计数桥使能够建模和解卷积转录组数据
Count Bridges enable Modeling and Deconvolving Transcriptomic Data
AI总结：
Count Bridges通过随机桥过程建模整数数据并实现解卷积，适用于单细胞基因表达和空间转录组分析。
摘要：
许多现代生物检测，包括RNA测序，会产生整数值计数，这些计数反映了检测到的分子数量。这些测量通常并不在期望的分辨率上：虽然感兴趣的单位通常是单个细胞，但许多测量技术会产生在细胞集上的计数。尽管最近的生成框架，如扩散和流匹配，已被扩展到非欧几里得和离散设置，但仍然不清楚如何最好地建模整数值数据或如何系统地解卷积聚合观测。我们引入了Count Bridges，一种在整数上的随机桥过程，为计数数据提供了一个精确且可处理的扩散式模型的类比，具有闭合形式的条件，以实现高效的训练和采样。我们扩展了这一框架，以使能够通过期望最大化式方法直接从聚合测量中进行训练，将单位级计数视为潜在变量。我们展示了在整数分布匹配基准上的最先进性能，在各种指标上与流匹配和离散流匹配基线进行比较。然后，我们将Count Bridges应用于两个大规模的生物学问题：在核苷酸分辨率上建模单细胞基因表达数据，应用于解卷积批量RNA-seq，以及将多细胞空间转录组斑点解析为单细胞计数概况。我们的方法为跨尺度和模态的生物计数数据生成建模和解卷积提供了系统的基础。
英文摘要：
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 matching benchmarks, comparing against flow matching and discrete flow matching baselines across various metrics. We then apply Count Bridges to two large-scale problems in biology: modeling single-cell gene expression data at the nucleotide resolution, with applications to deconvolving bulk RNA-seq, and resolving multicellular spatial transcriptomic spots into single-cell count profiles. Our methods offer a principled foundation for generative modeling and deconvolution of biological count data across scales and modalities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
