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

Antonio Calcagnì; Arianna Consiglio; Przemyslaw Grzegorzewski; Corrado Mencar

doi:10.1016/j.spl.2026.110808

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arXiv · 2604.00763

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

Antonio Calcagnì·Arianna Consiglio·Przemyslaw Grzegorzewski·Corrado Mencar

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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.

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Antonio Calcagnì, Arianna Consiglio, Przemyslaw Grzegorzewski, Corrado Mencar. 2026-04-01. Non-ignorable fuzziness in granular counts: the case of RNA-seq data. https://doi.org/10.1016/j.spl.2026.110808

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