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In this article, we propose a new statistical method—MutRSeq—for detecting differentially expressed single nucleotide variants (SNVs) based on RNA‐seq data. Specifically, we focus on nonsynonymous mutations and employ a hierarchical likelihood approach to jointly model observed mutation events as well as read count measurements from RNA‐seq experiments. We then introduce a likelihood ratio‐based...
The next‐generation sequencing data, called high‐throughput sequencing data, are recorded as count data, which are generally far from normal distribution. Under the assumption that the count data follow the Poisson ‐normal distribution, this article provides an ‐penalized likelihood framework and an efficient search algorithm to estimate the structure of sparse directed acyclic graphs (DAGs)...
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