Figure 2

Class prediction. Using a class prediction algorithm, a list of genes that most consistently distinguished diseased vs. normal samples was generated. Classification was generated by the k-nearest neighbors algorithm (26). The number of neighbors selected was six, with a decision cutoff for P value ratio of 0.2. The final list was determined by an iterative cross-validation process in which the best combination of number of genes and neighbors was found to derive the most discriminating list. In the cross-validation mode, each sample in turn was set aside as the test article, and the remainder of the samples were used to generate the model, which was then evaluated on the test article. (A) Fold change and P values of the 29 prediction genes. (B) Unsupervised hierarchical cluster analysis of the 29 genes. The expression patterns of 29 genes are displayed in a dendrogram where columns represent each sample and rows represent individual genes. Genes are colored on a gradient (from −10-fold to 10-fold) with those increase in expression relative to the average of the control in red. Those that decrease are in blue, and those with little or no change are in yellow.