Classification of microarrays; synergistic effects between normalization, gene selection and machine learning
1 Umeå Plant Science Center, Department of Plant Physiology, Umeå University, 901 87 Umeå, Sweden
2 Department of Clinical Microbiology, Division of Clinical Bacteriology, Umeå University, 901 85 Umeå, Sweden
3 Department of Mathematics and Mathematical Statistics, Umeå University, 901 87 Umeå, Sweden
4 Computational Life Science Cluster (CLiC), Umeå University, 901 87 Umeå, Sweden
5 Department of Medical Sciences, Uppsala University, Academic Hospital, 751 85 Uppsala, Sweden
BMC Bioinformatics 2011, 12:390 doi:10.1186/1471-2105-12-390Published: 7 October 2011
Machine learning is a powerful approach for describing and predicting classes in microarray data. Although several comparative studies have investigated the relative performance of various machine learning methods, these often do not account for the fact that performance (e.g. error rate) is a result of a series of analysis steps of which the most important are data normalization, gene selection and machine learning.
In this study, we used seven previously published cancer-related microarray data sets to compare the effects on classification performance of five normalization methods, three gene selection methods with 21 different numbers of selected genes and eight machine learning methods. Performance in term of error rate was rigorously estimated by repeatedly employing a double cross validation approach. Since performance varies greatly between data sets, we devised an analysis method that first compares methods within individual data sets and then visualizes the comparisons across data sets. We discovered both well performing individual methods and synergies between different methods.
Support Vector Machines with a radial basis kernel, linear kernel or polynomial kernel of degree 2 all performed consistently well across data sets. We show that there is a synergistic relationship between these methods and gene selection based on the T-test and the selection of a relatively high number of genes. Also, we find that these methods benefit significantly from using normalized data, although it is hard to draw general conclusions about the relative performance of different normalization procedures.