Analysis of intracellular and intercellular crosstalk from omics data.

Disease phenotypes can be described as the consequence of interactions among molecular processes that are altered beyond resilience. Here, we address the challenge of assessing the possible alteration of intra- and inter-cellular molecular interactions among gene sets, which are intended to represent processes and or cellular phenotypes. We present an approach, designated as "Ulisse", which complements the existing methods of enrichment analysis and cell-cell communication analysis. It can be applied to a gene list as well as multiple ranked gene lists, typically derived in the context of omics or multi-omics studies. The approach highlights the presence of alterations in those components that control the interactions between processes or cells. Crosstalk quantification is supported by two null models. Further, the approach provides an additional way of identifying the genes associated with the phenotype. As a proof-of-concept, we applied Ulisse to study the alteration of pathway crosstalks and cell-cell communications in triple negative breast cancer samples, based on single-cell RNA sequencing. In conclusion, our work supports the usefulness of crosstalk analysis as an additional instrument in the "toolkit" of biomedical research for translating complex biological data into actionable insights.