Bioinformatics Advance Access originally published online on June 22, 2007
Bioinformatics 2007 23(16):2147-2154; doi:10.1093/bioinformatics/btm312
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Visualization-based cancer microarray data classification analysis
ar 1
Zupan 1,2,*
1Faculty of Computer and Information Science, University of Ljubljana, Traka 25, 1000 Ljubljana, Slovenia and 2Department of Molecular and Human Genetics, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
*To whom correspondence should be addressed.
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Abstract |
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Motivation: Methods for analyzing cancer microarray data often face two distinct challenges: the models they infer need to perform well when classifying new tissue samples while at the same time providing an insight into the patterns and gene interactions hidden in the data. State-of-the-art supervised data mining methods often cover well only one of these aspects, motivating the development of methods where predictive models with a solid classification performance would be easily communicated to the domain expert.
Results: Data visualization may provide for an excellent approach to knowledge discovery and analysis of class-labeled data. We have previously developed an approach called VizRank that can score and rank point-based visualizations according to degree of separation of data instances of different class. We here extend VizRank with techniques to uncover outliers, score features (genes) and perform classification, as well as to demonstrate that the proposed approach is well suited for cancer microarray analysis. Using VizRank and radviz visualization on a set of previously published cancer microarray data sets, we were able to find simple, interpretable data projections that include only a small subset of genes yet do clearly differentiate among different cancer types. We also report that our approach to classification through visualization achieves performance that is comparable to state-of-the-art supervised data mining techniques.
Availability: VizRank and radviz are implemented as part of the Orange data mining suite (http://www.ailab.si/orange).
Contact: blaz.zupan{at}fri.uni-lj.si
Supplementary information: Supplementary data are available from http://www.ailab.si/supp/bi-cancer.
Associate Editor: Jonathan Wren
Received on February 12, 2007; revised on May 17, 2007; accepted on June 5, 2007
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