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Detection of Copy Number Alterations Using Single Cell Sequencing
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A semiparametric Bayesian model for comparing DNA copy numbers.

Luis Nieto-Barajas1, Yuan Ji2, Veerabhadran Baladandayuthapani3

  • 1Department of Statistics, ITAM, Rio Hondo 1, Progreso Tizapan, 01080 Mexico, D.F. Mexico.

Brazilian Journal of Probability and Statistics
|October 6, 2023
PubMed
Summary

This study introduces a novel two-step Bayesian method for analyzing genomic copy number data. The approach identifies differential copy number regions across disease subtypes, improving cancer subtype analysis.

Keywords:
Bayesian nonparametricsDirichlet process mixture modelbivariate spike and slab priorcircular binary segmentationcomparative genomic hybridizationrandom effects

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Copy number alterations (CNAs) are crucial in cancer development.
  • Analyzing CNA data across diverse disease subtypes presents statistical challenges.
  • Existing methods may not fully capture subtype-specific and sample-specific variations.

Purpose of the Study:

  • To develop a robust statistical framework for analyzing copy number data in multiple disease subtypes.
  • To identify genomic regions with differential copy numbers across disease subtypes.
  • To account for both subtype-specific and sample-specific variations in copy number alterations.

Main Methods:

  • A two-step analytical approach.
  • Partitioning of genome aberrations.
  • A semiparametric Bayesian model incorporating random effects mixture models.
  • Dirichlet process priors for mixture components.

Main Results:

  • The proposed model effectively analyzes copy number data from multiple samples and disease subtypes.
  • It successfully identifies regions of differential copy numbers across disease subtypes.
  • The model accounts for inter-sample variability within the same disease subtype.

Conclusions:

  • The novel Bayesian method provides a powerful tool for understanding genomic alterations in complex diseases.
  • This approach enhances the identification of subtype-specific genomic signatures.
  • The method is validated on simulated data and a breast cancer dataset.