Visualization-based cancer microarray data classification analysis

Minca Mramor and Gregor Leban and Janez Demsar and Blaz Zupan (2007) Visualization-based cancer microarray data classification analysis. Bioinformatics, 23 (16). pp. 2147-2154.

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Abstract

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 perform-ance 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 separa-tion of data instances of different class. We here extend VizRank with techniques to uncover outliers, score features (genes) and per-form 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, interpret-able 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). SUPPLEMENTARY MATERIAL: Supplementary material is available from http://www.ailab.si/supp/bi-cancer.

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