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Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
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Some Statistical Strategies for DAE-seq Data Analysis: Variable Selection and Modeling Dependencies among

Naim U Rashid1, Wei Sun1, Joseph G Ibrahim1

  • 1Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, U.S.A.

Journal of the American Statistical Association
|March 29, 2014
PubMed
Summary
This summary is machine-generated.

We developed a novel Autoregressive Hidden Markov Model (AR-HMM) to improve the analysis of DNA After Enrichment sequencing (DAE-seq) data. This method accurately identifies enriched genomic regions by accounting for confounding factors and signal correlations.

Keywords:
Autoregressive modelingHidden Markov ModelHigh-throughput SequencingMixture RegressionVariable Selection

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • DNA After Enrichment sequencing (DAE-seq) isolates genomic regions for high-throughput sequencing.
  • Statistical analysis of DAE-seq data aims to detect "enriched regions" against a background.
  • Existing methods often fail due to confounding factors and correlated signals in adjacent regions.

Purpose of the Study:

  • To develop a novel statistical model for DAE-seq data analysis.
  • To address confounding factors and the violation of independence assumptions in DAE-seq signals.
  • To improve the detection of enriched genomic regions, particularly in epigenetic studies.

Main Methods:

  • Development of a novel Autoregressive Hidden Markov Model (AR-HMM).
  • Incorporation of covariate effects and accounting for dependencies between genomic regions.
  • Introduction of a variable selection procedure within the HMM/AR-HMM framework.

Main Results:

  • The AR-HMM demonstrated improved performance in identifying enriched regions in both simulated and real DAE-seq datasets.
  • The model showed particular efficacy in epigenetic datasets with broader DAE-seq signal enrichment.
  • The variable selection procedure proved effective in simulated and real DAE-seq data.

Conclusions:

  • The developed AR-HMM offers a robust approach for DAE-seq data analysis.
  • The methodology enhances the identification of biologically significant genomic regions.
  • The statistical approaches are applicable to broader problems in statistical analysis beyond DAE-seq.