A statistical framework for disease classification with scRNA-Seq data

使用 scRNA-Seq 数据进行疾病分类的统计框架

• 批准号: 10707488
• 负责人: Elizabeth Purdom
• 金额: $ 30.25万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10707488
• 关键词: Address; Area; Biological; Cells; Complement; Computer software; Computing Methodologies; Data; Data Analyses; Data Reporting; Detection; Development; Diagnosis; Disease; Eating; Evaluation; Foundations; Future; Genes; Goals; Health; Heterozygote; Human; Individual; Mathematics; Measurement; Methodology; Methods; Modeling; Outcome; Output; Partner in relationship; Patient-Focused Outcomes; Patients; Phenotype; Population; Population Study; Prevention; Probability; Procedures; Research; Research Personnel; Role; Statistical Methods; Stream; Techniques; Testing; Therapeutic; Tweens; Variant; Visualization software; Work; advanced disease; analytical tool; bioinformatics pipeline; biological systems; biomarker identification; cell type; direct application; disease classification; disease diagnosis; human disease; improved; individual patient; insight; mRNA Expression; method development; novel; novel marker; open source; patient biomarkers; patient population; patient variability; potential biomarker; predictive modeling; programs; single cell mRNA sequencing; single cell sequencing; single-cell RNA sequencing; tool

Project Summary Background Single-cell sequencing data has enormous potential to improve our understanding of human health, with direct applications in the areas of diagnosis and therapeutic selection. Single- cell sequencing of mRNA expression levels (scRNA-Seq) initially focused on understanding fun- damental biological systems at the single-cell level, but there is an increasing emphasis on using scRNA-Seq to understand the role of single-cell variability on human health outcomes. While the exploration of single-cell human variability and its relationship to disease is advancing, the cor- responding statistical methodology to handle this type of data at the human population level lags behind. Project Objectives Broadly, the long-term goal of this proposal is a coherent methodological framework for the analysis of the effect of single-cell variability on patient phenotypes. This pro- posal considers the setting of population scRNA-Seq studies, where scRNA-Seq data is collected from many patients representing populations with differing health outcomes. The proposed re- search consists of the development and evaluation of statistical methodologies for these kinds of scRNA-Seq population studies. The methodology developed by this proposal will fill a critical gap, helping to unlock the potential of scRNA-Seq data for improving human health. Project Methods The proposed research program focuses on three specific aims that target the most common analysis needs in scRNA-Seq population studies. Aim 1: Patient-level represen- tation for scRNA-Seq data. This Aim will develop a summary representation of the scRNA-Seq profile of a patient and create statistical methods that allow comparisons of this summary profile between different patient populations. Aim 2: Predicting patient phenotypes based on scRNA-Seq data. This aim will develop models that can predict health phenotypes based on the scRNA-Seq measurements on a patient. Aim 3: Identifying cell-level and gene-level biomarkers for patient phe- notypes. The methods developed in this aim will allow for identifying genes and cell populations that differ at the single-cell level between patient populations. The biomarkers identified from these methods will generate testable hypotheses for future exploration of the mechanistic relationship between single-cell variability and patient outcome.

项目摘要 背景单细胞测序数据具有巨大的潜力，可以提高我们对 人类健康，直接应用于诊断和治疗选择领域。单- mRNA表达水平的细胞测序（scRNA-Seq）最初专注于了解fun， 在单细胞水平上破坏生物系统，但越来越强调使用 scRNA-Seq了解单细胞变异性对人类健康结果的作用。而 探索单细胞人类变异性及其与疾病的关系正在推进，核心 在人口水平上处理这类数据的相应统计方法滞后 后面 项目目标总的来说，本提案的长期目标是采用一种连贯的方法， 该框架用于分析单细胞变异性对患者表型的影响。这个亲- 研究中心考虑了人群scRNA-Seq研究的设置，其中收集了scRNA-Seq数据 来自许多代表不同健康结果人群的患者。拟议的重新- 搜索包括开发和评估这些类型的统计方法， scRNA-Seq群体研究。本提案制定的方法将填补一个关键空白， 帮助释放scRNA-Seq数据改善人类健康的潜力。 项目方法拟议的研究计划侧重于三个具体目标，针对 scRNA-Seq群体研究中最常见的分析需求。目标1：患者水平代表- 用于scRNA-Seq数据。本目标将开发scRNA-Seq的概要表示， 并创建统计方法，以比较该摘要特征 在不同的患者群体之间。目的2：基于scRNA-Seq预测患者表型 数据该目标将开发可以基于scRNA-Seq预测健康表型的模型。 对患者进行测量。目的3：确定患者phe的细胞水平和基因水平生物标志物。 无名氏为此目的开发的方法将允许识别基因和细胞群 在单细胞水平上的差异。从这些生物标志物中鉴定出艾德， 方法将产生可检验的假设，用于将来探索机械关系 单细胞变异性和患者预后之间的关系。