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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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A robust hidden semi-Markov model with application to aCGH data processing.

Jiarui Ding1, Sohrab Shah2

  • 1Department of Computer Science, University of British Columbia, Vancouver, Canada. jiaruid@cs.ubc.ca

International Journal of Data Mining and Bioinformatics
|January 10, 2014
PubMed
Summary
This summary is machine-generated.

We developed a robust hidden semi-Markov model using Student's t-mixture models to improve genomic data analysis. This approach enhances reliability by protecting against model errors and outliers in comparative genomic hybridization data.

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Area of Science:

  • Genomics
  • Statistical Modeling
  • Bioinformatics

Background:

  • Hidden semi-Markov models (HSMMs) effectively model sequential data with homogeneous zones using state duration distributions.
  • Standard HSMMs can be sensitive to model mis-specification and outliers, potentially affecting analysis reliability.

Purpose of the Study:

  • To develop a robust hidden semi-Markov model (RHSMM) that mitigates issues from model mis-specification and outliers.
  • To apply the RHSMM for modeling array-based comparative genomic hybridization (aCGH) data.

Main Methods:

  • The proposed robust HSMM incorporates Student's t-mixture models as emission distributions.
  • Student's t-distributions offer enhanced robustness against outliers compared to traditional Gaussian distributions.
  • The model was applied to analyze aCGH data, a technique used for detecting DNA copy number variations.

Main Results:

  • The RHSMM demonstrated reliable performance in modeling aCGH data.
  • Experiments on benchmark Coriell cell line data and glioblastoma multiforme data validated the technique's effectiveness.
  • The use of Student's t-mixture models provided protection against outliers, improving model stability.

Conclusions:

  • The robust hidden semi-Markov model with Student's t-mixture emissions is a reliable method for analyzing genomic data, particularly aCGH.
  • This approach offers improved performance over standard HSMMs by handling model mis-specification and outliers effectively.
  • The findings highlight the utility of robust statistical modeling in genomic sequence analysis.