SwitchTFI
Martini P, Hartebrodt A, de Almeida GP, Hackstein CP, Zehn D, Blumenthal DB
2025 · Genome biology
Many methods exist that infer cell differentiation trajectories from single-cell RNA sequencing data, but only few determine which mechanisms drive the inferred differentiation dynamics.
Abstract
From the original paper, Genome biology · PubMed
Many methods exist that infer cell differentiation trajectories from single-cell RNA sequencing data, but only few determine which mechanisms drive the inferred differentiation dynamics. To close this gap, we developed the algorithm and Python package SwitchTFI. Utilizing regression stump learning, permutation-based family-wise error rate control, and node centrality measures, SwitchTFI identifies differentiation-driving gene regulatory networks and the key transcription factors involved in them. Comprehensive tests on pancreatic endocrinogenesis, erythrocyte differentiation, and T cell exhaustion datasets show that SwitchTFI can rediscover known differentiation factors, that it can discover novel biologically plausible hypotheses, and that it compares favorably to competitor methods. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-025-03876-0.
Summary
Editorial summary pending review by the maintainer. The paper's own abstract appears above; the Atlas summary in the maintainer's voice will explain how SwitchTFI relates to the cross-modality inverse-design framework of the review.
Why this level
Level 1 because the method scores candidate interventions through static regulatory-network influence relative to a target GRN, without computing the post-intervention state . Representation family is regulatory-network influence. Cited in §3.2 of the review. Editorial rationale pending review by the maintainer.