Clustering gene expression regulators: new approach to disease subtyping

Abstract

One of the main challenges in modern medicine is to stratify different patient groups in terms of underlying disease molecular mechanisms as to develop more personalized approach to therapy. Here we propose novel method for disease subtyping based on analysis of activated expression regulators on a sample-by-sample basis. Our approach relies on Sub-Network Enrichment Analysis algorithm (SNEA) which identifies gene subnetworks with significant concordant changes in expression between two conditions. Subnetwork consists of central regulator and downstream genes connected by relations extracted from global literature-extracted regulation database. Regulators found in each patient separately are clustered together and assigned activity scores which are used for final patients grouping. We show that our approach performs well compared to other related methods and at the same time provides researchers with complementary level of understanding of pathway-level biology behind a disease by identification of significant expression regulators. We have observed the reasonable grouping of neuromuscular disorders (triggered by structural damage vs triggered by unknown mechanisms), that was not revealed using standard expression profile clustering. For another experiment we were able to suggest the clusters of regulators, responsible for colorectal carcinoma vs adenoma discrimination and identify frequently genetically changed regulators that could be of specific importance for the individual characteristics of cancer development. Proposed approach can be regarded as biologically meaningful feature selection, reducing tens of thousands of genes down to dozens of clusters of regulators. Obtained clusters of regulators make possible to generate valuable biological hypotheses about molecular mechanisms related to a clinical outcome for individual patient.

Figure 1. Overall pipeline of the proposed approach for disease subtyping.

See corresponding section for detailed description.

Figure 2. Heatmap of activity scores (k-values) for clusters of regulators identified in GSE4183 dataset.

Samples are in columns, clusters of regulators are in rows. Horizontal side bar color encodes true class labels.

Figure 3. Comparison of clustering of 12 diseases of human muscle.

A) Dendrogram obtained using proposed approach based on analysis of regulators activity. B) Dendrogram obtained using Ward's method for clustering gene expression data.