MOTIVATION: Several methods have been developed for trajectory inference in single-cell studies. However, identifying relevant lineages among several cell types and interpreting the results of downstream analysis remains a challenging task that requires deep understanding of various cell type transitions and progression patterns. Therefore, there is a need for methods that can aid researchers in the analysis and interpretation of such trajectories. RESULTS: We developed PyEvoCell, a dashboard for trajectory interpretation and analysis that is augmented by large language model (LLM) capabilities. PyEvoCell applies the LLM to the outputs of trajectory inference methods such as Monocle3, to suggest biologically relevant lineages. Once a lineage is defined, users can conduct differential expression and functional analyses which are also interpreted by the LLM. Finally, any hypothesis or claim derived from the analysis can be validated using the veracity filter, a feature enabled by the LLM, to confirm or reject claims by providing relevant PubMed citations. AVAILABILITY AND IMPLEMENTATION: The software is available at https://github.com/Sanofi-Public/PyEvoCell. It contains installation instructions, user manual, demo datasets, as well as license conditions. https://doi.org/10.5281/zenodo.15114803.

We present in this paper a new approach for constructing implicit social networks from electronic sources, like emails, SMS and phone calls. The main novelty is the use of Social Interaction Analysis to assist end-users in their communication needs. We discuss how a social proximity can be calculated between two persons in a social network and show how a weighted, directed network is constructed based on interactions between people. After a description of the architecture of the framework, we show how a contextual, weighted social network can help in automatically finding the contact with highest probability to know the whereabouts of a person who is currently not reachable. The implemented social helper application uses a specific contextual view of the social network, exploiting only interactions that occurred in the last 48 hours.