Visualization methods for statistical analysis of microarray clusters

Background: The most common method of identifying groups of functionally related genes in microarray data is to apply a clustering algorithm. However, it is impossible to determine which clustering algorithm is most appropriate to apply, and it is difficult to verify the results of any algorithm due to the lack of a gold-standard. Appropriate data visualization tools can aid this analysis process, but existing visualization methods do not specifically address this issue. Results: We present several visualization techniques that incorporate meaningful statistics that are noise-robust for the purpose of analyzing the results of clustering algorithms on microarray data. This includes a rank-based visualization method that is more robust to noise, a difference display method to aid assessments of cluster quality and detection of outliers, and a projection of high dimensional data into a three dimensional space in order to examine relationships between clusters. Our methods are interactive...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Matthew A. Hibbs, Nathaniel C. Dirksen, Kai Li, Olga G. Troyanskaya
• Keywords: Algorithms, BMCBI 2005, Microarray Data, Visualization Method