Data-Fusion in Clustering Microarray Data: Balancing Discovery and Interpretability

作者：Kustra Rafal^*; Zagdanski Adam

来源：IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2010, 7(1): 50-63.

DOI：10.1109/TCBB.2007.70267

• Clustering expression data
• Gene Ontology
• genomic data fusion
• semantic similarity
• cluster stability
• knowledge-based validation

While clustering genes remains one of the most popular exploratory tools for expression data, it often results in a highly variable and biologically uninformative clusters. This paper explores a data fusion approach to clustering microarray data. Our method, which combined expression data and Gene Ontology (GO)-derived information, is applied on a real data set to perform genome-wide clustering. A set of novel tools is proposed to validate the clustering results and pick a fair value of infusion coefficient. These tools measure stability, biological relevance, and distance from the expression-only clustering solution. Our results indicate that a data-fusion clustering leads to more stable, biologically relevant clusters that are still representative of the experimental data.

• 出版日期2010-3