Spatial Cellular Networks from Omics Data



The increasing quantity and quality of spatially resolved omics data provide a unique opportunity to explore the complex molecular mechanisms that take place in biological systems. However, to understand the system, it is not sufficient to only observe and describe it. Mathematical models are needed which are able to holistically integrate all available molecular readouts and their spatial context to elucidate the molecular networks active in an organism, comprising both intra- and intercellular communication.  
The inference of molecular networks using statistical approaches has been a hot topic for many years. Probabilistic graphical models (PGMs) are among the most popular statistical network inference methods and were repeatedly studied for the analysis of high-dimensional omics data, comprising transcriptomic and proteomic data. In this talk, I present an instance of PGMs which takes into account spatial context of molecular variables in order to elucidate the cellular communication that takes place within and between cells.

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