title: Some Statistical Strategies for DAE-seq Data Analysis: Variable Selection and Modeling Dependencies among Observations.
abstract: In DAE (DNA After Enrichment)-seq experiments, genomic regions related with certain biological processes are enriched/isolated by an assay and are then sequenced on a high-throughput sequencing platform to determine their genomic positions. Statistical analysis of DAE-seq data aims to detect genomic regions with significant aggregations of isolated DNA fragments ("enriched regions") versus all the other regions ("background"). However, many confounding factors may influence DAE-seq signals. In addition, the signals in adjacent genomic regions may exhibit strong correlations, which invalidate the independence assumption employed by many existing methods. To mitigate these issues, we develop a novel Autoregressive Hidden Markov Model (AR-HMM) to account for covariates effects and violations of the independence assumption. We demonstrate that our AR-HMM leads to improved performance in identifying enriched regions in both simulated and real datasets, especially in those in epigenetic datasets with broader regions of DAE-seq signal enrichment. We also introduce a variable selection procedure in the context of the HMM/AR-HMM where the observations are not independent and the mean value of each state-specific emission distribution is modeled by some covariates. We study the theoretical properties of this variable selection procedure and demonstrate its efficacy in simulated and real DAE-seq data. In summary, we develop several practical approaches for DAE-seq data analysis that are also applicable to more general problems in statistics.
authors.affiliation: Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, U.S.A.
authors.forename: Naim U
authors.initials: NU
authors.lastname: Rashid
authors.affiliation: Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, U.S.A.
authors.forename: Wei
authors.initials: W
authors.lastname: Sun
authors.affiliation: Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, U.S.A.
authors.forename: Joseph G
authors.initials: JG
authors.lastname: Ibrahim
keywords: Autoregressive modeling; Hidden Markov Model; High-throughput Sequencing; Mixture Regression; Variable Selection