Methods for improved detection of activated molecular pathways in cancer

项目总结/摘要 通过RNA测序（RNA-seq）定量基因表达来研究肿瘤已被证明至关重要 阐明其活跃的生物学途径和过程，它们与正常组织的不同之处， 他们如何成为治疗的目标此外，新的单细胞RNA-seq（scRNA-seq） 技术开始揭示肿瘤的异质性， 分辨率从表达数据中获得途径活性的知识需要应用 方法，如基因集富集分析（GSEA），这是一个社区标准， 评估途径、过程和表型的协调上调或下调 由基因组或“基因集”表示。由于GSEA要求高质量和良好的注释 为了进行稳健的分析，Mesirov实验室维护并免费分发了分子生物学和基因组。 签名数据库（MSigDB），其中包含多个基因集的集合，以伴随我们的 GSEA软件。理想情况下，该数据库将由连贯的基因集组成，即， 成员基因显示协同上调或协同下调，并特异性地指示 与特定细胞类型或疾病相关的特定途径或过程的激活或抑制 表型。然而，由于在MSigDB中收集一些基因集的方式，例如，策展 从科学出版物或从经典途径数据库中提取， 缺乏连贯性。此外，GSEA实现的用户开始输入新的 scRNA-seq数据。然而，我们已经发现了统计问题所产生的稀疏 scRNA-seq数据使得标准GSEA结果无法解释。为了解决这些问题，我们 提出以下目标。 目标1：我们将为MSigDB中的基因集开发一种数据驱动的细化方法。 我们的方法将利用大规模的表达数据集和蛋白质-蛋白质 交互网络使用现有的基因集作为出发点，以构建完善的基因集。 目标2：我们将使用目标1中的细化方法来组装一个新的Hallmark系列 用于GSEA的基因组。 目标3：我们将开发和验证一种途径富集检测方法， 解释了scRNA-seq的稀疏性。 在完成这些目标之后，我们将发布一个新的，免费提供的 基因集，使更强大的GSEA以及一种新的方法，将允许这些新的，或任何， 用于测试scRNA-seq中富集的基因组。

PROJECT SUMMARY/ABSTRACT Studying tumors by quantifying gene expression via RNA-sequencing (RNA-seq) has proven crucial to elucidating their active biological pathways and processes, how they differ from normal tissue, and how they might be targeted for therapy. Furthermore, new single cell RNA-seq (scRNA-seq) techniques are beginning to uncover the heterogeneity of tumors by profiling them at single cell resolution. Deriving knowledge of pathway activity from expression data requires the application of methods such as Gene Set Enrichment Analysis (GSEA), which is a community standard for assessing the coordinate up- or down-regulation of pathways, processes, and phenotypes represented by groups of genes or ‘gene sets’. As GSEA requires high-quality and well-annotated gene sets for a robust analysis, the Mesirov lab maintains and freely distributes the Molecular Signatures Database (MSigDB), which contains multiple collections of gene sets to accompany our GSEA software. Ideally, this database would consist of coherent gene sets, that is, sets whose member genes show coordinate up-regulation or coordinate down-regulation and specifically indicate activation or repression of a specific pathway or process relevant to a particular cell type or disease phenotype. However, due to the manner of collection of some gene sets in MSigDB, e.g., curation from scientific publications or extraction from canonical pathway databases, some of the gene sets lack coherence. In addition, users of our GSEA implementations are beginning to input new scRNA-seq data. However, we have identified statistical problems arising from the sparsity of scRNA-seq data that make standard GSEA results uninterpretable. To address these concerns, we propose the following aims. Aim 1: We will develop a data-driven refinement approach for the gene sets in the MSigDB. Our approach will leverage large-scale compendia of expression datasets and protein-protein interaction networks to use existing gene sets as starting points to construct refined gene sets. Aim 2: We will use the refinement method from Aim 1 to assemble a new Hallmark collection of refined gene sets for use in GSEA. Aim 3: We will develop and validate an approach to pathway enrichment detection that accounts for the sparsity of scRNA-seq. Following the completion of these aims, we will have released a new, freely available collection of gene sets that enable more robust GSEA as well as a new method which will allow these new, or any, gene sets to be used to test for enrichment in scRNA-seq.