Cancer Subtype Discovery and Biomarker Identification via a New Robust Network Clustering Algorithm

• Published in: PloS one Vol. 8; no. 6; p. e66256
• Main Authors: Wu, Meng-Yun, Dai, Dao-Qing, Zhang, Xiao-Fei, Zhu, Yuan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 17.06.2013; Public Library of Science (PLoS)
• Subjects: Algorithms; Bioindicators; Biology; Biomarkers; Biomarkers, Tumor - genetics; Biomarkers, Tumor - metabolism; Cancer; Cell cycle; Cluster Analysis; Clustering; Clusters; Computer simulation; Covariance; Data analysis; Datasets; DNA microarrays; Engineering; Fines & penalties; Gene expression; Genes; Humans; Leukemia; Lymphoma; Mathematics; Medicine; Methods; Models, Theoretical; Neoplasms - genetics; Neoplasms - metabolism; Outliers (statistics); Parameter estimation; Random variables; Robustness; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0066256

In cancer biology, it is very important to understand the phenotypic changes of the patients and discover new cancer subtypes. Recently, microarray-based technologies have shed light on this problem based on gene expression profiles which may contain outliers due to either chemical or electrical reasons. These undiscovered subtypes may be heterogeneous with respect to underlying networks or pathways, and are related with only a few of interdependent biomarkers. This motivates a need for the robust gene expression-based methods capable of discovering such subtypes, elucidating the corresponding network structures and identifying cancer related biomarkers. This study proposes a penalized model-based Student's t clustering with unconstrained covariance (PMT-UC) to discover cancer subtypes with cluster-specific networks, taking gene dependencies into account and having robustness against outliers. Meanwhile, biomarker identification and network reconstruction are achieved by imposing an adaptive [Formula: see text] penalty on the means and the inverse scale matrices. The model is fitted via the expectation maximization algorithm utilizing the graphical lasso. Here, a network-based gene selection criterion that identifies biomarkers not as individual genes but as subnetworks is applied. This allows us to implicate low discriminative biomarkers which play a central role in the subnetwork by interconnecting many differentially expressed genes, or have cluster-specific underlying network structures. Experiment results on simulated datasets and one available cancer dataset attest to the effectiveness, robustness of PMT-UC in cancer subtype discovering. Moveover, PMT-UC has the ability to select cancer related biomarkers which have been verified in biochemical or biomedical research and learn the biological significant correlation among genes.