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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Fundamental limits on dynamic inference from single-cell snapshots.

Caleb Weinreb1, Samuel Wolock1, Betsabeh K Tusi2

  • 1Department of Systems Biology, Harvard Medical School, Boston, MA 02115.

Proceedings of the National Academy of Sciences of the United States of America
|February 22, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces Population Balance Analysis (PBA) to infer cell gene expression dynamics from static single-cell data. PBA overcomes limitations in reconstructing cell trajectories from population snapshots.

Keywords:
dynamic inferencehematopoiesispseudotimesingle cellspectral graph theory

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Genomics

Background:

  • Single-cell expression profiling offers detailed molecular state insights but destroys cells, precluding direct temporal measurements.
  • Inferring cellular dynamics from static single-cell data is challenging due to inherent ambiguities in interpreting population-level state distributions.
  • Existing methods for single-cell dynamics inference face limitations, often yielding multiple possible explanations for observed gene expression patterns.

Purpose of the Study:

  • To identify fundamental limits in inferring gene expression dynamics from static single-cell snapshots.
  • To develop a computational framework, Population Balance Analysis (PBA), for robust cell dynamics reconstruction.
  • To establish necessary assumptions for uniquely determining cell dynamics from static measurements.

Main Methods:

  • Developed Population Balance Analysis (PBA), a novel algorithmic approach.
  • Utilized spectral graph theory to solve high-dimensional differential equations governing gene expression dynamics.
  • Validated PBA through simulations and application to hematopoietic progenitor cell (HPC) single-cell expression data.

Main Results:

  • PBA effectively reconstructs cell state trajectories from static single-cell expression data.
  • Simulations demonstrated the strengths and limitations of the PBA framework.
  • PBA predictions for HPCs align with established experimental fate assay results.

Conclusions:

  • PBA provides a rigorous method for interpreting gene expression continua and inferring cellular dynamics from static snapshots.
  • The study clarifies fundamental limitations in dynamic inference from static data.
  • PBA offers guidance for optimizing experimental designs for trajectory reconstruction.