FET: Small: Embracing the dropouts in high-throughput genomics analysis

基因组学是一个快速发展的领域，它在分子水平的数据采集技术和高维数据的分析算法以及生物学见解和机制的发现方面取得了许多进步。该项目旨在开发数据分析算法，以解决基因组学中的计算挑战。基因组学分析经常产生复杂的数据集，这些数据集规模大，维度高。此外，数据集通常包含相当数量的缺失值，这使得计算分析和生物学解释相当具有挑战性。在几乎所有现有的计算算法中，缺失值都被视为一个需要修复的问题。相比之下，这个项目对缺失值采取了一种新颖的观点，并将它们视为有用的信号。这是一种独特的视角，与文献中关于缺失值的常见假设相反。发展和建立这一观点有可能揭示新的生物学见解，激发算法发展的新方向，并在课程材料和研究活动方面产生教育和培训机会。这个项目的目标是检查缺失值的效用在两种类型的基因组分析。本项目的目的1集中于单细胞rna测序分析。由于单细胞水平的信号通常相当低且随机，因此单细胞RNA测序通常产生高度稀疏的基因表达数据集，零的百分比通常大于90%。该目标将开发和验证算法，以根据单细胞RNA测序数据中的缺失值识别和聚集细胞类型。本项目的目的2集中在大组织rna测序分析。尽管大组织RNA测序提供了比单细胞更强的信号，但大组织数据中也存在缺失值，这主要是由于基因表达的组织特异性。这一目标将检查跨越多种组织类型和疾病的大规模基因表达数据集的缺失值模式，并探索缺失值与感兴趣的临床变量相关的效用。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。

Genomics is a fast evolving field that has led to many advances in data-acquisition technologies at the molecular level and analytics algorithms for high-dimensional data, as well as discoveries of biological insights and mechanisms. This project aims to develop data-analytics algorithms to address computational challenges in genomics. Genomics analyses often generate complex datasets that are large in size and high in dimensionality. In addition, the datasets often contain considerable amounts of missing values, which makes computational analysis and biological interpretation quite challenging. In almost all existing computational algorithms, the missing values are treated as a problem to be fixed. In contrast, this project takes a novel view of the missing values, and considers them as useful signals. This is a unique perspective, opposite to the common assumptions on missing values in the literature. Developing and establishing this perspective has the potential to reveal new biological insights, inspire new directions for algorithmic developments, and generate educational and training opportunities in terms of both course materials and research activities. The goal of this project is to examine the utility of missing values in two types of genomic analyses. Aim 1 of this project focuses on single-cell RNA-sequencing analysis. Since signals at the single-cell level are typically quite low and stochastic, single-cell RNA sequencing often produces gene-expression datasets that are highly sparse, with the percentage of zeros typically greater than 90%. This aim will develop and validate algorithms to identify and cluster cell types based on the missing values in single-cell RNA sequencing data. Aim 2 of this project focuses on bulk-tissue RNA-sequencing analysis. Although bulk-tissue RNA sequencing provides stronger signal than single cells, missing values also exist in bulk-tissue data, mainly due to tissue specificity of gene expression. This aim will examine the patterns of missing values across large-scale gene-expression datasets spanning across multiple tissue types and diseases, and explore the utility of missing values in correlating with clinical variables of interest.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.