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Tree inference for single-cell data.

Katharina Jahn1,2, Jack Kuipers1,2, Niko Beerenwinkel3,4

  • 1Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.

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|May 7, 2016
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Summary
This summary is machine-generated.

SCITE is a new algorithm that reconstructs tumor evolutionary history from single-cell mutation data. It accurately identifies tumor evolution and estimates sequencing errors, improving cancer therapy development.

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Area of Science:

  • Computational biology
  • Genomics
  • Cancer research

Background:

  • Tumor mutational heterogeneity is crucial for developing effective cancer therapies.
  • Accurate reconstruction of tumor evolutionary history is needed.

Purpose of the Study:

  • To present SCITE, a novel stochastic search algorithm for identifying tumor evolutionary history.
  • To enable analysis of noisy, incomplete single-cell mutation profiles.

Main Methods:

  • Developed a flexible Markov chain Monte Carlo (MCMC) sampling scheme.
  • Implemented computation of maximum-likelihood mutation history.
  • Enabled sampling from posterior probability distributions and estimation of sequencing error rates.

Main Results:

  • SCITE demonstrates scalability with current single-cell sequencing data.
  • Achieved improved reconstruction accuracy compared to existing methods.
  • Validated on real cancer data and simulation studies.

Conclusions:

  • SCITE provides an accurate and scalable approach for reconstructing tumor evolution.
  • The algorithm aids in understanding mutational heterogeneity for cancer therapy development.