Non-ignorable fuzziness in granular counts: the case of RNA-seq data
arXiv:2604.00763 · doi:10.1016/j.spl.2026.110808
Abstract
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.
10 pages, 1 figure, 0 tables. Note: The compressed source folder contains the Supplementary Materials