Some statistical strategies for DAE-seq data analysis: : variable selection and modeling dependencies among observations

• Autores: Naim Rashid, Wei Sun, Joseph G. Ibrahim
• Localización: Journal of the American Statistical Association, ISSN 0162-1459, Vol. 109, Nº 505, 2014, págs. 78-94
• Idioma: inglés
• DOI: 10.1080/01621459.2013.869222
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• Resumen

  • 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. Supplementary materials for this article are available online.