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，这些细胞类型的相互作用支撑了它们的功能。即使在给定的细胞类型中，由于不同的外部影响、细胞的历史或随机噪声，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关于细胞状态的分解数据，使得能够对复杂组织的成分进行无偏见的分析。然而，这样的测量本身就有噪音，需要专门的统计和计算工具来进行分析。该项目将开发敏感的统计方法，从单细胞转录组数据中识别和表征生物上不同的细胞亚群，并将它们应用于研究人类、小鼠和其他生物体的神经组织的细胞组成和功能。这样的描述可能会为研究大脑发育的机制提供有价值的见解。所开发的工具将广泛应用于其他生物学领域。最后，由这个项目创建的教学材料将向初学者和高级学生介绍理解现代生物测量所必需的统计和计算技术的基本集合。这项研究将开发一种基于单细胞转录数据分析细胞异质性的方法。基于模型的因子分析将被用来捕捉被测细胞群体内转录异质性的结构，这种结构对单细胞测量固有的技术和生物变异性具有高度的容忍度。将开发纳入可用空间信息和预测子种群空间定位的方法。将开发统计方法，以从单细胞数据中确定世代调控相关性。这些方法将被应用于人类和模式生物神经组织中转录异质性和调控过程的分析。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，说明作为大多数分析方法假设基础的常见计数过程、从计数数据估计不确定度的统计工具，以及用于处理单细胞测序数据的常见算法。为了接触到更广泛的学生受众，交互式教学工具将具有适应的难度水平，并将通过网络直接访问。该项目的更多信息和正在进行的结果将在以下网址公布：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