Résumé
Modeling genes regulation networks is a major yet challenging stake to understand physiopathology. We show that genes belonging to the same local regulation network have common geometrical variations when their biological function is modified by an environmental exposure. This allows the first topological approach to transcriptomes analysis: the Druplet method.After a database pre-processing, we use UMAP, a dimensional reduction algorithm topology preserving, to sum up the conditions observed. The impacted co-regulated genes tend to set apart. We use a db-scan to isolate those clusters. Finally, we estimate the investment likeliness for each cluster by measuring their individual prediction performance on the studied condition.Our method provides strong inferences on which genes are implied in the cell behavior changes caused by external exposures. Moreover, it provides leads for common transcription factors among genes concerned in specific pathological situations, and thus fornew therapeutic targets.