Testing and Estimation for Multi-Modality Single Cell Genomics

该项目旨在为一种被称为单细胞CRISPR筛选的新技术产生的数据开发新的统计分析方法，该技术有望帮助揭示人类疾病的分子机制并指导药物开发。这项技术通过敲除关键但鲜为人知的基因组区域的功能，然后测量对细胞的影响，从而发现这些区域的功能。鉴于这些可能性，该技术在学术界和工业界都引起了极大的兴趣，导致其迅速得到采用。然而，对CRISPR筛选产生的数据进行统计分析对实现其承诺提出了重大挑战。PI将处理该技术提出的几个最重要的统计分析挑战，并在软件工具中实现由此产生的方法。这些工具将帮助科学家从他们的单细胞CRISPR筛选数据中得出可靠的结论，从而加速人类疾病的预后、诊断和治疗的进展。该项目将通过让研究生参与研究，为他们提供培训机会。PI最近利用蒸馏条件随机化试验（dCRT）方法设计了SCEPTRE，这是一种计算效率高、校准良好、功能强大的单细胞CRISPR筛选分析方法。在这个项目中，PI将把他最近在单细胞CRISPR筛选的关联测试方面的工作扩展到更具挑战性的问题设置（非介入筛选）和更具挑战性的推理任务（估计和相互作用检测）。首先，PI将扩展SCEPTRE的基础思想，以解决多组学筛选（CRISPR筛选的观察对立物）中的关联测试问题。其次，PI将开发一种程序，以评估敲除调节元件对基于单细胞CRISPR筛选的基因表达的影响。这将需要更仔细地考虑CRISPR扰动的间接测量机制。第三，PI将设计一个测试两个调控元素之间的相互作用，同样基于CRISPR筛选。这个问题更难，但对于了解基因调控的复杂性至关重要。最后，所得到的方法将在一个集成的软件包中实现，该软件包旨在实际应用于真实的单细胞数据。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to develop new statistical analysis methodologies for data produced by a new technology known as single cell CRISPR screens, which promise to help unravel the molecular mechanisms of human disease and guide drug development. This technology discovers the functions of crucial but poorly understood genomic regions by knocking them out and then measuring the impact on the cell. Given these possibilities, this technology has attracted enormous interest in both academia and industry, leading to its rapidly growing adoption. However, the statistical analysis of the data produced by CRISPR screens presents a major challenge to realizing their promise. The PI will tackle several of the most important statistical analysis challenges presented by this technology and implement the resulting methodologies in software tools. These tools will help scientists draw reliable conclusions from their single cell CRISPR screen data and therefore accelerate progress in the prognosis, diagnosis, and treatment of human disease. The project will provide training opportunities to graduate students by involving them in the research. The PI recently leveraged the distilled conditional randomization test (dCRT) methodology to design SCEPTRE, a computationally efficient, well-calibrated, and powerful analysis method for single cell CRISPR screens. In this project, the PI will expand his recent work on association testing for single cell CRISPR screens to more challenging problem settings (non-interventional screens) and more challenging inferential tasks (estimation and interaction detection). First, the PI will extend the ideas underlying SCEPTRE to address the problem of association testing in multi-omics screens, the observational counterparts of CRISPR screens. Second, the PI will develop a procedure to estimate the effect of knocking out a regulatory element on the expression of a gene based on single cell CRISPR screens. This will require more carefully accounting for the indirect measurement mechanism of CRISPR perturbations. Third, the PI will design a test of interaction between two regulatory elements, also based on CRISPR screens. This problem is even harder but critical to capturing the complexity of gene regulation. Finally, the resulting methods will be implemented in an integrated software package designed for practical application to real single cell data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.