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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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During embryogenesis, cells become progressively committed to different fates through a two-step process: specification followed by determination. Specification is demonstrated by removing a segment of an early embryo, “neutrally” culturing the tissue in vitro—for example, in a petri dish with simple medium—and then observing the derivatives. If the cultured region gives rise to cell types that it would normally generate in the embryo, this means that it is specified. In...
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Related Experiment Video

Updated: Jun 4, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
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High order expression dependencies finely resolve cryptic states and subtypes in single cell data.

Abel Jansma1,2, Yuelin Yao1,3, Jareth Wolfe1

  • 1MRC Human Genetics Unit, Institute of Genetics & Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK.

Molecular Systems Biology
|January 3, 2025
PubMed
Summary
This summary is machine-generated.

Stator is a new method for single-cell analysis that identifies cell types and states by analyzing gene expression dependencies. This approach provides a finer resolution than traditional clustering, revealing complex cell states and predicting cell fates and patient survival.

Keywords:
Cell Cycle PhasesCell StateHigher-order Gene Expression DependenciesSingle-cell TranscriptomicsStructure Learning

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Area of Science:

  • Genomics
  • Computational Biology
  • Single-cell Analysis

Background:

  • Traditional single-cell typing relies on clustering in reduced dimensional space.
  • This approach often limits the resolution of cell states and subtypes.

Purpose of the Study:

  • Introduce Stator, a novel data-driven method for cell (sub)type and state identification.
  • To achieve finer resolution of cell populations beyond local proximity in transcriptome space.

Main Methods:

  • Stator derives higher-order gene expression dependencies from sparse gene-by-cell matrices.
  • It labels single cells multiply by type, subtype, and various states (e.g., activation, maturity, cell cycle).
  • The method is implemented as a Nextflow pipeline and Shiny App.

Main Results:

  • Stator reveals finer cell resolutions in mouse embryonic brain and human liver datasets.
  • Identified 34 distinct radial glia states in embryonic cells, predicting neuronal fate.
  • Discovered liver cancer states with expression programs predicting patient survival.

Conclusions:

  • Stator offers a more granular understanding of cell populations.
  • The method enhances the ability to predict cell fate and clinical outcomes.
  • Stator provides a powerful tool for complex single-cell data analysis.