Computational Methods to Integrate and Interpret the Transcriptome from Single Cell and Tissue Level Data

在过去的十年中，在发现遗传变异和相关基因方面取得了实质性进展， 有精神疾病的风险改变了大脑中的基因表达，特别是在细胞类型特异性 水平，被认为是通过这些遗传变异赋予风险的驱动因素。为了将改变的转录与精神病理学联系起来，正在积累大量的转录组学数据，包括单细胞和组织水平的转录组。其中一些样本涵盖了关键的发育时期。一个突出的挑战是如何整合单细胞和组织水平的转录组学数据，以及遗传变异如何在基因组学中发挥作用。 改变特定细胞的转录从而产生精神病理学在这个高维度的经济学环境中，我们 需要强大的统计和机器学习工具来进行综合分析并将这些结果融合在一起 利用大型精神病学遗传数据集来获得新的见解。我们建议利用我们在高维统计推断方面的专业知识来应对这一挑战。我们超越了机器学习模型， 在预测方面，重点是提供可解释的统计推断。我们识别基因群落， 根据细胞类型和时空窗口定义，驱动风险。有了大量的数据， 基于非严格分析的虚假推论的风险。另一方面，可靠但幼稚的工具可以 通过不完全整合所有可用信息来牺牲功率。我们的总体目标是生产分析工具 这产生了可靠和有力的推论，将细胞类型特异性基因表达与遗传风险因素联系起来。 通过向研究界提供这些分析工具，我们的长期目标是加快该领域的发现，从而为精神疾病的治疗目标奠定基础。 我们的目标将实现以下具体目标：1）统计严格的方法来选择 细胞类型标记和估计细胞类型特异性（CTS）表达，这将有助于下游分析，包括来自组织的CTS eQTL; 2）在整个发育过程中建模动态基因群落， 细胞谱系或组织，并将它们与基于社区的评分统计数据相关联，以深入了解 精神疾病的遗传风险因素; 3）评估基因共表达网络的新方法 从单细胞RNA-seq。这一贡献意义重大，因为它将使许多转录组学资源 更有价值，并能够进行下游分析，例如在更大的样品组中检测CTS eQTL， 更高的权力。动态网络分析工具增强了我们识别基因群落的能力， 发育时期，这种变化有助于推断细胞类型和发育时期 风险因素。在我们看来，这项研究是创新的，因为它使用了新颖的统计方法。 用于对来自多个来源的数据进行综合分析，并获得代表高维数据的尖端结果 数据以一种有意义的方式，适合于聚类和网络分析。

In the past decade, substantial progress has been made in discovery of genetic variants and genes associated with risk for psychiatric disorders. Altered gene expression in the brain, particularly at the cell-type-specific level, is believed to be a driving factor in conferring risk through these genetic variants. To link altered transcription to psychopathology, an immense amount of transcriptomic data is being accumulated, including single-cell and tissue level transcriptomes. Some of these samples cover critical developmental periods. An outstanding challenge is how to integrate single cell and tissue level transcriptomic data and how genetic variation alters transcription in specific cells to produce psychopathology. In this high dimensional ‘omics setting, we need powerful statistical and machine learning tools to produce integrative analyses and mesh those results with large psychiatric genetic datasets to achieve new insights. We propose to use our expertise in high dimensional statistical inference to tackle this challenge. We go beyond machine learning models that specialize in prediction, focusing instead on providing interpretable statistical inferences. We identify gene communities, defined in terms of cell type and spatiotemporal window, driving risk. With vast amounts of data comes great risk of spurious inferences based on non-rigorous analyses. On the other hand, reliable, but naïve tools can sacrifice power by not fully integrating all available information. Our overall objective to produce analytic tools that yield reliable and powerful inferences relating cell-type-specific gene expression with genetic risk factors. With these analytical tools made available to the research community, our longer-term goal is to hasten discoveries in the field and thus build the foundation from which therapeutic targets for psychiatric disorders emerge. Our objectives will be accomplished with the following Specific aims: 1) statistically rigorous methods to select cell-type markers and to estimate cell-type-specific (CTS) expression, which will facilitate downstream analyses, including CTS eQTLs from tissue; 2) modeling dynamic gene communities throughout development of cell lineages or tissue and relating them to community-based-score statistics to gain insight into the impact of genetic risk factors on psychiatric disorders; and 3) novel methods for estimating gene co-expression networks from single cell RNA-seq. This contribution is significant because it will make many transcriptomic resources more valuable and enable downstream analyses, such as detection of CTS eQTLs in larger sample sets with higher power. Dynamic network analysis tools enhance our ability to identify gene communities that vary over developmental epochs and this variation facilitates inferences that relate cell type and developmental period with risk factors. The research proposed is innovative, in our opinion, because it uses novel statistical methods for integrative analysis of data from multiple sources, and cutting edge results to represent high dimensional data in a meaningful way that lends itself to clustering and network analysis.