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cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data.

Carmen Bravo González-Blas1,2, Liesbeth Minnoye1,2, Dafni Papasokrati1,2

  • 1VIB Center for Brain & Disease Research, Leuven, Belgium.

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Summary

We introduce cisTopic, a new computational framework for analyzing single-cell epigenomics data. This method effectively identifies cell types, regulatory elements, and transcription factors from sparse datasets.

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Area of Science:

  • Genomics
  • Computational Biology
  • Epigenetics

Background:

  • Single-cell epigenomics data, such as single-cell ATAC-seq, is sparse and complex.
  • Understanding cell states and regulatory elements is crucial for deciphering biological processes.
  • Existing methods may struggle with the inherent sparsity and heterogeneity of this data.

Purpose of the Study:

  • To present cisTopic, a probabilistic framework for analyzing single-cell epigenomics data.
  • To simultaneously discover coaccessible enhancers and stable cell states.
  • To provide insights into regulatory heterogeneity in cell populations.

Main Methods:

  • cisTopic utilizes topic modeling on sparse single-cell epigenomics data.
  • The framework was applied to datasets including differentiating hematopoietic cells, brain tissue, and transcription factor perturbations.
  • Probabilistic modeling enables robust identification of biological features.

Main Results:

  • cisTopic successfully identified cell types, enhancers, and relevant transcription factors.
  • The method demonstrated robustness in analyzing diverse single-cell ATAC-seq datasets.
  • Coaccessible enhancers and stable cell states were simultaneously discovered.

Conclusions:

  • Topic modeling is a powerful approach for single-cell epigenomics.
  • cisTopic offers a robust framework for dissecting regulatory heterogeneity.
  • The tool aids in understanding cell differentiation and regulatory mechanisms.