Shape-influenced clustering of dynamic patterns of gene profiles

Statistical evaluation of temporal gene expression profiles plays an important role in particular biological processes and conditions. We introduce a clustering method for this purpose, which is based on the expression patterns but is also influenced by temporal changes. We compare the results of our platform with methods based on expression or the rank of temporal changes. The proposed platform is illustrated with a temporal gene expression dataset comprised of primary human chondrocytes and mesenchymal stem cells (MSCs). We derived three clusters in each cell type and compared the content of these classes in terms of temporal changes, which can support biological performance. For statistical evaluation we introduce a validity measure that takes under consideration these temporal changes and we also perform an enrichment analysis of three central genes in each cluster. Even though we can detect certain statistical similarities, these might be due to different biological processes. Our proposed platform contributes to both the statistical and biological validation of temporal profiles.