Construction of cell specific gene co-regulations signatures based on single cell transcriptomics analysis

在组织样本上测量的基因表达值是其所有组成细胞的集合表达。 由于存在不同类型的细胞，研究来自组织样本的基因表达是困难的 在组织里。要全面探索不同类型的细胞如何交互影响组织或疾病的发展， 更广泛地说，从基于组织的转录数据分析中进行稳健的机制解释，重要的是 将观察到的组织水平的基因表达解释为其细胞成分的表达水平的组合。 基因在单个细胞中的表达受一组转录调控信号(TRSS)的调控，如 转录因子、miRNAs、lncRNA和表观基因组调节因子。卵磷脂细胞类型特异性表达贡献为 相当于在组织样本的不同细胞成分中识别真正的细胞类型特定的TRSS。考虑到 哺乳动物细胞中高度多样化的TRs类型不能用目前的实验方法同时测量，我们将 通过数学上定义良好的共调控模块对细胞类型的特定TRS进行建模和量化 基于单细胞RNA-Seq数据。我们假设，在多个细胞中，由共同TRs共同调控的基因可能是 在单细胞RNA-Seq数据中具有特征，并形成TRS的基因签名。从数学上讲，这样的问题可以是 作为检测单细胞表达的Malrix中的子矩阵，其中基因共享一致的表达 在某些单细胞样本上的模式。我们的初步数据表明，这个问题可以通过双聚类来解决。 基于局部低秩子矩阵的检测方法。 在这个项目中，我们建议开发一个计算基础设施来获得细胞类型的基因签名 从单细胞RNA-Seq数据中提取特定的TRSS并分解组织转录数据对TRSS在ITS中的贡献 组件单元格。具体地说，我们有以下三个目标：(1)对TRS和相关的共同监管进行数学建模 (2)提出了一种新的双聚类识别算法 单细胞转录数据中的条件/细胞类型特定的共调控基因模块；以及(3)识别和注释 每个TRs的基因签名，并估计独立组织数据中每个TRs的水平。 最近的研究表明，基质细胞和免疫细胞在癌症的进展和转移中起着至关重要的作用。 我们将把计算方法应用于TCGA组织表达和来自其他来源的单细胞表达数据，以 定量估计癌症组织内不同细胞类型的特定细胞类型的TRSS水平。所有已开发的 计算工具和衍生知识将保存在网络服务器/数据库中，供公众使用。

The gene expression values measured on a tissue sample is the, ensemble expression of all its comprising cells. Studying gene expression derived from tissue samples is COl)lplicated by the fact that there exist heterogeneous cell types in the tissue. For a full exploration of how different cell types interactively impact the development of a tissue or a disease, and more generally, robust mechanism interpretations from tissue-based transcriptomic data analysis, it is important to decipher the observed tissue-level gene expressions to the combination of expression levels of its cell components. A gene's expression in an individual cell is regulated by ,a set of transcriptional regulatory signals (TRSs) such as transcription factors, miRNAs, lncRNA, and epigenomic regulators. Oeciphering cell-type specific expression contribution is equivalent to identifying the true cell-type specific TRSs in different cell components of a tissue sample. Considering that the highly diverse TRS types in mammalian cells cannot be simultaneously measured by current experimental methods, we will model and quantify cell-type specific TRSs via mathematically well-defined co-regulation modules of their regulated genes based on single-cell RNA-Seq data. We hypothesize that the genes co-regulated by a common TRS in multiple cells can be characterized in single-cell RNA-Seq data and form gene signatures of the TRS. Mathematically, such a problem can be formulated as detection of a submatrix in a single-cell expression malrix, where the genes share coherent expression patterns over certain single-cell samples. Our preliminary data demonstrated that this problem can be solved by a biclustering based local low-rank submatrix detection approach. In this project, we propose the development of a computational infrastructure to derive gene signatures of cell-type specific TRSs from single-cell RNA-Seq data and decompose a tissue transcriptomic data to the contributions of TRSs in its component cells. Specifically, we have the following three aims: (1) Mathematically model TRS and associated co-regulation gene modules through transcriptomic profiles of single cells; (2) Develop a novel bi-clustering algorithm for identifying condition/cell-type specific co-regulated gene modules in single-cell transcriptomic data; and (3) Identify and annotate the gene signatures for each TRS, and estimate the level of each TRS in independent tissue data. Recent studies revealed the crucial impact of stromal and immune cells on the progression and metastasis of cancer. We will apply the computational methods to TCGA tissue expression and single-cell expression data from other sources, to quantitatively estimate the level of cell type-specific TRSs for different cell types within a cancer tissue. All the developed computational tools and derived knowledge will be maintained into/ a web server/database for public utilization.