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Detection of Copy Number Alterations Using Single Cell Sequencing
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Post-selection inference for changepoint detection algorithms with application to copy number variation data.

Sangwon Hyun1, Kevin Z Lin2, Max G'Sell3

  • 1Department of Data Sciences and Operations, University of Southern California, Los Angeles, California, USA.

Biometrics
|January 12, 2021
PubMed
Summary
This summary is machine-generated.

This study enhances changepoint detection for copy number variation analysis by integrating post-selection inference. New methods improve uncertainty quantification and hypothesis testing power for genomic data.

Keywords:
changepoint detectioncomparative genomic hybridization analysiscopy number variationhypothesis testspost-selection inferencesegmentation algorithms

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Area of Science:

  • Genomics
  • Statistical Methods
  • Bioinformatics

Background:

  • Changepoint detection is crucial for analyzing genomic copy number variations.
  • Existing methods lack robust quantification of uncertainty in detected changepoints.
  • Post-selection inference offers a framework but often yields low-powered tests.

Purpose of the Study:

  • To tailor post-selection inference methods for changepoint detection in copy number variation data.
  • To address limitations in power and practical usability of current post-selection inference techniques.
  • To improve the quantification of uncertainty in changepoint detection.

Main Methods:

  • Applied post-selection inference, specifically auxiliary randomization, to changepoint detection.
  • Investigated common algorithms: binary segmentation (wild, circular) and fused lasso.
  • Utilized Markov chain Monte Carlo (MCMC) algorithms, including importance sampling and hit-and-run sampling.

Main Results:

  • Developed tailored post-selection inference methods that significantly improve statistical power.
  • Demonstrated enhanced practical usability for changepoint detection.
  • Provided validated recommendations for improving methodology.

Conclusions:

  • The tailored post-selection inference framework effectively enhances changepoint detection in genomic data.
  • The improved methods offer more reliable uncertainty quantification for copy number variations.
  • This work advances the analysis of genomic data by providing more powerful and usable statistical tools.