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. 2015 Jul 1;43(W1):W566-70.
doi: 10.1093/nar/gkv468. Epub 2015 May 12.

ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap

Tauno Metsalu  1 , Jaak Vilo  2
Affiliations

ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap

Tauno Metsalu et al. Nucleic Acids Res. .

Abstract

The Principal Component Analysis (PCA) is a widely used method of reducing the dimensionality of high-dimensional data, often followed by visualizing two of the components on the scatterplot. Although widely used, the method is lacking an easy-to-use web interface that scientists with little programming skills could use to make plots of their own data. The same applies to creating heatmaps: it is possible to add conditional formatting for Excel cells to show colored heatmaps, but for more advanced features such as clustering and experimental annotations, more sophisticated analysis tools have to be used. We present a web tool called ClustVis that aims to have an intuitive user interface. Users can upload data from a simple delimited text file that can be created in a spreadsheet program. It is possible to modify data processing methods and the final appearance of the PCA and heatmap plots by using drop-down menus, text boxes, sliders etc. Appropriate defaults are given to reduce the time needed by the user to specify input parameters. As an output, users can download PCA plot and heatmap in one of the preferred file formats. This web server is freely available at http://biit.cs.ut.ee/clustvis/.

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Figures

Figure 1.
Figure 1.
PCA plot of stromal molecular signatures of breast and prostate cancer samples. Ellipses and shapes show clustering of the samples.
Figure 2.
Figure 2.
Heatmap of stromal molecular signatures of breast and prostate cancer samples. Annotations on top of the heatmap show clustering of the samples.
See this image and copyright information in PMC

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