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. 2018:4:e155.
doi: 10.7717/peerj-cs.155. Epub 2018 May 21.

ClusterEnG: an interactive educational web resource for clustering and visualizing high-dimensional data

Mohith Manjunath  1   2 , Yi Zhang  1   2 , Steve H Yeo  2 , Omar Sobh  2 , Nathan Russell  3 , Christian Followell  3 , Colleen Bushell  3 , Umberto Ravaioli  4 , Jun S Song  1   2   5
Affiliations

ClusterEnG: an interactive educational web resource for clustering and visualizing high-dimensional data

Mohith Manjunath et al. PeerJ Comput Sci. 2018.

Abstract

Summary: Clustering is one of the most common techniques used in data analysis to discover hidden structures by grouping together data points that are similar in some measure into clusters. Although there are many programs available for performing clustering, a single web resource that provides both state-of-the-art clustering methods and interactive visualizations is lacking. ClusterEnG (acronym for Clustering Engine for Genomics) provides an interface for clustering big data and interactive visualizations including 3D views, cluster selection and zoom features. ClusterEnG also aims at educating the user about the similarities and differences between various clustering algorithms and provides clustering tutorials that demonstrate potential pitfalls of each algorithm. The web resource will be particularly useful to scientists who are not conversant with computing but want to understand the structure of their data in an intuitive manner.

Availability: ClusterEnG is part of a bigger project called KnowEnG (Knowledge Engine for Genomics) and is available at http://education.knoweng.org/clustereng.

Contact: songi@illinois.edu.

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Conflict of interest statement

Conflict of Interest: none declared.

Figures

Figure 1
Figure 1. Typical workflow of ClusterEnG encompassing educational and visualization components.
Figure 2
Figure 2. A partial snapshot of ClusterEnG user interface showing a choice of clustering algorithms and related options.
Figure 3
Figure 3. NCI60 gene expression sample data clustering of samples using k-medoids algorithm.
The snapshots show visualizations of first three principal components and vectors from PCA and t-SNE, respectively, in (A) 2D and (B) 3D with perspective and orthogonal projection of principal components.
Figure 4
Figure 4. Dynamic clustering application in affinity propagation using R Shiny server displaying heatmap of similarity matrix of selected data points.
Figure 5
Figure 5. Benchmarking results illustrating algorithm run time for the clustering algorithms in ClusterEnG.
“PCA time” data indicates the time taken to compute principal components, a step common to all the algorithms for visualization.
See this image and copyright information in PMC

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