# Analysis of intracellular and intercellular crosstalk from omics data

## TL;DR

The paper introduces Ulisse, a new method to analyze how molecular processes and cell communication are altered in diseases like breast cancer using omics data.

## Contribution

Ulisse introduces a novel approach combining pathway crosstalk and cell-cell communication analysis with two null models for statistical validation.

## Key findings

- Ulisse identifies altered molecular interactions in triple-negative breast cancer using single-cell RNA sequencing data.
- The method provides actionable insights by highlighting genes and pathways involved in disease phenotypes.
- Ulisse complements existing tools by offering a new way to assess inter- and intracellular crosstalk.

## Abstract

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 processes or cells. We present an approach, designated as “Ulisse”, which complements the existing methods in the domains of enrichment analysis, pathway crosstalk analysis and cell-cell communication analysis. It applies to gene lists that contain quantitative information about gene-related alterations, typically derived in the context of omics or multi-omics studies. Ulisse highlights the presence of alterations in those components that control the interactions between processes or cells. Considering the complexity of statistical assessment of network-based analyses, crosstalk quantification is supported by two distinct null models, which systematically sample alternative configurations of gene-related changes and gene-gene interactions. 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.