Article thumbnail

Comparison of performance of one-color and two-color gene-expression analyses in predicting clinical endpoints of neuroblastoma patients

By A Oberthuer, D Juraeva, L Li, Y Kahlert, F Westermann, R Eils, F Berthold, L Shi, R D Wolfinger, M Fischer and B Brors


Microarray-based prediction of clinical endpoints may be performed using either a one-color approach reflecting mRNA abundance in absolute intensity values or a two-color approach yielding ratios of fluorescent intensities. In this study, as part of the MAQC-II project, we systematically compared the classification performance resulting from one- and two-color gene-expression profiles of 478 neuroblastoma samples. In total, 196 classification models were applied to these measurements to predict four clinical endpoints, and classification performances were compared in terms of accuracy, area under the curve, Matthews correlation coefficient and root mean-squared error. Whereas prediction performance varied with distinct clinical endpoints and classification models, equivalent performance metrics were observed for one- and two-color measurements in both internal and external validation. Furthermore, overlap of selected signature genes correlated inversely with endpoint prediction difficulty. In summary, our data strongly substantiate that the choice of platform is not a primary factor for successful gene expression based-prediction of clinical endpoints

Topics: Original Article
Publisher: Nature Publishing Group
OAI identifier:
Provided by: PubMed Central

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.

Suggested articles


  1. (2001). A simple generalization of the area under the ROC curve for multiple class classification problems. Machine Learning
  2. (1975). Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta
  3. (2006). Customized oligonucleotide microarray gene expression-based classification of neuroblastoma patients outperforms current clinical risk stratification.
  4. (2002). Diagnosis of multiple cancer types by shrunken centroids of gene expression.
  5. (2006). Evaluation of reference-based two-color methods for measurement of gene expression ratios using spotted cDNA microarrays.
  6. (1999). Expression profiling using cDNA microarrays. Nat Genet
  7. (2001). Forty years of decoding the silence in X-chromosome inactivation. Hum Mol Genet
  8. (2002). Fundamentals of experimental design for cDNA microarrays.
  9. (2002). Gene selection for cancer classification using support vector machines. Machine Learning
  10. (2005). Independence and reproducibility across microarray platforms. Nat Methods
  11. (2005). Limma: linear models for microarray data. In: Gentleman
  12. Microarray scanner calibration curves: characteristics and implications.
  13. (2005). Outcome signature genes in breast cancer: is there a unique set? Bioinformatics
  14. (2006). Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project. Nat Biotechnol
  15. (1995). Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science
  16. (1997). Ratio-based decisions and the quantitative analysis of cDNA microarray images.
  17. The MAQC-II Project: a comprehensive study of common practices for the development and validation of microarray-based predictive models.
  18. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition