Scalable approaches for analysis of human genome-wide ...

ASupplementary Results for Gene Set StatisticsA.1. ClassifiersIn addition to the centroid classifier, we tested the shrunken centroid (Tibshirani et al.,2003) in the R package pamr (Hastie et al., 2009b), our implementation of the classifierfrom (van ’t Veer et al., 2002), and a support vector machine with a linear kernel (kernlabpackage (Karatzoglou et al., 2004)). We optimised the shrunken centroid’s threshold and theSVM’s number of features and its l 2 penalty using nested random splits, where the data wasrandomly split into three parts: training, validation, and testing. The model was fit to thetraining data, and its AUC calculated for its prediction on the validation data. This wasrepeated over a grid of values appropriate for each model type. The optimal hyperparameterswere then chosen as the ones maximising the AUC over the validation set. The model wasthen refit using the optimal hyperparameters on the training and validation data together,and tested on the remaining test data. Its AUC over the test data is reported. The wholeprocedure is repeated B times, producing B classifiers (for each classifier type), with differentsets of optimal hyperparameters. The procedure is performed separately for each of the fivedatasets.There are conflicting descriptions of the exact form of the classifier used in (van ’t Veer etal., 2002). In the original paper, it seems that the classifier classifies each sample using itsPearson correlation with each of the centroids of the positive and negative metastasis classes:ŷ i = arg min {Corr(x i, c j )},j∈{−1,+1}(A.1)205