Figure 1. Summary of the clustering process.

A) Obtaining the bi-dimensional correlation matrix. We part from a dataset with genes in rows, patients in columns and different visitsfor which we have gene expression and clinical variables for every patient. A correlation value for each gene and each patient is calculated throughout visits and a bi-dimensional matrix of genes, patients and correlation values is created. Feature selection is applied to select the best gene candidates to stratify and filter out the rest of genes. B) We performed consensus clustering on pediatric and adult bi-dimensional correlation matrices independently and then a functional and clinical characterization is performed.