Gene set analysis of single cell genomics

项目总结 单细胞分析的进步使DNA序列、RNA的全基因组测量成为可能 分离的数万个细胞的表达、染色质可及性和蛋白质丰度 一个单一的组织样本。捕捉或推断空间或时间信息的方法提供了额外的 背景信息，以创建详细的，细胞水平的基因活动和功能的图景。这些冰毒- Ods为研究人员提供了一个强大的工具，用于识别所分析组织中的细胞类型，即表型 这些细胞以及控制组织结构和功能的细胞-细胞相互作用网络。虽然 这些技术彻底改变了复杂组织的研究，显著的稀疏性和噪声 单细胞测量意味着统计分析通常在大细胞水平上执行 一组或一群细胞。虽然基于聚类的分析可以减少稀疏性和噪声，但重新 结果反映了集群中平均单元的状态，这可能与许多单元非常不同 在不同的种群中。为了充分发挥单细胞图谱的潜力，生物信息学方法--生物信息学方法-- 需要能够准确描述单个细胞而不是细胞组的ods。一件有希望的事 生成细胞水平估计的方法是基因集测试或路径分析，这可以比 通过组合Biolog中所有基因的测量结果，有效地捕获单个细胞的状态- 精神路径。不幸的是，单细胞基因组和整体基因组之间的统计和生物学差异 数据使使用现有的基因集测试方法变得具有挑战性，这些方法是为大宗组织开发的， 对单个单元格数据。为了解决这一限制，我们将为细胞级别的基因集创建创新的算法 测试，并将使用这些技术来支持细胞类型、表型和相互作用的估计 潜力。这些方法在研究T细胞内免疫细胞信号转导方面的应用。 MOR微环境将有助于验证我们的方法，并提供对免疫的重要见解 对癌症的反应。

PROJECT SUMMARY Advances in single cell assays have enabled the genome-wide measurement of DNA sequence, RNA expression, chromatin accessibility and protein abundance for tens-of-thousands of cells isolated from a single tissue sample. Methods that capture or infer spatial or temporal information provide additional contextual information to create a detailed, cell-level picture of gene activity and function. These meth- ods give researchers a powerful tool for identifying the cell types in the analyzed tissue, the phenotype of those cells and the network of cell-cell interactions that control tissue structure and function. Although these techniques have revolutionized the study of complex tissues, the significant sparsity and noise of single cell measurements means that statistical analysis is typically performed at the level of large groups or clusters of cells. Although a cluster-based analysis can mitigate sparsity and noise, the re- sults reflect the state of the average cell in the cluster, which may be quite dissimilar from many cells in heterogeneous populations. To fully realize the potential of single cell profiling, bioinformatics meth- ods are needed that can accurately characterize individual cells rather than cell groups. One promising approach for generating cell-level estimates is gene set testing or pathway analysis, which can more effectively capture the state of individual cells by combining the measurements for all genes in a biolog- ical pathway. Unfortunately, statistical and biological differences between single cell and bulk genomic data make it challenging to use existing gene set testing methods, that were developed for bulk tissue, on single cell data. To address this limitation, we will create innovative algorithms for cell-level gene set testing and will use these techniques to support the estimation of cell type, phenotype and interaction potential. The translational application of these methods to study immune cell signaling within the tu- mor microenvironment will help validate our approach and provide important insights into the immune response to cancer.