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

多模式 FACS 和 SUMOylation 数据的可扩展贝叶斯网络分析，并推广到其他大型混合生物数据集

• 批准号: 10205173
• 负责人: Andrei Rodin
• 金额: $ 26.18万
• 依托单位: BECKMAN RESEARCH INSTITUTE/CITY OF HOPE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10205173
• 关键词: Address; Algorithmic Analysis; Algorithmic Software; Algorithms; Alzheimer' s Disease; Bayesian Analysis; Bayesian Network; Belief; Biological; Cells; Chromatin; Cities; Clinical; Clinical Trials; Computer software; Data; Data Analyses; Data Set; Data Storage and Retrieval; Development; Engineering; Epidemiology; Epigenetic Process; Evaluation; Evolution; Flow Cytometry; Genetic; Genomics; Goals; Hybrids; Immune signaling; Immunogenetics; Immunological Models; Letters; Literature; Malignant Neoplasms; Methodology; Methods; Modeling; Modernization; Outcome; Output; Pathway Analysis; Pathway interactions; Phenotype; Process; Proteomics; Public Health; Publications; Research; Research Personnel; Research Project Grants; Series; Sumoylation Pathway; Systems Biology; Testing; Transfer RNA; Visualization; Work; algorithm development; base; biological research; cancer epidemiology; data handling; experimental study; genetic epidemiology; genome-wide; genome-wide analysis; genomic data; heterogenous data; heuristics; high dimensionality; interest; interoperability; metabolomics; multimodality; network models; novel; parallelization; public health relevance; reconstruction; simulation; software development; tool; transcriptomics; usability; user-friendly; virtual

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.

多模态 FACS 和 SUMOylation 数据的可扩展贝叶斯网络分析 推广到其他大型混合生物数据集 抽象的 贝叶斯或信念网络 (BN) 建模是一种强大的工具，目前正在成为一种强大的工具。 多模式（又称混合或混合）的主要数据分析、探索和可视化方法 我们之前开发了全面的 BN 算法。 近年来，我们致力于异构大生物数据分析。 已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）；开展另外三个项目（使用 FACS 推断免疫信号网络） 数据、全基因组 SUMOylation、阿尔茨海默病基因组分析）目前正在进行中。 在这项工作中，我们在方法论上确定了需要解决的关键“瓶颈” 水平，使 BN 分析在我们的一般环境（即生物大数据）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解释、评估和比较 由此产生的网络模型）尚未在该领域得到充分解决，从而限制了 原本非常强大且优雅的 BN 方法的可用性。 经过测试，该项目的主要目标是开发新颖的 BN 分析算法 强调 (a) 可扩展性、(b) 处理混合数据类型以及 (c) 所得网络的解释 我们对定量流式细胞术的 BN 分析特别感兴趣。 (FACS) 数据是作为正在进行的希望之城癌症免疫遗传学研究项目的一部分而生成的， 因为此类数据体现了 BN 建模挑战以及算法和软件方面的任何进步 我们随后将把开发推广到大多数生物大数据实例。 将 BN 分析应用于 SUMOylation 和染色质相互作用基因组数据（也生成为 正在进行的希望之城合作研究项目的一部分），以进一步测试普遍性，并 产生额外的生物学结果。