ChIP-Seq数据空间相关聚类的多重分配混合模型
Mixture model with multiple allocations for clustering spatially correlated observations in the analysis of ChIP-Seq data
摘要：
基于模型的聚类是一种广泛用于将单元集合分组为互斥组的技术。然而，在某些情况下，一项观察原则上可能属于一个以上的组群。例如，在下一代测序（NGS）实验的背景下，数据中观察到的信号可能是由两个（或更多个）不同的生物过程共同作用产生的，一个基因可能参与其中的两个（或全部）。我们提出了一种新的方法来聚类NGS离散数据，来自ChIP-Seq实验，具有混合模型，允许每个单元潜在地属于一个以上的组：这些多个分配集群可以通过一个函数灵活地定义，该函数结合了原始组的特征，而无需引入新的参数。这一提法自然会产生一个“零通货膨胀组”，其中可以分配接近零的值，作为对这类数据中大量零的校正。我们考虑到观测之间的空间依赖性，这是通过一个潜在的条件自回归过程，可以反映不同的依赖模式。我们在模拟环境中评估我们的模型的性能，然后将其应用于ChIP-seq真实数据。
英文摘要：
Model-based clustering is a technique widely used to group a collection of units into mutually exclusive groups. There are, however, situations in which an observation could in principle belong to more than one cluster. In the context of Next-Generation Sequencing (NGS) experiments, for example, the signal observed in the data might be produced by two (or more) different biological processes operating together and a gene could participate in both (or all) of them. We propose a novel approach to cluster NGS discrete data, coming from a ChIP-Seq experiment, with a mixture model, allowing each unit to belong potentially to more than one group: these multiple allocation clusters can be flexibly defined via a function combining the features of the original groups without introducing new parameters. The formulation naturally gives rise to a `zero-inflation group' in which values close to zero can be allocated, acting as a correction for the abundance of zeros that manifest in this type of data. We take into account the spatial dependency between observations, which is described through a latent Conditional Auto-Regressive process that can reflect different dependency patterns. We assess the performance of our model within a simulation environment and then we apply it to ChIP-seq real data.
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。
