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Array data were clustered using Cluster 3.0.

The gene expression data were clustered using complete-linkage agglomerative hierarchical clustering based on the Euclidean distance (heatmap.2 function in R gplots package).

Data were clustered using a two-way unsupervised clustering method, with uncentered correlation as similarity metrics for both genes and subjects value vectors.

For inference, data were clustered using a mixture model [48] with a mixture of beta distributions [49], and the number of classes was determined by Bayesian information criterion (BIC) [50], [51].

Data were clustered using K-means.

Expression data were clustered using uncentred correlation and average linkage.

During acoustic model training, HMM states that are observed in the training data are clustered using decision trees to avoid overfitting.

Gene expression data was clustered using an unsupervised hierarchical clustering algorithm using PermutMatrix [ 66].

The combined data was clustered using hierarchical clustering (Euclidean distances, complete linkage algorithm).

The expression data was clustered using the self-organizing map algorithm as follows.

These data are clustered using CD-HIT [ 25] with a sequence identity level of 95%%.