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spliceJAC: transition genes and state-specific gene regulation from single-cell transcriptome data.

Federico Bocci1,2, Peijie Zhou1, Qing Nie1,2,3

  • 1Department of Mathematics, University of California, Irvine, CA, USA.

Molecular Systems Biology
|November 2, 2022
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Summary

spliceJAC quantifies mRNA splicing from single-cell RNA sequencing (scRNA-seq) data to reveal gene interactions and predict key driver genes for cell state transitions.

Keywords:
attractor linear stabilitycell state transitiongene regulatory networkmRNA splicingsingle-cell RNA sequencing

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Area of Science:

  • Computational Biology
  • Molecular Biology
  • Genomics

Background:

  • Single-cell RNA sequencing (scRNA-seq) offers insights into cell heterogeneity and dynamics.
  • Understanding cell state transitions and gene interactions is crucial for biological discovery.
  • Existing methods often lack theory-driven, bottom-up approaches for analyzing cell state differences.

Purpose of the Study:

  • To introduce spliceJAC, a novel method for quantifying multivariate mRNA splicing from scRNA-seq data.
  • To enable the construction of cell state-specific gene regulatory networks.
  • To predict driver genes critical for cell state transitions.

Main Methods:

  • spliceJAC utilizes unspliced and spliced mRNA count matrices from scRNA-seq.
  • It constructs cell state-specific gene-gene regulatory interactions.
  • Stability analysis is applied to identify putative driver genes.

Main Results:

  • spliceJAC successfully predicted genes with specific signaling roles in different cell states.
  • The method recovered known differentially expressed genes in pancreas endothelium development and EMT.
  • New transition genes, both exclusive and shared, were identified for cell state changes.

Conclusions:

  • spliceJAC provides a powerful tool for extracting dynamical information from scRNA-seq data.
  • The method enhances understanding of gene interactions and cell state transitions.
  • spliceJAC aids in identifying key regulatory genes driving cellular differentiation and disease processes.