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

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.

[1]  Adeeb Rahman,et al.  Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data , 2017, Scientific Data.

[2]  Bernard Desgraupes Clustering Indices , 2016 .

[3]  Hui Xiong,et al.  Understanding of Internal Clustering Validation Measures , 2010, 2010 IEEE International Conference on Data Mining.

[4]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[5]  Nuria Lopez-Bigas,et al.  Gitools: Analysis and Visualisation of Genomic Data Using Interactive Heat-Maps , 2011, PloS one.

[6]  Christian A. Rees,et al.  Systematic variation in gene expression patterns in human cancer cell lines , 2000, Nature Genetics.

[7]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[8]  Sean R. Davis,et al.  GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor , 2007, Bioinform..

[9]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[10]  Jaak Vilo,et al.  ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap , 2015, Nucleic Acids Res..

[11]  C Maithri,et al.  Parallel K-Means Implementation for Data Clustering Using Hadoop Map-Reduce , 2018, Journal of Computational and Theoretical Nanoscience.

[12]  Jinwook Seo,et al.  XCluSim: a visual analytics tool for interactively comparing multiple clustering results of bioinformatics data , 2015, BMC Bioinformatics.

[13]  Jeffrey Heer,et al.  D³ Data-Driven Documents , 2011, IEEE Transactions on Visualization and Computer Graphics.

[14]  Daniel Svozil,et al.  InCHlib – interactive cluster heatmap for web applications , 2014, Journal of Cheminformatics.

[15]  Dirk Merkel,et al.  Docker: lightweight Linux containers for consistent development and deployment , 2014 .

[16]  M. Schatz,et al.  Big Data: Astronomical or Genomical? , 2015, PLoS biology.

[17]  Edward Y. Chang,et al.  Parallel Spectral Clustering in Distributed Systems , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Jeffrey Heer,et al.  SpanningAspectRatioBank Easing FunctionS ArrayIn ColorIn Date Interpolator MatrixInterpola NumObjecPointI Rectang ISchedu Parallel Pause Scheduler Sequen Transition Transitioner Transiti Tween Co DelimGraphMLCon IData JSONCon DataField DataSc Dat DataSource Data DataUtil DirtySprite LineS RectSprite , 2011 .

[19]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[20]  Zhen Hu,et al.  WebGimm: An integrated web-based platform for cluster analysis, functional analysis, and interactive visualization of results , 2010, Source Code for Biology and Medicine.

[21]  John Quackenbush,et al.  WebMeV: a Cloud Platform for Analyzing and Visualizing Cancer Genomic Data , 2017, bioRxiv.

[22]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .