Gene set analysis of single cell genomics

单细胞基因组学的基因集分析

• 批准号: 10708043
• 负责人: Hildreth Frost
• 金额: $ 41万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-22 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10708043
• 关键词: Address; Algorithms; Bioinformatics; Biological; Cell Communication; Cell Separation; Cells; Cellular Assay; Chromatin; Complex; DNA Sequence; Data; Data Analyses; Genes; Genetic Transcription; Genomics; Immune; Immune response; Individual; Malignant Neoplasms; Measurement; Methods; Noise; Pathway Analysis; Pathway interactions; Phenotype; Population Heterogeneity; Proteins; Research Personnel; Signal Transduction; Statistical Data Interpretation; Structure; Techniques; Testing; Tissue Sample; Tissues; cell type; genome-wide; genomic data; innovation; insight; single cell analysis; tool; translational applications; tumor microenvironment

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.

项目摘要 单细胞测定的进展使得能够在全基因组范围内测量DNA序列、RNA和DNA片段。 表达，染色质可及性和蛋白质丰度的成千上万的细胞分离， 一个组织样本捕获或推断空间或时间信息的方法提供额外的 背景信息，以创建详细的，细胞水平的基因活性和功能的图片。这些冰毒 ods为研究人员提供了一个强有力的工具，用于识别分析组织中的细胞类型， 以及控制组织结构和功能的细胞间相互作用网络。虽然 这些技术彻底改变了复杂组织的研究， 单细胞测量的显著性意味着统计分析通常在大细胞水平上进行， 细胞群或细胞簇。虽然基于聚类的分析可以减轻稀疏性和噪声，但 结果反映了群集中平均像元的状态，这可能与许多像元非常不同 在不同的人群中。为了充分发挥单细胞分析的潜力，生物信息学方法， 需要能够准确表征单个细胞而不是细胞群的ODS。一个有希望 产生细胞水平估计值的方法是基因集测试或途径分析， 通过组合生物学中所有基因的测量，有效地捕获单个细胞的状态， ical途径。不幸的是，单细胞和大量基因组之间的统计学和生物学差异 数据使得使用现有的基因组测试方法具有挑战性，这些方法是为大块组织开发的， 单细胞数据。为了解决这一限制，我们将创建细胞级基因集的创新算法 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 潜力这些方法的翻译应用，以研究免疫细胞信号转导内的TU- 莫尔微环境将有助于验证我们的方法，并提供重要的见解免疫 对癌症的反应。