation of the complex data arising in many-objective evolutionary algorithms remains a challenge, and this work examines the use of treemaps and sunbursts for visualising such data. We present a novel algorithm for arranging a treemap so that it explicitly displays the dominance relations that characterise many-objective populations, as well as considering approaches for creating trees with which to represent multi- and many-objective solutions. We show that treemaps and sunbursts can be used to display important aspects of evolutionary computation, such as the diversity and convergence of a search population, and demonstrate the approaches on a range of test problems and a real-world problem from the literature.

show abstract

Icicle Plots: Better Displays for Hierarchical Clustering

Cited by 129 publications

References 6 publications

MiSTIC, an integrated platform for the analysis of heterogeneity in large tumour transcriptome datasets

MiSTIC, an integrated platform for the analysis of heterogeneity in large tumour transcriptome datasets

Clustering cliques for graph-based summarization of the biomedical research literature

Visualisation with treemaps and sunbursts in many-objective optimisation

Contact Info

Product

Resources

About