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Atlas of Computational Cell Reprogramming

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L0 · Target-feature discovery T

Okawa & Del Sol (synergistic TF core)

Okawa S, Del Sol A

2019 · Nucleic acids research

Advances in single-cell RNA-sequencing techniques reveal the existence of distinct cell subpopulations.

Abstract

From the original paper, Nucleic acids research · PubMed

Advances in single-cell RNA-sequencing techniques reveal the existence of distinct cell subpopulations. Identification of transcription factors (TFs) that define the identity of these subpopulations poses a challenge. Here, we postulate that identity depends on background subpopulations, and is determined by a synergistic core combination of TFs mainly uniquely expressed in each subpopulation, but also TFs more broadly expressed across background subpopulations. Building on this view, we develop a new computational method for determining such synergistic identity cores of subpopulations within a given cell population. Our method utilizes an information-theoretic measure for quantifying transcriptional synergy, and implements a novel algorithm for searching for optimal synergistic cores. It requires only single-cell RNA-seq data as input, and does not rely on any prior knowledge of candidate genes or gene regulatory networks. Hence, it can be directly applied to any cellular systems, including those containing novel subpopulations. The method is capable of recapitulating known experimentally validated identity TFs in eight published single-cell RNA-seq datasets. Furthermore, some of these identity TFs are known to trigger cell conversions between subpopulations. Thus, this methodology can help design strategies for cell conversion within a cell population, guiding experimentalists in the field of stem cell research and regenerative medicine.

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 Okawa & Del Sol (synergistic TF core) relates to the cross-modality inverse-design framework of the review.

Why this level

Level 0 because the method scores features against a target/background contrast without modelling an intervention uu — the output is a ranked list of candidate identity determinants, not an intervention whose effect has been computed. Representation family is signature / state-matching. Cited in §3.1 of the review. Editorial rationale pending review by the maintainer.

Classification

Level
L0
Representation
Signature / state-matching
Modalities
T
Intervention
Transcription factors
Framework
Information-theoretic

Software

Reproducibility
3 of 4
FAIR4RS
0 of 5

Last audited 2026-05-24

Citation

Okawa S et al. (2019). A general computational approach to predicting synergistic transcriptional cores that determine cell subpopulation identities., Nucleic acids research.

DOI: 10.1093/nar/gkz147

PMID: 30820550

BibTeX
@article{okawa-delsol2019,
  title  = {A general computational approach to predicting synergistic transcriptional cores that determine cell subpopulation identities.},
  author = {Okawa S et al.},
  year   = {2019},
  journal = {Nucleic acids research},
  pmid = {30820550},
  doi  = {10.1093/nar/gkz147}
}