Comparing the Performance of One-Color Versus Two-Color Gene-Ex

Supplemental Material

Supplemental Figure 1 (Suppl_Figure_1.pdf)

Comparison of classification results from one-color vs. two-color training data using 10 iterations of 5x cross validation. The data shown are the mean results from 10 independent runs. A set of 9986 common probes and 244 samples was used to train the classifiers. For each endpoint, eight different classification methods were applied, namely discriminant analysis (DA), general linear model selection (GLM), logistic regression (LR), partial least square (PLS), partition tree (PT), radial basis machine (RBM), prediction analysis of microarrays (PAM) and support vector machines plus recursive feature elimination (SVM+RFE) were selected for prediction. Values for Accuracy (A) and root mean squared error (B) of the prediction results using one-color and two-color data are plotted against each other. Endpoints are coded by color: OS_MO (endpoint J), red; EFS_MO (endpoint K), green; patient's sex (endpoint L), blue; random classes (endpoint M), orange.

Supplemental Figure 2 (Suppl_Figure_2.pdf)

Comparison of classification results from one-color vs. two-color data on an independent validation set. A set of 9986 common probes and 244 samples was used to train the classifiers. Eight classification methods, discriminant analysis (DA), general linear model selection (GLM), logistic regression (LR), partial least square (PLS), partition tree (PT), radial basis machine (RBM), prediction analysis of microarrays (PAM) and support vector machines plus recursive feature elimination (SVM+RFE) were selected for prediction. Accuracy (A) and root mean squared error (B) of the prediction results using one-color and two-color data are plotted against each other. Endpoints are coded by color: OS_MO (endpoint J), red; EFS_MO (endpoint K), green; patient's sex (endpoint L), blue; random classes (endpoint M), orange.

Supplemental Table 1 (Suppl_Table_S1.xls)

Datasets and endpoints used in the MAQC-II project.

Supplemental Table S2 (Suppl_Table_S2.xls)

Clinical characteristics of neuroblastoma samples from the training set.

Supplemental Table S3 (Suppl_Table_S3.xls)

Clinical characteristics of neuroblastoma samples from the validation set.

Supplemental Table S4 (Suppl_Table_S4.xls)

Differences between the one-color and the two-color hybridization protocols.

Supplemental Table S5 (Suppl_Table_S5.xls)

Detailed results of classification performances in internal validation by 5-fold cross validation and on an independent validation set.

Supplemental Methods (Supplemental_Methods.doc)

Details of classifier training and prediction.

Supplemental Description of MAQC-II Project
(TPJ_MAQC-II_Mauscripts_Highlights_02NOV2009.pdf)

Description of overall design and course of the MAQC-II project as well as of further manuscripts related to this project. A detailed description is given in ref. 1.