RNA-seq count data are often affected by read-to-gene alignment ambiguity, especially in high-dimensional transcriptomics. This type of ambiguity can be conveniently expressed through granular counts, namely fuzzy-valued observations of latent discrete quantities. We study a class of fuzzy-reporting mechanisms and show that, when reporting exploits graded membership, ignorability fails generically, leading to a coarsening-not-at-random structure. A hierarchical model is then introduced as a tractable instance of this construction and illustrated using RNA-seq data.

翻译：RNA-seq计数数据常受到读段-基因比对模糊性的影响，尤其在高度维度的转录组学中尤为突出。这种模糊性可通过颗粒计数（即潜在离散量的模糊值观测）得以便捷表达。我们研究了一类模糊报告机制，并表明当报告利用梯度隶属度时，可忽略性通常失效，从而产生一种非随机粗化结构。随后，我们引入了一个层次模型作为该构造的可处理实例，并利用RNA-seq数据进行了说明。