A Systems Biology Approach for Unsupervised Clustering of High-Dimensional Data


One main challenge in modern medicine is the discovery of molecular disease subtypes characterized by relevant clinical differences, such as survival. However, clustering high-dimensional expression data is challenging due to noise and the curse of high-dimensionality. This article describes a disease subtyping pipeline that is able to exploit the important information available in pathway databases and clinical variables. The pipeline consists of a new feature selection procedure and existing clustering methods. Our procedure partitions a set of patients using the set of genes in each pathway as clustering features. To select the best features, this procedure estimates the relevance of each pathway and fuses relevant pathways. We show that our pipeline finds subtypes of patients with more distinctive survival profiles than traditional subtyping methods by analyzing a TCGA colon cancer gene expression dataset. Here we demonstrate that our pipeline improves three different clustering methods: k-means, SNF, and hierarchical clustering.

Second International Workshop on Machine Learning, Optimization and Big Data