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。 有精神障碍的风险。大脑中基因表达的改变，特别是在特定细胞类型的 水平，被认为是通过这些基因变异增加风险的驱动因素。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数据，包括单细胞和组织水平的转录。其中一些样本涵盖了关键的发育期。一个突出的挑战是如何整合单细胞和组织水平的转录数据，以及基因变异如何 改变特定细胞的转录以产生精神病理学。在这个高维的组学环境中，我们 需要强大的统计和机器学习工具来生成综合分析并整合这些结果 利用庞大的精神病学基因数据集获得新的见解。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。我们超越了专门的机器学习模型 在预测方面，转而专注于提供可解释的统计推断。我们确定基因群落， 根据细胞类型和时空窗口定义，驱动风险。海量的数据带来了巨大的 基于非严格分析的虚假推断的风险。另一方面，可靠但幼稚的工具可以 通过不完全集成所有可用信息来牺牲权力。我们的总体目标是生产分析工具 得出可靠而有力的推论，将特定细胞类型的基因表达与遗传风险因素联系起来。 有了这些可供研究界使用的分析工具，我们的长期目标是加速该领域的发现，从而为出现精神障碍的治疗目标奠定基础。 我们的目标将通过以下具体目标来实现：1)统计严谨的方法选择 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)在整个发育过程中对动态基因群落进行建模 细胞谱系或组织，并将它们与基于社区的得分统计相关联，以深入了解 精神障碍的遗传危险因素；以及3)估计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资源 更有价值并支持下游分析，例如在更大的样本集中检测CTS eQTL 更强大的力量。动态网络分析工具增强了我们识别各种不同基因群落的能力 发育时期，这种变异有助于推断细胞类型和发育时期 有风险的因素。我们认为，提出的研究是创新的，因为它使用了新的统计方法 对来自多个来源的数据进行综合分析，并以尖端结果表示高维数据 数据以一种有意义的方式提供给集群和网络分析。

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.