Email updates

Keep up to date with the latest news and content from BioData Mining and BioMed Central.

Open Access Open Badges Methodology

Semi-supervised consensus clustering for gene expression data analysis

Yunli Wang1* and Youlian Pan2

Author Affiliations

1 National Research Council Canada, 46 Dineen Dr., Fredericton, Canada

2 National Research Council Canada, 1200 Montreal Rd., Ottawa, Canada

For all author emails, please log on.

BioData Mining 2014, 7:7  doi:10.1186/1756-0381-7-7

Published: 8 May 2014



Simple clustering methods such as hierarchical clustering and k-means are widely used for gene expression data analysis; but they are unable to deal with noise and high dimensionality associated with the microarray gene expression data. Consensus clustering appears to improve the robustness and quality of clustering results. Incorporating prior knowledge in clustering process (semi-supervised clustering) has been shown to improve the consistency between the data partitioning and domain knowledge.


We proposed semi-supervised consensus clustering (SSCC) to integrate the consensus clustering with semi-supervised clustering for analyzing gene expression data. We investigated the roles of consensus clustering and prior knowledge in improving the quality of clustering. SSCC was compared with one semi-supervised clustering algorithm, one consensus clustering algorithm, and k-means. Experiments on eight gene expression datasets were performed using h-fold cross-validation.


Using prior knowledge improved the clustering quality by reducing the impact of noise and high dimensionality in microarray data. Integration of consensus clustering with semi-supervised clustering improved performance as compared to using consensus clustering or semi-supervised clustering separately. Our SSCC method outperformed the others tested in this paper.

Semi-supervised clustering; Consensus clustering; Semi-supervised consensus clustering; Gene expression