Visualization-based cancer microarray data classification analysis.
BACKGROUND
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.


BACKGROUND
VizRank and radviz are implemented as part of the Orange data mining suite (http://www.ailab.si/orange).


BACKGROUND
Supplementary data are available from http://www.ailab.si/supp/bi-cancer.
Mramor
Minca
M
Faculty of Computer and Information Science, University of Ljubljana, Trzaska 25, 1000 Ljubljana, Slovenia.
Leban
Gregor
G
Demsar
Janez
J
Zupan
Blaz
B
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000145:classification / Q000378:metabolism*; D003196:Computer Graphics; D003628:Database Management Systems; D030562:Databases, Protein; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D009363:Neoplasm Proteins / Q000145:classification / Q000378:metabolism*; D009369:Neoplasms / Q000378:metabolism*; D012984:Software; D014584:User-Computer Interface
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins