Testing and Estimation for Multi-Modality Single Cell Genomics

该项目旨在为单细胞CRISPR筛选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知之甚少的基因组区域，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，这项技术引起了学术界和工业界的极大兴趣，导致其迅速增长的采用。然而，CRISPR筛选产生的数据的统计分析对实现其承诺提出了重大挑战。PI将解决该技术提出的几个最重要的统计分析挑战，并在软件工具中实施由此产生的方法。这些工具将帮助科学家从单细胞CRISPR筛选数据中得出可靠的结论，从而加快人类疾病的预后、诊断和治疗进展。该项目将为研究生提供培训机会，让他们参与研究。PI最近利用蒸馏条件随机化测试（dCRT）方法来设计SCEPTRE，这是一种用于单细胞CRISPR筛选的计算效率高、校准良好且功能强大的分析方法。在这个项目中，PI将把他最近关于单细胞CRISPR筛选关联测试的工作扩展到更具挑战性的问题设置（非干预性筛选）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的基本思想，以解决多组学筛选中的关联测试问题，CRISPR筛选的观察对应物。其次，PI将开发一种程序，以基于单细胞CRISPR筛选来估计敲除调控元件对基因表达的影响。这将需要更仔细地解释CRISPR扰动的间接测量机制。第三，PI将设计两个调控元件之间相互作用的测试，也是基于CRISPR筛选。这个问题甚至更难，但对于捕捉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，该软件包是为实际应用而设计的，以真实的单细胞data.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.