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Atlas of Computational Cell Reprogramming

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C1 · Network informed intervention prioritization D

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

Level-1 scRNA driver discovery: given a count matrix, baseline TF→target GRN, and progenitor/offspring labels, SwitchTFI fits MAGIC-imputed depth-1 regression stumps, prunes edges with Westfall-Young FWER control, and ranks TFs by PageRank or weighted outdegree on the transition GRN. Validated on murine pancreatic endocrinogenesis, erythroid differentiation, and Th1 exhaustion; no head-to-head benchmark against other cell-reprogramming methods (paper compares trajectory-driver tools).

Why this class

Level 1 because the method scores candidate interventions through static regulatory-network influence relative to a target GRN, without computing the post-intervention state P^S,u\hat P_{S,u}. Representation family is regulatory-network influence. Cited in §3.2 of the review. Editorial rationale pending review by the maintainer.

Classification

Class
C1 · Network informed intervention prioritization
Representation
Regulatory-network influence
Modalities
D
Intervention
Transcription factors
Framework
Static network analysis

Software

Reproducibility
4/4
FAIR4RS
4/5

Last audited 2026-06-01

Citation

Martini P et al. (2025). SwitchTFI: identifying transcription factors driving cell differentiation., Genome biology.

DOI: 10.1186/s13059-025-03876-0

PMID: 41331466

BibTeX
@article{switchtfi2025,
  title  = {SwitchTFI: identifying transcription factors driving cell differentiation.},
  author = {Martini P et al.},
  year   = {2025},
  journal = {Genome biology},
  pmid = {41331466},
  doi  = {10.1186/s13059-025-03876-0}
}

Validation datasets

  • murine pancreatic endocrinogenesis (Bastidas-Ponce) other
  • mouse erythrocyte differentiation (Paul et al.) other
  • murine T helper exhaustion (LCMV) other
  • MEF to iEP scalability (Morris reprogramming) other

Datasets used to validate the method's predictions in the original paper. Solid borders are linked to the source archive; dashed borders denote identifiers without a canonical URL on record.