Non-ignorable fuzziness in granular counts: the case of RNA-seq data

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.

Source: arXiv:2604.00763v1 - http://arxiv.org/abs/2604.00763v1 PDF: https://arxiv.org/pdf/2604.00763v1 Original Link: http://arxiv.org/abs/2604.00763v1