CAREER: Statistical modeling of single-cell states

健康和患病的组织都是由多种细胞类型组成的，它们的相互作用是它们功能的基础。即使在给定的细胞类型中，由于不同的外部影响、细胞历史或随机噪声，单个细胞的确切状态也会有所不同。这种异质性对生物组织的建模和理解提出了重大挑战。快速发展的单细胞测量现在可以提供有关细胞状态的全面数据，从而能够对复杂组织的组成进行无偏见的分析。然而，这样的测量本身就有噪声，需要专门的统计和计算工具进行分析。该项目将开发灵敏的统计方法，用于从单细胞转录组数据中识别和表征生物学上不同的细胞子集，并将应用它们来研究人类、小鼠和其他生物体神经组织的细胞组成和功能。这样的特征很可能为大脑发育的机制提供有价值的见解。所开发的工具将广泛适用于其他生物学背景。最后，本计画所制作的教学资料将引导初学者与进阶学生了解现代生物测量所需的统计与计算技术，并发展出一套以单细胞转录物资料为基础的细胞异质性分析方法。基于模型的因子分析将被用来捕获结构的转录异质性内的测量细胞群体的方式是高度宽容的技术和生物变异性固有的thessinglecell测量。将制定纳入现有空间信息和预测亚群空间定位的方法。将开发统计方法，以确定单细胞数据的基因调控依赖性。这些方法将被应用于分析人类和模式生物的神经组织中的转录异质性和调控过程。为了促进相关分析方法的教学，该项目将开发一系列互动练习，这些练习将说明大多数分析方法假设的常见计数过程，用于估计计数数据不确定性的统计工具，以及用于处理单个-细胞测序数据。为了覆盖更广泛的学生受众，交互式教学工具将具有可适应的难度水平，并且可以直接访问通过网络。更多信息和该项目的持续成果将发布在：http://pklab.med.harvard.edu/peterk/nsf/CAREER.html

Both healthy and diseased tissues are composed of multiple cell types whose interplay underpins theirfunctions. Even within a given cell type, individual cells will differ in their exact state due to distinct externalinfluences, cell's history, or random noise. Such heterogeneity presents a major challenge for modeling andunderstanding biological tissues. Rapidly progressing single-cell measurements can now providecomprehensive data on cell state, enabling unbiased analysis of composition of complex tissues. Suchmeasurements, however, are inherently noisy and require specialized statistical and computational tools for theiranalysis. The project will develop sensitive statistical methods for identification and characterization ofbiologically distinct subsets of cells from single-cell transcriptome data, and will apply them to investigate cellularcomposition and function of neural tissues in humans, mice and other organisms. Such characterization willlikely provide valuable insights into the mechanisms underlying brain development. The developed tools will bewidely applicable in other biological contexts. Finally, the instructional material created by this project willintroduce beginning and advanced students to the fundamental set of statistical and computational techniquesnecessary to understand modern biological measurements.The research will develop an approach for analysis cell heterogeneity based on the single-cell transcriptomedata. Model-based factor analysis will be used to capture the structure of the transcriptional heterogeneity withinthe measured cell populations in a way that is highly tolerant of technical and biological variability inherent to thesingle-cell measurements. Methods for incorporating available spatial information and predicting spatiallocalization of subpopulations will be developed. Statistical methods will be developed to identify generegulatory dependencies from single-cell data. The approaches will be applied to analysis of transcriptionalheterogeneity and regulatory processes in neuronal tissues of humans and model organisms. To facilitateteaching of relevant analysis methods, the project will develop a series of interactive exercises, which willillustrate common counting processes that underlie the assumptions of most analysis methods, statistical toolsfor estimating uncertainty from count data, and common algorithms used to process single-cell sequencing data.To reach wider student audience the interactive instructional tools will have an adaptable difficulty level and willbe directly accessible over the web. More information and ongoing results of this project will be posted at: http://pklab.med.harvard.edu/peterk/nsf/CAREER.html