Scalable Bayesian Network analysis of multimodal FACS and SUMOylation data, with generalization to other big mixed biological datasets

可扩展的贝叶斯网络分析多模式FACS和SUMOACTION数据，具有 对其他大型混合生物数据集的推广 摘要 贝叶斯或信念网络(BN)建模是目前正在兴起的一种强大的工具，它是 多式联运的主要数据分析、探索和可视化方法 异质)“大”生物数据。我们之前已经开发了全面的BN算法 以及针对异质生物大数据分析的软件包。近几年来，我们 已经将其应用于不同的生物研究领域/数据集(包括染色质相互作用， TRNA进化、遗传流行病学和代谢组学、癌症流行病学和单细胞 胸腺生成数据)；另外三个项目(使用FACS推断免疫信号网络 数据、全基因组SUMO化、阿尔茨海默氏症基因组分析)目前正在进行中。在…的过程中 这项工作我们已经确定了需要解决的关键的“瓶颈”，在方法论上 级别，以使BN分析在我们的一般上下文(即，大生物数据)中普遍可用 包含大量不同类型的变量)。这些问题(BN的可扩展性 重建过程，处理混合数据类型，以及对 所产生的网络模型)尚未在现场得到充分解决，因此限制了 在其他方面非常强大和优雅的BN方法的可用性。 因此，该项目的主要目标是开发新的BN分析算法 强调(A)可伸缩性，(B)处理混合数据类型，以及(C)结果网络的解释 和评估。我们对定量流式细胞术中的BN分析特别感兴趣 (FACS)数据是正在进行的希望之城癌症免疫遗传学研究项目的一部分， 因为这种类型的数据体现了BN建模的挑战，以及算法和软件方面的任何进步 开发将适用于大生物数据的大多数实例。我们随后将 将BN分析应用于SUMO化和染色质相互作用基因组数据(也生成为 正在进行的合作城市希望研究项目的一部分)，以进一步测试普适性，并 产生更多的生物学结果。

Scalable Bayesian Network analysis of multimodal FACS and SUMOylation data, with generalization to other big mixed biological datasets Abstract The Bayesian, or Belief, Network (BN) modeling is a powerful tool that is currently emerging as one of the principal data analysis, exploration and visualization methods for multimodal (aka mixed, or heterogeneous) “big” biological data. We have previously developed comprehensive BN algorithms and software package aimed at heterogeneous big biological data analysis. Over the recent years we have applied it to the different biological research domains / datasets (including chromatin interaction, tRNA evolution, genetic epidemiology and metabolomics, cancer epidemiology and single cell thymopoiesis data); work on three more projects (inferring immune signaling networks using FACS data, genome-wide SUMOylation, Alzheimer's genomic analysis) is currently in progress. In course of this work we have identified crucial “bottlenecks” that need to be addressed, on the methodological level, to make the BN analysis universally usable in our general context (that is, big biological data containing large numbers of variables of different types). These issues (scalability of the BN reconstruction process, handling mixed data types, and interpretation, evaluation & comparison of the resulting network models) have not been adequately addressed in the field yet, thus limiting the usability of the otherwise very powerful and elegant BN approach. Consequently, the primary goal of this project is to develop novel BN analysis algorithms with emphasis on (a) scalability, (b) handling mixed data types, and (c) resulting networks' interpretation and evaluation. We are particularly interested in the BN analysis of the quantitative flow cytometry (FACS) data generated as part of the ongoing City of Hope cancer immunogenetics research projects, as this type of data exemplifies BN modeling challenges, and any advances in algorithm and software development would be generalizable to most instances of big biological data. We will subsequently apply the BN analysis to the SUMOylation and chromatin interaction genomic data (also generated as part of the ongoing collaborative City of Hope research projects), to further test generalizability, and to produce additional biological results.