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

Tauno Metsalu,Jaak Vilo

doi:10.1093/nar/gkv468

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/.

Highlights

With the advent of high-throughput experiments, scientists are often confronted with multivariate data, usually presented as a matrix
We present a web tool called ClustVis that aims to make this type of analysis easier
ClustVis is aiming for an intuitive user interface

Summary

Introduction

With the advent of high-throughput experiments, scientists are often confronted with multivariate data, usually presented as a matrix. This type of data can come from a variety of sources, for example gene expression studies where looking at specific genetic pathways is of great interest [1]. PCA is a method where a multivariate data set is linearly transformed into a set of uncorrelated variables, ordered in descending manner by the variance explained [3]. This way, one can interpret first few components that often explain large amount of the variation

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Conclusion