EAGER: IIBR Informatics: A reinforced imputation framework for accurate gene expression recovery from single-cell RNA-seq data

EAGER：IIBR 信息学：从单细胞 RNA-seq 数据中准确恢复基因表达的强化插补框架

• 批准号: 1945971
• 负责人: Qin Ma
• 金额: $ 30万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1945971&HistoricalAwards=false
• 关键词: EAGER; IIBR; Informatics; reinforced; imputation

Single-cell RNA-Seq (scRNA-Seq) analyses have revolutionized the methods in which researchers can investigate tissue samples of specific cell types. While single-cell sequencing technologies have provided a new frontier for researchers, they also come with a complex set of problems. One of these problems is related to the quality of gene expression estimates, which are used in numerous downstream analyses from the prediction of the cell types/trajectories to determining differentially expressed genes between cell types or tissues. The low coverage and sequencing inefficiencies can affect up to 90% of gene expression estimates for scRNA-Seq studies, and hence, are challenging to overcome. However, there are two critical problems in the way that current methods attempt to address this problem: (1) inadequate use of bulk data to compensate for low expression genes and (2) under-utilization of iterative procedures to optimize highly-connected steps for imputation of gene expression estimates.This project will develop a novel computational framework to integrate bulk RNA-seq data into scRNA-seq data modeling and analyses, aiming at accurate gene expression estimates from the sparse scRNA-Seq data, and high quality, reliability, and precision of downstream analyses. The aim is to model particular features of the heterogeneous gene expression patterns among various cell types. Integration of bulk RNA-Seq data through de-convolution will be used to develop heterogeneous compensation distributions and probabilities. Utilization of the gamma distribution to determine empirical distribution for single-cell gene expression estimates will improve the baseline expression in a specific cell type and identify estimates of interest through the high level of noise in sequencing data, which will then be combined with compensation information from bulk RNA-Seq data to correct biases from the high noise scRNA-Seq data. Finally, the updated expression estimate will be used to iterate back through the process to provide improved results for each stage of the process. The outcome will be a novel imputation framework that should enable scRNA-Seq expression estimates through the integration of the above three new characteristics.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.

单细胞RNA-Seq（scRNA-Seq）分析彻底改变了研究人员研究特定细胞类型组织样本的方法。虽然单细胞测序技术为研究人员提供了一个新的前沿，但它们也带来了一系列复杂的问题。这些问题之一涉及基因表达估计的质量，其用于从细胞类型/轨迹的预测到确定细胞类型或组织之间差异表达的基因的许多下游分析。低覆盖率和测序效率低下可能会影响scRNA-Seq研究中高达90%的基因表达估计，因此难以克服。然而，在当前方法试图解决该问题的方式中存在两个关键问题：（1）未充分利用批量数据来补偿低表达基因，（2）未充分利用迭代程序来优化高度连接的步骤以估算基因表达估计。本项目将开发一种新的计算框架，将批量RNA-seq数据整合到scRNA-seq数据建模和分析中，旨在从稀疏的scRNA-Seq数据中准确估计基因表达，以及下游分析的高质量、可靠性和精确性。其目的是模拟不同细胞类型之间异质基因表达模式的特定特征。通过去卷积的批量RNA-Seq数据的整合将用于开发异质补偿分布和概率。利用γ分布确定单细胞基因表达估计值的经验分布将改善特定细胞类型中的基线表达，并通过测序数据中的高水平噪声鉴定感兴趣的估计值，然后将其与来自批量RNA-Seq数据的补偿信息组合以校正来自高噪声scRNA-Seq数据的偏差。最后，更新后的表达式估计将用于在整个过程中进行回溯，以提供过程每个阶段的改进结果。 该奖项反映了NSF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。

项目成果

The use of single-cell multi-omics in immuno-oncology.

• DOI: 10.1038/s41467-022-30549-4
• 发表时间: 2022-05-18
• 期刊: Nature communications
• 影响因子: 16.6

Deep learning analysis of single-cell data in empowering clinical implementation.

• DOI: 10.1002/ctm2.950
• 发表时间: 2022-07
• 期刊: Clinical and translational medicine
• 影响因子: 10.6

scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses.

• DOI: 10.1038/s41467-021-22197-x
• 发表时间: 2021-03-25
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Wang J;Ma A;Chang Y;Gong J;Jiang Y;Qi R;Wang C;Fu H;Ma Q;Xu D
• 通讯作者: Xu D

Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data.

• DOI: 10.1038/s41467-022-34277-7
• 发表时间: 2022-10-30
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Chen, Junyi;Wu, Zhenyu;Qi, Ren;Ma, Anjun;Zhao, Jing;Xu, Dong;Li, Lang;Ma, Qin
• 通讯作者: Ma, Qin