Evidence map›Paper›PMID 41331466›Full record

ArticleGenome biology2025

SwitchTFI: identifying transcription factors driving cell differentiation.

Paul Martini, Anne Hartebrodt, Gustavo P de Almeida, Carl-Philipp Hackstein, Dietmar Zehn, David B Blumenthal

Abstract read
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In one paragraph

Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Paul MartiniBiomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nürnberger Str. 74, Erlangen, 91052, Germany. paul.martini@fau.de.
Anne HartebrodtBiomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nürnberger Str. 74, Erlangen, 91052, Germany.
Gustavo P de AlmeidaDivision of Animal Physiology and Immunology, TUM School of Life Sciences and Center for Infection Prevention (ZIP), Technical University of Munich, Liesel-Beckmann-Str. 1, Freising, 85354, Germany.
Carl-Philipp HacksteinInstitute for Molecular Immunology, TUM School of Life Sciences and Center for Infection Prevention (ZIP), Technical University of Munich, Liesel-Beckmann-Str. 1, Freising, 85354, Germany.
Dietmar ZehnDivision of Animal Physiology and Immunology, TUM School of Life Sciences and Center for Infection Prevention (ZIP), Technical University of Munich, Liesel-Beckmann-Str. 1, Freising, 85354, Germany.
David B BlumenthalBiomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nürnberger Str. 74, Erlangen, 91052, Germany. david.b.blumenthal@fau.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Cell DifferentiationSoftwareTranscription FactorsAlgorithmsAnimalsGene Regulatory NetworksHumansSingle-Cell AnalysisSingle-Cell Gene Expression AnalysisT-Cell ExhaustionTranscription FactorsCell differentiationGene regulatory networksSingle-cell gene expression dataTranscription factors

Identifiers

PMID41331466
PMCPMC12673684

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.