[2603.04730] Count Bridges enable Modeling and Deconvolving Transcriptomic Data

Computer Science > Machine Learning arXiv:2603.04730 (cs) [Submitted on 5 Mar 2026] Title:Count Bridges enable Modeling and Deconvolving Transcriptomic Data Authors:Nic Fishman, Gokul Gowri, Tanush Kumar, Jiaqi Lu, Valentin de Bortoli, Jonathan S. Gootenberg, Omar Abudayyeh View a PDF of the paper titled Count Bridges enable Modeling and Deconvolving Transcriptomic Data, by Nic Fishman and 6 other authors View PDF HTML (experimental) Abstract:Many modern biological assays, including RNA sequencing, yield integer-valued counts that reflect the number of molecules detected. These measurements are often not at the desired resolution: while the unit of interest is typically a single cell, many measurement technologies produce counts aggregated over sets of cells. Although recent generative frameworks such as diffusion and flow matching have been extended to non-Euclidean and discrete settings, it remains unclear how best to model integer-valued data or how to systematically deconvolve aggregated observations. We introduce Count Bridges, a stochastic bridge process on the integers that provides an exact, tractable analogue of diffusion-style models for count data, with closed-form conditionals for efficient training and sampling. We extend this framework to enable direct training from aggregated measurements via an Expectation-Maximization-style approach that treats unit-level counts as latent variables. We demonstrate state-of-the-art performance on integer distribution matchi...