SiMCAL 1 algorithm for analysis of gene expression data related to the phosphatidylserine receptor.

Objective: SiMCAL 1 (simple multilevel clustering and linking, version 1) is a novel clustering algorithm for time-series microarray data, presented here with an application to a specific data set. The purpose of the algorithm is to present a complete feature set not found in either Jarvis-Patrick clustering, from which it is derived, or in other popular clustering methods such as hierarchical and k-means. The data concern the activity of the phosphatidylserine receptor (PSR) which is believed to be a crucial molecular switch in the mediation of inflammatory response in apoptosis and lysis. By analyzing the behavior of PSR-related genes in mouse macrophages, we hope to elucidate the mechanisms involved in this important biological process.

Methods And Materials: SiMCAL 1 is implemented in the Python programming language using the Numerical Python extensions, and the data are stored using the MySQL database management system. The data are derived from exposures of multiple Affymetrix mouse gene microarray chips to elevated levels of PSR antibody and control conditions. Code and data are available at (accessed: 17 January 2005).

Results: The algorithm meets its objectives: it is simple, in that it is computationally inexpensive; it is multilevel, in that it provides a small number of clearly defined hierarchical levels of clusters; and it offers linking between clusters at the same level in each hierarchy. Clustering and linking results indicate previously unknown co-regulation for genes expressing PGH synthase (COX2) and PGE2, appear to confirm increased production of proteins for clearance of apoptotic cells in the presence of PSR antibody, and correspond to other findings regarding the temporal relationship between PGE2 production and B cell proliferation and differentiation. These results are promising but should be taken as highly preliminary.

Conclusion: Both the algorithm and its application to this problem show great potential for future development. We plan to improve and extend the SiMCAL family of algorithms, and to obtain new data so that the algorithm(s) may be further applied to this and other problems of interest.