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TRACING CO-REGULATORY NETWORK DYNAMICS IN NOISY, SINGLE-CELL TRANSCRIPTOME TRAJECTORIES.

Pablo Cordero1, Joshua M Stuart

  • 1UC Santa Cruz Genomics Institute, University of California, Santa Cruz, California, USA.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|November 30, 2016
PubMed
Summary

SCIMITAR infers cell differentiation trajectories from single-cell gene expression data. This method models data noise to reveal gene regulatory network changes and identify key genes and transcription factors driving cellular progression.

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Area of Science:

  • Computational Biology
  • Genomics
  • Systems Biology

Background:

  • Single-cell gene expression data enables studying complex biological processes like differentiation and oncogenesis.
  • Reconstructing developmental trajectories from static single-cell transcriptomes is challenging due to data noise and the need to understand gene regulatory network rewiring.

Purpose of the Study:

  • To present a novel computational framework, SCIMITAR (Single Cell Inference of MorphIng Trajectories and their Associated Regulation), for inferring cell progression trajectories and associated gene regulatory networks from single-cell transcriptomic data.
  • To accurately model noise in single-cell data for robust inference of gene regulatory network rewiring during cellular processes.

Main Methods:

  • SCIMITAR employs a continuous parametrization of Gaussian mixtures in high-dimensional curves to infer progressions from static single-cell transcriptomes.
  • The framework generates rich models highlighting genes with expression and co-expression patterns associated with inferred trajectories.
  • It extracts regulatory states from trajectory-evolving co-expression networks and is benchmarked on simulated data for accuracy in cell ordering and gene network inference.

Main Results:

  • SCIMITAR accurately infers cell ordering and gene networks from simulated data.
  • When applied to human fetal neuron data, SCIMITAR identified progression-associated genes in key neural differentiation pathways missed by standard differential expression tests.
  • The method revealed dynamic changes in co-regulatory states and gene modules across the inferred trajectory, implicating new transcription factors in neural differentiation.

Conclusions:

  • SCIMITAR provides a robust framework for inferring cellular trajectories and gene regulatory dynamics from single-cell transcriptomic data.
  • The method enhances the understanding of molecular mechanisms underlying differentiation and oncogenesis by accurately modeling data noise and identifying key regulatory elements.
  • SCIMITAR's application to neural differentiation highlights its potential for discovering novel regulatory factors in complex biological processes.