scDECO: A novel statistical framework to identify differential co-expression gene combinations systematically using single-cell RNA sequencing data

scDECO：一种新颖的统计框架，利用单细胞 RNA 测序数据系统地识别差异共表达基因组合

基本信息

批准号：
10474599
负责人：
Yen-Yi Ho
金额：
$ 16.91万
依托单位：
UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2024-08-31
项目状态：
已结题

项目摘要

Project Summary Recent single-cell RNA sequencing (scRNAseq) studies have revealed complex tumor ecosystems characterized by intricate interactions between heterogeneous cell types and diverse transcriptional programs. Differential co-expression (DC) analysis is emerging as a crucial complement to the standard differential expression analysis (DE) for gene profiling data. DC analysis can detect correlation changes between pairs of genes across different modulating conditions. However, most DC analysis approaches are originally designed for use on either microarray or bulk RNAseq data. There is an urgent need to develop advanced DC analytical techniques that are tailored to the characteristics of single-cell data, study design and biological objectives. In Aim 1, we will develop a novel, flexible Bayesian model-based framework named scDECO to improve the accuracy of identifying DC gene combinations using scRNAseq data. Using data generated from various scRNAseq experiment protocols, we will evaluate the proposed scDECO algorithm and perform benchmarking analyses to compare our proposed approaches to current approaches. These analyses will provide a better understanding of the advantages and limitations of these methods. In Aim2, we will implement the scDECO algorithm using scRNAseq datasets from melanoma and prostate circulating tumor cells. By identifying sets of clinically relevant DC gene pairs using single-cell data, the findings can promote understanding of the transcriptional co-regulatory processes in cancer stem-like cells and other cells in the tumor microenvironment. Furthermore, the proposed framework has the potential to improve clinical disease severity prediction by incorporating gene co-expression information into risk score calculation. The predictive performance of the proposed algorithm will be further evaluated using both scRNAseq and bulk RNAseq data. Finally, in Aim3, freely available R/Bioconductor software packages will be distributed. The R/Bioconductor environments are both very commonly used by biomedical researchers. Ultimately, this proposed framework will accelerate studies seeking to understand the differential co-regulatory transcriptional activities in tumors.

项目摘要最近的单细胞RNA测序（SCRNASEQ）研究显示了复杂的肿瘤生态系统的特征是异质细胞类型之间的复杂相互作用和各种转录程序。差异共表达（DC）分析正在出现基因标准差分表达分析（DE）的关键补充分析数据。 DC分析可以检测基因对之间的相关性变化不同的调节条件。但是，大多数DC分析方法最初是设计用于微阵列或散装RNASEQ数据。迫切需要开发高级直流分析技术，这些技术是根据特征量身定制的单细胞数据，研究设计和生物学目标。在AIM 1中，我们将开发一个基于贝叶斯模型的新颖，灵活的框架命名为SCDECO，以提高使用DC基因组合的准确性 SCRNASEQ数据。使用从各种scrnaseq实验协议生成的数据，我们将评估所提出的SCDECO算法并执行基准分析将我们提出的方法与当前方法进行比较。这些分析将提供更好地了解这些方法的优势和局限性。在AIM2中，我们将使用SCRNASEQ数据集实现SCDECO算法黑色素瘤和前列腺循环肿瘤细胞。通过识别一组临床相关直流基因对使用单细胞数据，这些发现可以促进对癌症干细胞和其他细胞中的转录共调节过程肿瘤微环境。此外，提议的框架有可能通过纳入基因共表达来改善临床疾病的严重程度预测信息计算中的风险分数计算。提议的预测性能将使用SCRNASEQ和BOLK RNASEQ数据进一步评估算法。最后，在AIM3中，将分发免费可用的R/生物通用器软件包。这生物医学研究人员通常使用R/Bioconductor环境。最终，这个拟议的框架将加速试图了解的研究肿瘤中的差异共调节转录活性。