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

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的组织样本的方法。虽然单细胞测序技术为研究人员提供了一个新的前沿，但它们也伴随着一系列复杂的问题。这些问题之一与基因表达估计的质量有关，该估计用于许多下游分析，从预测细胞类型/轨迹到确定细胞类型或组织之间的差异表达基因。低覆盖率和测序效率低下可能会影响scRNA-Seq研究高达90%的基因表达估计，因此，克服这些问题具有挑战性。然而，目前解决这一问题的方法存在两个关键问题：(1)不充分使用大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用于基因表达估计.本项目将开发一个新的计算框架来将大量RNA-seq数据集成到scRNA-seq数据建模和分析中，旨在从稀疏的scRNA-seq数据中准确地估计基因表达，以及高质量、可靠性和精确度的下游分析.其目的是对不同细胞类型之间的异质基因表达模式的特定特征进行建模。通过去卷积整合大量的RNA-Seq数据将被用来开发不同种类的补偿分布和概率。利用伽马分布来确定单细胞基因表达估计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识别感兴趣的估计，然后将其与来自批量RNA-Seq数据的补偿信息相结合，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值将用于迭代该过程，以便为该过程的每个阶段提供改进的结果。结果将是一个新的分配框架，通过整合上述三个新特征，应该能够进行scRNA-Seq表达估计。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。

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.