Novel Statistical Methods for Multiscale Analysis of Single-cell Transcriptomes

单细胞转录组多尺度分析的新统计方法

• 批准号: 10667206
• 负责人: Wei Vivian Li
• 金额: $ 38.88万
• 依托单位: UNIVERSITY OF CALIFORNIA RIVERSIDE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10667206
• 关键词: Address; Bioinformatics; Biological; Biology; Biotechnology; Cancer Biology; Cancer Institute of New Jersey; Cells; Computer software; Computing Methodologies; Data; Data Science; Development; Disease; Disease Progression; Foundations; Gene Expression; Genes; Genetic Transcription; Goals; Health; Institutes; Intestines; Knowledge; Measurement; Methods; Modeling; Molecular Profiling; Neurobiology; Pathway interactions; Patients; Polyadenylation; Population; Post-Transcriptional Regulation; RNA; Regulator Genes; Research; Resolution; Sampling; Statistical Methods; Statistical Models; Structure; Tissues; Transcript; Transcriptional Regulation; Work; bioinformatics tool; cell type; computerized tools; denoising; design; experimental study; gene interaction; genomic data; high dimensionality; human disease; improved; innovation; insight; novel; programs; single cell analysis; single-cell RNA sequencing; transcriptome; user friendly software

Project Summary/Abstract Single-cell RNA sequencing (scRNA-seq) is currently at the forefront of biotechnological innovation. scRNA-seq experiments enable gene expression measurement at a single-cell resolution, and provide an opportunity to characterize the molecular signatures of diverse cell types, states, and structures in tissue development and disease progression. However, it remains a substantive challenge to construct a comprehensive view of single- cell transcriptomes in health and disease, due to the knowledge gap in properly modeling the high-dimensional, sparse, and noisy scRNA-seq data. While the development of new data science methods, including our recent work, has facilitated the design and analysis in scRNA-seq studies to identify and annotate distinct cell populations, there is a critical need for computational methods that can accurately evaluate biological hypotheses for these diverse cell populations. To address this knowledge gap and critical need and thereby enable a systematic understanding of transcriptional and post-transcriptional mechanisms across biological scales (from cells to genes to RNA molecules), the objective of our MIRA research program is to develop novel statistical methods and bioinformatics software for multiscale analysis of single-cell transcriptomes. We will pursue three parallel but complementary research directions: (1) to develop novel statistical methods for quantifying and comparing gene regulatory associations from single-cell gene expression data; (2) to develop the first statistically principled methods for identifying, quantifying, and comparing alternative polyadenylation usage from 3’-end scRNA-seq data; and (3) to develop a novel statistical model for jointly analyzing and comparing scRNA-seq data from heterogeneous biological samples, such as multiple patients, developmental stages, or related species. The proposed research will be built on the foundations of our recent studies in developing interpretable statistical methods and user-friendly software for quantifying, denoising, integrating, and comparing genomic data at various biological scales. Throughout the program, we will work closely with experimental biologists at Rutgers Cancer Institute of New Jersey and Wistar Institute, and use our proposed methods to identify and study transcriptional mechanisms in intestinal biology, neurobiology, and cancer biology. Together, this concerted effort will provide efficient and broadly applicable statistical and bioinformatics tools for generating substantial insights into identifying key cells, pathways, gene interactions, and RNA transcripts associated with various biological contexts, including human disease. The proposed program also aligns with my team’s long-term goal to develop a statistically principled understanding of transcriptional and post- transcriptional regulation in single cells, thus improving our ability to define, interpret, and predict cellular commitment and functionality in health and disease.

项目总结/摘要 单细胞RNA测序（scRNA-seq）目前处于生物技术创新的最前沿。scRNA-seq 实验能够以单细胞分辨率测量基因表达，并提供了一个机会， 表征组织发育中不同细胞类型、状态和结构的分子特征， 疾病进展。然而，构建一个单一的全面观点仍然是一个实质性的挑战， 细胞转录组在健康和疾病，由于在正确建模的高维知识差距， 稀疏且有噪声的scRNA-seq数据。虽然新的数据科学方法的发展，包括我们最近的 这项工作促进了scRNA-seq研究的设计和分析，以识别和注释不同的细胞 人口，有一个计算方法，可以准确地评估生物的迫切需要 这些不同的细胞群体的假设。为了解决这一知识差距和关键需求， 使人们能够系统地理解生物学中的转录和转录后机制， 规模（从细胞到基因到RNA分子），我们的MIRA研究计划的目标是开发新的 用于单细胞转录组的多尺度分析的统计方法和生物信息学软件。我们将 追求三个平行但互补的研究方向：（1）开发新的统计方法， 从单细胞基因表达数据定量和比较基因调控关联;（2）开发 第一个统计学原理的方法，用于识别，定量，和比较替代聚腺苷酸化， 使用3 '端scRNA-seq数据;和（3）开发一种新的统计模型， 比较来自异质生物样品的scRNA-seq数据，例如多个患者，发育 或相关物种。拟议的研究将建立在我们最近研究的基础上， 开发可解释的统计方法和用户友好的软件，用于量化，去噪，整合， 并在不同的生物尺度上比较基因组数据。在整个项目中，我们将与 实验生物学家在罗格斯癌症研究所的新泽西和Wistar研究所，并使用我们的建议， 方法，以确定和研究在肠道生物学，神经生物学和癌症生物学的转录机制。 总之，这一协调一致的努力将提供有效和广泛适用的统计和生物信息学工具， 对识别关键细胞、途径、基因相互作用和RNA转录本产生了实质性的见解 与包括人类疾病在内的各种生物学背景相关。拟议的方案还符合 我的团队的长期目标是对转录和转录后的基因组进行统计学原理性的理解， 在单细胞中的转录调控，从而提高我们的能力，定义，解释和预测细胞 在健康和疾病方面的承诺和功能。