Statistical methods for improving reproducibility and utility of chromatin interaction data

项目摘要 基因组在细胞核中的空间组织在转录调控中起着重要的作用。 基因.目前，Hi-C是最广泛使用的高通量技术，其探测全基因组空间分布， 染色质的组织。然而，Hi-C实验涉及多个复杂的实验步骤， 引入了各种各样的偏见。许多数据分析挑战仍然必须克服，以达到 对数据进行可靠和可重复的生物学解释。每项研究的小样本量 进一步限制了数据分析的能力和可靠性。当重复样品可用时，重现性 重复样本之间的差异告诉我们识别的保真度，并且可能它可以用于 检测在单个样品中太小而不能可靠检测的可再现信号。即使是样品 通过联合分析，可以从不同的细胞中借用信息，以提高对两者的识别能力 拓扑相关域（TADs）和具有不同结构的区域。该项目提出， 开发一套新的统计方法，使用重复提供的再现性信息， 样品，以选择可靠的鉴定和提高峰调用和重复调用的准确性。 此外，它提出了一个联合分析框架，以确定特定条件的体系结构差异 不同的细胞。目标1将开发统计方法来评估鉴定的重复性 染色质环和选择可重复的鉴定。基于可复制性的选择标准 补充了通常对单个样本的显著性测量，但具有可比性的优点 跨数据集、协议和不同的重要性度量。目标2将开发稳健的联合多样本 峰值呼叫和峰值呼叫方法。这些方法将允许一个协同信息跨样本 并适当考虑重复样本之间的差异，最终提高分析的能力， 减少假阳性。目标3将开发统计方法， 不同细胞类型、细胞状况或疾病状态之间的差异。包括在每个拟议目标中 这些方法的输出的严格评估，利用正交表观基因组数据和实验 根据分析方法的结果进行假设检验。这些方法将使用户能够 产生可靠和强大的科学解释，并最终促进对核武器的理解。 组织及其在基因表达和细胞功能中的作用。

Project Summary The spatial organization of the genome in the nucleus plays an important role in the transcriptional control of genes. Currently, Hi-C is the most widely used high-throughput technique that probes the genome-wide spatial organization of chromatin. However, Hi-C experiments involve multiple complex experimental steps, introducing various sources of biases. Many data-analytical challenges still must be overcome to reach reliable and reproducible biological interpretations of the data. The small sample size of each individual study further limits the power and reliability of data analyses. When replicate samples are available, reproducibility across replicate samples informs us about the fidelity of the identification, and potentially it can be used to detect reproducible signals that are too modest to be detected reliably in individual samples. Even for samples from different cells, information may be borrowed through joint analyses to improve the identification of both topologically associated domains (TADs) and regions with different structures. This project proposes to develop a suite of new statistical methods that use the reproducibility information provided by replicate samples to select reliable identifications and to improve the accuracy of peak calling and TAD calling. Furthermore, it proposes a joint analysis framework to identify condition-specific architectural differences across different cells. Aim 1 will develop statistical methods to evaluate the reproducibility of identified chromatin loops and to select reproducible identifications. The reproducibility-based selection criterion complements the usual measure of significance on a single sample, but has the benefit of being comparable across data sets, protocols and different measures of significance. Aim 2 will develop robust, joint multi-sample peak calling and TAD calling methods. These methods will allow one to synergize information across samples and properly take account of variations across replicates, ultimately improving the power of the analysis and reducing false positives. Aim 3 will develop statistical methods for detecting TAD and other architectural differences between different cell types, cellular conditions, or disease status. Included in each proposed Aim are rigorous evaluations of the output of these methods utilizing orthogonal epigenomic data and experimental tests of hypotheses derived from the results of the analytical methods. These methods will enable users to generate reliable and robust scientific interpretation, and ultimately advance the understanding of nuclear organization and its role in gene expression and cellular function.