Statistical methods for differential peak detection in Hi-C data

Hi-C 数据中差分峰检测的统计方法

• 批准号: 9904123
• 负责人: Hillary Koch
• 金额: $ 2.48万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9904123
• 关键词: 3-Dimensional; Address; Adopted; Affect; Alzheimer' s Disease; Architecture; Attention; Autoimmune Diseases; Bioconductor; Biological; Biological Assay; Cell Line; Cell Lineage; Cells; ChIP-seq; Chromatin; Communities; Complex; Computer software; Data; Data Analyses; Data Set; Dependence; Detection; Development; Disease; Disease Pathway; Distal; Enhancers; Evolution; Functional Magnetic Resonance Imaging; Gene Expression; Genetic; Genome; Genomic Segment; Genomics; Goals; Health; Heterogeneity; Human; Joints; Knowledge; Learning; Malignant Neoplasms; Measures; Methodology; Methods; Modeling; Oncogenes; Pathogenesis; Pathway interactions; Performance; Reproducibility; Research Personnel; Sampling; Scientist; Shapes; Somatic Mutation; Source; Statistical Methods; Structure; Techniques; Testing; Transcriptional Regulation; Work; cell type; computerized tools; design; differential expression; epigenetic regulation; experimental study; flexibility; frontier; genome-wide; in vivo; interest; novel; promoter; sound; stem cells; tool; transcriptome sequencing

Abstract: Hi-C is currently the most popular assay used to probe 3D chromatin organization within the cell genome-wide. Because loci far away in 1D genomic distance are often packed close together in 3D space, enhancer-promoter interactions can occur between distal regions of the genome. Importantly, this genome structure is well-conserved across cell types and even species, and dysregulation of this structure has been implicated as a source of aber- rant gene expression associated with diseases such as Alzheimer's, autoimmune disorders, and cancer. Thus, it is necessary that powerful methods be made available to pinpoint differential interactions between healthy and diseased cells in order to accurately identify new sources of pathogenesis and potential pathways for treatment. Analysis of Hi-C data is challenging because the unique spatial structure in the data, which implies both a 1D genomic distance dependence and a 3D spatial dependence, requires careful attention. Statistical tools that do not account for these dependencies suffer from reduced power to detect interactions, especially those between distal chromosomal regions. Further, methods for differential peak detection between a pair of Hi-C datasets are underdeveloped, and methods that scale to multiple joint comparisons are wholly missing. I propose to address these problems by developing a statistically rigorous methodology for detecting differential peaks in Hi-C data that both accounts for Hi-C's hallmark spatial dependence structure and scales to multiple joint comparisons across biological conditions (i.e. cell types, cell lineages, or experimental and control types). I hypothesize that this approach will greatly boost power to detect differential interactions in Hi-C samples. Moreover, with software made available to the public, scientists will be able to apply these tools to identify new drivers of pathogenesis, ultimately benefitting human health. I will conduct this work under the close guidance of a sponsor and co- sponsor, respectively, with statistical and biological expertise with Hi-C data.

摘要： Hi-C是目前用于探测细胞全基因组内的3D染色质组织的最流行的测定。 因为在1D基因组距离中远离的基因座通常在3D空间中紧密地堆积在一起，所以增强子-启动子 相互作用可以发生在基因组的远端区域之间。重要的是，这种基因组结构是非常保守的， 跨细胞类型，甚至物种，这种结构的失调已被牵连作为aber的来源， 与阿尔茨海默氏症、自身免疫性疾病和癌症等疾病相关的基因表达。因此，在本发明中， 有必要提供强有力的方法来查明健康和 患病细胞，以便准确地识别新的发病机制来源和潜在的治疗途径。 Hi-C数据的分析是具有挑战性的，因为数据中的独特空间结构，这意味着1D 基因组距离依赖性和3D空间依赖性，需要仔细注意。统计工具， 不考虑这些依赖性会降低检测相互作用的能力，特别是那些 染色体末端区域此外，描述了用于一对Hi-C数据集之间的差分峰值检测的方法。 不发达，和方法，规模多个联合比较完全缺失。我提议 这些问题，通过开发一种统计上严格的方法，用于检测Hi-C数据中的差异峰 这既解释了Hi-C的标志性空间依赖结构，又扩展到多个联合比较 跨生物条件（即细胞类型、细胞谱系或实验和对照类型）。我假设 这种方法将大大提高检测Hi-C样品中差异相互作用的能力。此外，随着软件 向公众提供，科学家将能够应用这些工具来确定发病机制的新驱动因素， 最终贝内人类健康。我将在一个赞助商和共同赞助商的密切指导下开展这项工作。 分别为具有Hi-C数据统计学和生物学专门知识的赞助商。