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| Signaling pathway analysis and interpretation of the massive gene expression data is a challenging task. Clustering technology is one of the useful and popular methods to obtain these patterns inherent in the data. In order to find the best way to establish theoretical p38 MAPK signaling pathway based on Head and neck squamous cell carcinoma expression profiles, we use and compare common hierarchical clustering algorithms, such as Pearson’s correlation, Euclidean distance, Euclidean distance harmonic, spearman rank correlation, kendall’s tau, City-block distance etc, The algorithm constructs a hierarchy from top to bottom based on a self-organizing tree. It dynamically finds the number of clusters at each level.We find that,Spearman rank correlation, kendall’s tau, City-block distance, and Euclidean Distance are all fit to the analysis of the cascade composed from a MAP3K11(MLK3), a MAP2K3(MKK3), a MAPK11(P38 MAPK/β) toMEF2A in head and neck squamous cell carcinoma expression profiles. The result is consistent with the biological experimental p38 MAPK signaling pathway. |
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| Keywords:p38 MAPK，gene expression，clustering，signaling patheway |
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