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

项目摘要 最近的单细胞RNA测序（scRNAseq）研究揭示了复杂的肿瘤 以异质细胞类型之间复杂的相互作用为特征的生态系统， 多样化的转录程序。差异共表达（DC）分析正在兴起， 基因差异表达分析（DE）的一个重要补充 剖析数据。DC分析可以检测基因对之间的相关性变化， 不同的调制条件。然而，大多数DC分析方法最初是 设计用于微阵列或批量RNAseq数据。迫切需要 开发先进的直流分析技术，适合的特点， 单细胞数据、研究设计和生物学目的。 在目标1中，我们将开发一个新颖、灵活的基于贝叶斯模型的框架 命名为scDECO，以提高使用 scRNAseq数据。使用从各种scRNAseq实验方案产生的数据，我们 将评估所提出的scDECO算法，并进行基准分析， 将我们提出的方法与当前的方法进行比较。这些分析将提供一个 更好地了解这些方法的优点和局限性。 在Aim 2中，我们将使用来自 黑色素瘤和前列腺循环肿瘤细胞。通过识别临床相关的 DC基因对使用单细胞数据，这一发现可以促进对 癌症干细胞样细胞和其他细胞中的转录共调节过程 肿瘤微环境此外，拟议的框架有可能 通过整合基因共表达改进临床疾病严重性预测 信息纳入风险评分计算。预测性能的建议 将使用scRNAseq和批量RNAseq数据进一步评估算法。最后， 在目标3中，将分发免费提供的R/Bioconductor软件包。的 R/Bioconductor环境都是生物医学研究人员非常常用的环境。 最终，这一拟议的框架将加速寻求了解 肿瘤中的差异共调节转录活性。

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.