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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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SCENIC: single-cell regulatory network inference and clustering.

Sara Aibar1,2, Carmen Bravo González-Blas1,2, Thomas Moerman3,4

  • 1VIB Center for Brain & Disease Research, Laboratory of Computational Biology, Leuven, Belgium.

Nature Methods
|October 10, 2017
PubMed
Summary

We developed SCENIC, a new computational tool for analyzing single-cell RNA sequencing data. This method reconstructs gene regulatory networks and identifies cell states, offering insights into cellular heterogeneity.

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Area of Science:

  • Computational Biology
  • Genomics
  • Single-cell Analysis

Background:

  • Understanding cellular heterogeneity is crucial in biological research.
  • Gene regulatory networks (GRNs) govern cellular functions.
  • Single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptomic data.

Purpose of the Study:

  • To present SCENIC, a novel computational method.
  • To enable simultaneous GRN reconstruction and cell-state identification from scRNA-seq data.
  • To leverage cis-regulatory analysis for biological insights.

Main Methods:

  • SCENIC (Single-Cell rEgulatory Network Inference and Clustering) computational pipeline.
  • Analysis of compendium single-cell RNA-seq datasets from tumors and brain.
  • Application of cis-regulatory analysis.

Main Results:

  • SCENIC successfully reconstructs gene regulatory networks.
  • SCENIC accurately identifies distinct cell states.
  • Demonstrated utility in tumor and brain single-cell datasets.
  • Cis-regulatory analysis guides transcription factor and cell-state identification.

Conclusions:

  • SCENIC offers a powerful approach for analyzing single-cell transcriptomic data.
  • The method provides critical biological insights into cellular heterogeneity.
  • SCENIC facilitates the identification of key transcription factors driving cell states.