SwitchTFI: identifying transcription factors driving cell differentiation

Martini P, Hartebrodt A, de Almeida GP, Hackstein CP, Zehn D, Blumenthal DB (2025)

Publication Type: Journal article

Publication year: 2025

Journal

Genome Biology

Book Volume: 26

Article Number: 410

Journal Issue: 1

DOI: 10.1186/s13059-025-03876-0

Abstract

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.

Authors with CRIS profile

Paul Martini Professur für Biomedical Network Science Anne Hartebrodt Professur für Biomedical Network Science David Blumenthal Professur für Biomedical Network Science

Involved external institutions

Technische Universität München (TUM)

Germany (DE)

How to cite

APA:

Martini, P., Hartebrodt, A., de Almeida, G.P., Hackstein, C.P., Zehn, D., & Blumenthal, D.B. (2025). SwitchTFI: identifying transcription factors driving cell differentiation. Genome Biology, 26(1). https://doi.org/10.1186/s13059-025-03876-0

MLA:

Martini, Paul, et al. "SwitchTFI: identifying transcription factors driving cell differentiation." Genome Biology 26.1 (2025).

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