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

FET：小型：拥抱高通量基因组学分析中的丢失

• 批准号: 2007029
• 负责人: Peng Qiu
• 金额: $ 26.49万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007029&HistoricalAwards=false
• 关键词: FET; Small; Embracing; dropouts; throughput

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.

基因组学是一个快速发展的领域，它导致了分子层面的数据获取技术和高维数据分析算法的许多进步，以及对生物学见解和机制的发现。该项目旨在开发数据分析算法，以应对基因组学中的计算挑战。基因组学分析通常产生规模大、维度高的复杂数据集。此外，数据集经常包含大量的缺失值，这使得计算分析和生物学解释非常具有挑战性。在几乎所有现有的计算算法中，缺失值都被视为一个待修复的问题。相比之下，这个项目以一种新的视角看待缺失的值，并将它们视为有用的信号。这是一个独特的视角，与文献中关于缺失价值的常见假设相反。发展和建立这种观点有可能揭示新的生物学见解，启发算法开发的新方向，并在课程材料和研究活动方面创造教育和培训机会。这个项目的目标是研究缺失数值在两种类型的基因组分析中的效用。该项目的目标1侧重于单细胞RNA测序分析。由于单细胞水平的信号通常相当低且随机，单细胞RNA测序通常产生高度稀疏的基因表达数据集，零值的百分比通常大于90%。这一目标将开发和验证基于单细胞RNA测序数据中缺失的值来识别和分类细胞类型的算法。该项目的目标2侧重于散装组织RNA测序分析。尽管大组织RNA测序提供了比单个细胞更强的信号，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，并探索缺失值在与临床相关变量相关方面的效用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Immune-Microbiota Crosstalk Underlying Inflammatory Bowel Disease

炎症性肠病背后的免疫微生物群串扰

• DOI: 10.1007/978-3-030-91415-8_2
• 发表时间: 2021
• 期刊: Z. (eds
• 作者: Xu, Congmin;Mac, Quoc D.;Jia, Qiong;Qiu, Peng
• 通讯作者: Qiu, Peng

Identification of Protein Markers Predictive of Drug-Specific Survival Outcome in Cancers

• DOI: 10.1007/978-3-030-91415-8_6
• 发表时间: 2021
• 期刊: Journal of Power Sources
• 影响因子: 9.2
• 作者: Shuting Lin;Jie-Chao Zhou;Yiqiong Xiao;B. Neary;Yong Teng;Peng-Chao Qiu

scMODD: A model-driven algorithm for doublet identification in single-cell RNA-sequencing data

• DOI: 10.3389/fsysb.2022.1082309
• 发表时间: 2023-01
• 作者: Xinye Zhao;Alexander Du;Peng-Chao Qiu

Quantifying Cell-Type-Specific Differences of Single-Cell Datasets Using Uniform Manifold Approximation and Projection for Dimension Reduction and Shapley Additive exPlanations

使用统一流形近似和投影进行降维和 Shapley 加法解释来量化单细胞数据集的细胞类型特异性差异

• DOI: 10.1089/cmb.2022.0366
• 发表时间: 2023
• 期刊: Journal of Computational Biology
• 影响因子: 1.7
• 作者: Lim, Hong Seo;Qiu, Peng
• 通讯作者: Qiu, Peng

Comparison of cell type annotation algorithms for revealing immune response of COVID-19

• DOI: 10.3389/fsysb.2022.1026686
• 发表时间: 2022-10
• 期刊: Electrochemical and Solid State Letters
• 作者: Congmin Xu;Huyun Lu;Peng Qiu