We introduce Plotviz, a data visualization tool developed at Indiana University to display 2 and 3 dimensional data. The motivation is that the human eye is very good at pattern recognition and can ‘’see’’ structure in data. Although most Big data is higher dimensional than 3, all can be transformed by dimension reduction techniques to 3D. He gives several examples to show how the software can be used and what kind of data can be visualized. This includes individual plots and the manipulation of multiple synchronized plots.Finally, he describes the download and software dependency of Plotviz.
We introduce Plotviz, a data visualization tool developed at Indiana University to display 2 and 3 dimensional data. The motivation is that the human eye is very good at pattern recognition and can ‘’see’’ structure in data. Although most Big data is higher dimensional than 3, all can be transformed by dimension reduction techniques to 3D. He gives several examples to show how the software can be used and what kind of data can be visualized. This includes individual plots and the manipulation of multiple synchronized plots. Finally, he describes the download and software dependency of Plotviz.
Slides (34 pages): https://iu.app.box.com/s/jypomnrz755xgps5e6iw
Files:
Fungi_LSU_3_15_to_3_26_zeroidx.pvizDatingRatings-OriginalLabels.pvizClusterFinal-M30-C28.pvizclusterFinal-M3-C3Dating-ReClustered.pvizThe motivation of Plotviz is that the human eye is very good at pattern recognition and can ‘’see’’ structure in data. Although most Big data is higher dimensional than 3, all data can be transformed by dimension reduction techniques to 3D and one can check analysis like clustering and/or see structure missed in a computer analysis. The motivations shows some Cheminformatics examples. The use of Plotviz is started in slide 4 with a discussion of input file which is either a simple text or more features (like colors) can be specified in a rich XML syntax. Plotviz deals with points and their classification (clustering). Next the protein sequence browser in 3D shows the basic structure of Plotviz interface. The next two slides explain the core 3D and 2D manipulations respectively. Note all files used in examples are available to students.
Video: 7:58: Motivation: http://youtu.be/4aQlCmQ1jfY
Initially we start with a simple plot of 8 points – the corners of a cube in 3 dimensions – showing basic operations such as size/color/labels and Legend of points. The second example shows a dataset (coming from GTM dimension reduction) with significant structure. This has .pviz and a .txt versions that are compared.
Video: 9:45: Example I: http://youtu.be/nCTT5mI_j_Q
This starts with an examination of a sample of Protein Universe Browser showing how one uses Plotviz to look at different features of this set of Protein sequences projected to 3D. Then we show how to compare two datasets with synchronized rotation of a dataset clustered in 2 different ways; this dataset comes from k Nearest Neighbor discussion.
Video: 9:14: Proteomics and Synchronized Rotation: http://youtu.be/lDbIhnLrNkk
This starts by describing use of Labels and Glyphs and the Default mode in Plotviz. Then we illustrate sophisticated use of these ideas to view a large Proteomics dataset.
Video: 8:37: Larger Proteomics Sample: http://youtu.be/KBkUW_QNSvs
This lesson starts by describing the Plotviz tools and then sets up two examples – Oil Flow and Trading – described in PowerPoint. It finishes with the Plotviz viewing of Oil Flow data.
Video: 10:17: Plotviz I: http://youtu.be/zp_709imR40
This starts with Plotviz looking at Trading example introduced in previous lesson and then examines solvent data. It finishes with two large biology examples with 446K and 100K points and each with over 100 clusters. We finish remarks on Plotviz software structure and how to download. We also remind you that a picture is worth a 1000 words.
Video: 14:58: Plotviz II http://youtu.be/FKoCfTJ_cDM
Download files from http://salsahpc.indiana.edu/pviz3/