Identifying transcription factors driving cell differentiation

Cellular differentiation is a fundamental biological process at the core of development and growth in multicellular organisms. Understanding it at the level of transcriptomic regulation opens the possibility for novel biological insights, for steering cell development for therapeutic purposes, and for developing targeted therapies for diseases. While many methods exist that infer developmental trajectories from single-cell RNA sequencing data, only few can effectively determine which biological mechanisms drive differentiation along such trajectories. To close this gap, we developed SwitchTFI (switch transcription factor identification), a method to identify differentiation-driving regulatory mechanisms and the transcription factors (TFs) that play a key role in them. SwitchTFI infers a cell state transition-specific gene regulatory network (GRN) from a user-provided baseline GRN via decision stump learning and permutation-based significance tests. Key TFs are then extracted by ranking them according to their centralities in the transition GRN. We show that SwitchTFI can identify TFs known to be involved in pancreatic endocrinogenesis and in erythrocyte differentiation and that it outperforms competitor methods with respect to the functional coherence of the predicted driver TFs. SwitchTFI is available as a Python package at https://github.com/bionetslab/SwitchTFI.