TMAinspiration: Decode Interdependencies in Multifactorial Tissue Microarray Data.

There are no satisfying tools in tissue microarray (TMA) data analysis up to now to analyze the cooperative behavior of all measured markers in a multifactorial TMA approach. The developed tool TMAinspiration is not only offering an analysis option to close this gap but also offering an ecosystem consisting of quality control concepts and supporting scripts to make this approach a platform for informed practice and further research. The TMAinspiration method is specifically focusing on the demands of the TMA analysis by controlling errors and noise by a generalized regression scheme while at the same time avoiding to introduce a priori too many constraints into the analysis of the data. So, we are testing partitions of a proximity table to find an optimal support for a ranking scheme of molecular dependencies. The idea of combining several partitions to one ensemble, which is balancing the optimization process, is based on the main assumption that all these perspectives on the cellular network need to be self-consistent. Several application examples in breast cancer and one in squamous cell carcinoma demonstrate that this procedure is nicely confirming a priori knowledge on the expression characteristics of protein markers, while also integrating many new results discovered in the treasury of a bigger TMA experiment. The code and software are now freely available at: http://complex-systems.uni-muenster.de/tma_inspiration.html.