Computational Tools for Single Cell Analysis: Application to Retinal Degeneration

识别视网膜变性过程中改变的调节网络将为深入了解其机制提供帮助 潜在的视网膜疾病。在单细胞水平上分析这种网络扰动将帮助我们准确地确定 在传统的大样本分析中可能遗漏的关键分子事件。然而，为了 识别视网膜疾病中改变的网络仍然具有挑战性，部分原因是缺乏强有力的 计算工具。首先，许多聚类方法产生不同的结果，有时甚至是相互冲突的结果。第二, 单细胞表达可用于检测以前未识别的细胞类型，而当前聚类 算法往往不够灵敏，无法检测出新颖的、有时是罕见的细胞类型。第三，基因组 从大样本和单个细胞获得的相互作用可能是相辅相成的， 在基因调控、共表达和蛋白质-蛋白质相互作用方面反映了不同的方面。小说 需要综合的方法来最大化我们从这些基因组数据集中获得的信息。致信地址 为了应对这些挑战，我们将开发单细胞数据分析的计算方法。具体来说，我们将 建立单细胞基因表达分析的迭代聚类方法(目标1)。我们的方法是 设计成坚固和敏感的。然后我们将开发一种方法，通过以下方式确定活跃的监管网络 从大样本中整合单细胞RNA-Seq数据集和ATAC-Seq(目标2)。这种方法将使我们能够 以识别单个细胞级别的调节电路。然后我们将在视网膜中进行单细胞RNA-Seq 退化模型(目标3)，并将我们的计算方法应用于数据集。我们预计将确定 参与光感受器退化的驱动因素和途径。最后，我们将为细胞开发一个数据库 通过分析可公开获得的单细胞数据集来标记基因(目标4)。我们认为，计算 我们建议开发的算法和数据库将成为研究界的宝贵资源。在- 对视网膜退行性变模型的深入研究将揭示导致疾病和 为视网膜退行性疾病提供新的治疗靶点。

Identifying the regulatory networks altered during retinal degeneration will provide insights into the mechanisms underlying retinal disease. Analysis of such network perturbation at the single cell level will help us to pinpoint the key molecular events that could be missed in traditional analysis using the bulk samples. However, to identify the altered networks in retinal disease is still challenging, partly due to the lack of powerful computational tools. First, many clustering methods yield different and sometimes conflicting results. Second, single cell expression can be used to detect previously unrecognized cell types, while the current clustering algorithms are often not sensitive enough to detect novel, sometimes rare, cell types. Third, genomic interactions obtained from bulk samples and single cells are likely to be complementary to each other and reflect different aspects in terms of gene regulation, co-expression and protein-protein interactions. Novel integrative methods are desired to maximize the information we gain from these genomic datasets. To address these challenges, we will develop computational approaches for single cell data analysis. Specifically, we will develop an iterative clustering method for single cell gene expression analysis (Aim 1). Our approach is designed to be robust and sensitive. We will then develop a method to determine active regulatory networks by integrating single cell RNA-Seq dataset and ATAC-Seq from bulk samples (Aim 2). This method will enable us to identify the regulatory circuits at the single cell level. We will then perform single cell RNA-Seq in retinal degenerative models (Aim 3) and apply our computational approaches to the dataset. We expect to identify the drivers and pathways involved in photoreceptor degeneration. Finally, we will develop a database for cell marker genes by analyzing publically available single cell datasets (Aim 4). We believe that the computational algorithms and database we propose to develop will be valuable resource for the research community. The in- depth study on retinal degenerative models will reveal key molecular events that lead to the disease and provide novel therapeutic targets for the retinal degenerative diseases.