  Graph-based representation approaches have been proven to be successful in
the analysis of biomedical data, due to their capability of capturing intricate
dependencies between biological entities, such as the spatial organization of
different cell types in a tumor tissue. However, to further enhance our
understanding of the underlying governing biological mechanisms, it is
important to accurately capture the actual distributions of such complex data.
Graph-based deep generative models are specifically tailored to accomplish
that. In this work, we leverage state-of-the-art graph-based diffusion models
to generate biologically meaningful cell-graphs. In particular, we show that
the adopted graph diffusion model is able to accurately learn the distribution
of cells in terms of their tertiary lymphoid structures (TLS) content, a
well-established biomarker for evaluating the cancer progression in oncology
research. Additionally, we further illustrate the utility of the learned
generative models for data augmentation in a TLS classification task. To the
best of our knowledge, this is the first work that leverages the power of graph
diffusion models in generating meaningful biological cell structures.
