Developing tools for the unbiased analysis and visualization of scRNA-seq data

摘要 单细胞rna测序(scrna-seq)提供了全基因组范围的基因表达信息。 单个单元格的分辨率。这些史无前例的数据范围正在彻底改变我们对 发育和组织动态平衡以及癌症等疾病。然而，scRNA-seq的一个主要问题是， 是数据的剪切尺度，由大约20,000个以千到百万为单位的基因表达测量值组成 细胞的数量。显然需要有效的计算方法来转换如此大小和复杂的数据 转化为可操作的生物学见解。例如，scRNA-seq数据大约是20,000维的，并且 因此，所有可用的分析管道都依赖于多维降维步骤。这通常需要一个 结合了像PCA这样的线性工具和像t-SNE和UMAP这样的非线性技术。数据通常是 减少到10-D和100-D之间用于数据分析(例如，聚集到不同的细胞类型)和2-D用于 视觉化。然而，问题是降维可能会导致信息丢失。我们 最近的研究表明，这种信息的丢失是戏剧性的：对于任何给定的小区，超过95%的邻居都是 在降维过程中发生了变化。数据结构的这种完全改变可以 在分析中引入显著的噪声和偏差，并提出替代方法的迫切需要。 这一应用的前提是，减少scRNA-seq数据分析中的偏差将使我们的能力最大化 从数据中提取有意义的信息。在这个提案中，我们专注于开发新的算法来 解决典型分析管道中的三个具体步骤：(1)降维：我们的假设是 深度神经网络可以被明确地训练，以最大限度地增加可以保留的信息量 用于数据分析和可视化。(2)特征选择：并不是所有的基因对 下游分析，所以研究人员通常根据基因的变异来选择基因的子集 人口。我们已经证明，选择基因的标准方法将真正的生物变异与 实验产生的技术噪音。我们假设统计模型是基于我们对 技术噪音的来源可以用来选择更有信息量的基因。(3)小区集群：集群 确定细胞类型的数据至关重要，但具有不同身份的细胞通常会形成复杂的重叠 基因表达空间中现有算法难以解析的几何结构。我们的假设是 新的集群工具，以先验知识为指导，利用从映像进行集群的创新 分割，可以克服这个问题。我们将构建这些新工具，并针对现有工具进行测试 基准数据集和由我们的实验合作者生成的新颖数据。我们还将整合这些 工具放入流行的scRNA-seq分析包中。拟议工作的成功完成将使 从迅速发展的scRNA-seq数据集中提取更多的生物相关信息。

ABSTRACT Single-cell RNA sequencing (scRNA-seq) provides genome-wide information about gene expression at the resolution of individual cells. The unprecedented scope of these data is revolutionizing our understanding of development and tissue homeostasis as well as diseases like cancer. A major issue with scRNA-seq, however, is the shear scale of the data, consisting of ~20,000 gene expression measurements in thousands to millions of cells. Effective computational approaches are clearly required to translate data of this size and complexity into actionable biological insights. For instance, scRNA-seq data are approximately 20,000-dimensional, and as a result all available analysis pipelines rely on multiple dimensionality reduction steps. This usually entails a combination of linear tools like PCA and non-linear techniques like t-SNE and UMAP. The data is generally reduced to between 10- and 100-D for data analysis (e.g. clustering into distinct cell types) and 2-D for visualization. The problem, however, is that dimensionality reduction can lead to loss of information. We recently showed that this loss of information is dramatic: for any given cell, over 95% of its neighbors are changed in the process of dimensionality reduction. This complete change in the structure of the data can introduce significant noise and bias into the analysis, and suggests the critical need for alternative approaches. The premise of this application is that reducing bias in scRNA-seq data analysis will maximize our ability to extract meaningful information from the data. In this proposal, we focus on developing new algorithms to address three specific steps in the typical analysis pipeline: (1) Dimensionality Reduction: Our hypothesis is that deep neural networks can be explicitly trained to maximize the amount of information that can be retained for both data analysis and visualization. (2) Feature Selection: Not all genes are equally informative for downstream analyses, so researchers generally choose a subset of genes based on variation in the population. We have shown that standard approaches to selecting genes convolve true biological variation with technical noise from the experiment. We hypothesize that statistical models based on our understanding of sources of technical noise can be used to select more informative genes. (3) Cell clustering: Clustering the data to determine cell types is critical, but cells with different identities often form complex, overlapping geometries in gene expression space that are difficult for existing algorithms to resolve. Our hypothesis is that new clustering tools, guided by prior knowledge and leveraging innovations in clustering from image segmentation, can overcome this problem. We will build these new tools and test them against existing benchmark datasets and novel data generated by our experimental collaborators. We will also integrate these tools into popular scRNA-seq analysis packages. Successful completion of the proposed work will allow the field to extract more biologically relevant information from the burgeoning set of scRNA-seq datasets.