Learning the Tree of Phenotypes Using Genomic Data and VISDA

Though supervised and unsupervised analyses of genomic data have been intensively studied in recent years, little effort has been made to discover the structural information contained in the data. In this work, we propose a stability analysis guided supervised clustering and visualization method aiming to discover the hierarchical structure in gene expression data, which we call the "tree of phenotypes". We applied the method on two multiclass gene expression microarray data sets and presented the biological plausibility of the learned trees. We also tested the multiclass classifiers built on the learned trees and demonstrated their good classification performance