Unsupervised Statistical Methods for Data-driven Analyses in Spatially Resolved Transcriptomics Data

空间分辨转录组数据中数据驱动分析的无监督统计方法

• 批准号: 10556351
• 负责人: Lukas Martin Weber
• 金额: $ 4.68万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10556351
• 关键词: Address; Adopted; Award; Awareness; Benchmarking; Biological; Cells; Classification; Communities; Complex; Computer software; Data; Data Analyses; Data Set; Data Storage and Retrieval; Development; Diagnosis; Dimensions; Disease; Evaluation; Expression Profiling; Extramural Activities; Faculty; Funding; Gene Expression; Gene Expression Profile; Generations; Genes; Genetic Transcription; Goals; Health; Heterogeneity; Human; Individual; Institution; Knowledge; Machine Learning; Measurement; Measures; Mentors; Methodology; Methods; Molecular; Nature; Outcome; Pathway interactions; Pattern; Phase; Population; Positioning Attribute; Procedures; Prognosis; Property; Quality Control; RNA; Research; Research Personnel; Resolution; Resources; Signal Transduction; Specificity; Spottings; Standardization; Statistical Methods; Systematic Bias; Techniques; Technology; Time; Tissue Microarray; Tissues; Training; Transcript; Variant; Weight; Work; career; cell type; complex data; data infrastructure; data resource; data standards; density; design; feature selection; improved; insight; novel; open source; precision medicine; preservation; single-cell RNA sequencing; statistics; tenure track; tool; transcriptome; transcriptomic profiling; transcriptomics; treatment response; two-dimensional

Project Summary/Abstract Recently developed spatially resolved transcriptomics (ST) technologies measure transcriptome-wide gene expression at a near-single-cell, single-cell, or sub-cellular resolution in intact tissue, preserving the spatial organization of complex tissues. These technologies build upon widely-adopted single-cell RNA sequencing (scRNA-seq) technologies by adding spatial coordinates to the transcriptome-wide gene expression measurements, thus enabling an understanding of how the spatial organization of cells in complex tissues influences function, disease initiation, progression, and therapeutic response in human health and disease. However, these technologies also present new statistical and computational challenges, which need to be addressed to accurately interpret this complex data. While initial studies applying these tools have reused data analysis methods and data storage techniques designed for scRNA-seq, unfortunately these approaches largely ignore spatial information. Furthermore, existing methodologies for ST data rely on external information such as marker genes or reference cell types, potentially leading to systematic errors and biased results during preprocessing, feature selection, classification of spatially resolved cell types, and differential discovery. There do not yet exist robust and accurate preprocessing and unsupervised statistical methodologies to investigate ST data in a data-driven manner. The overall goals of this K99/R00 Pathway to Independence Award proposal are to request support to address this fundamental gap in statistical methodology to develop spatially-aware (1) methods for preprocessing, (2) unsupervised methods for spatially resolved clustering and differential discovery between conditions, and (3) data infrastructure and benchmarking resources to standardize the storage and access of ST data. These proposed methods will lead to an improved understanding of health and disease mechanisms. This proposal will provide the training, mentoring, and professional development to accomplish my research goals and transition to a tenure track faculty position at a research institution with independent extramural funding. As the demand for ST technologies grows, in particular now that it has been highlighted as the Nature Methods 2020 Method of the Year, these urgently needed statistical methods and open-source software proposed in this project will enable ST technologies to transform precision medicine through novel biological insights relating to spatial properties of cell populations and gene expression in healthy and diseased tissues. At the completion of this award, I will become part of a new generation of researchers, proficient in spatial statistics, machine learning, and spatial transcriptomics technologies, enabling me to work closely with biomedical researchers spatially profiling the transcriptomes of complex tissues.

项目摘要/摘要 最近开发了空间分辨的转录组学（ST）技术测量整个转录组基因 在完整组织中的近节单细胞，单细胞或亚细胞分辨率上的表达，保留空间 复杂组织的组织。这些技术以广泛的单细胞RNA测序为基础 （SCRNA-SEQ）技术通过将空间坐标添加到转录组的基因表达中 测量值，从而使能够了解复杂组织中细胞的空间组织 影响人类健康和疾病的功能，疾病的启动，进展和治疗反应。 但是，这些技术还提出了新的统计和计算挑战，需要 被解决以准确解释此复杂数据。在使用这些工具的初步研究重复使用的同时 不幸的是，这些方法是为SCRNA-SEQ设计的数据分析方法和数据存储技术 在很大程度上忽略了空间信息。此外，现有的ST数据方法依赖于外部信息 例如标记基因或参考细胞类型，可能导致系统错误和在 预处理，特征选择，空间分辨的细胞类型的分类以及差异发现。那里 尚未存在鲁棒，准确的预处理和无监督的统计方法来研究 以数据驱动方式进行数据数据。这项K99/R00途径的总体目标是独立奖奖提案 请求支持以解决统计方法中的这一基本差距，以发展空间意识（1） 预处理的方法，（2）无监督的方法用于空间分辨的聚类和差异 条件之间的发现和（3）数据基础结构和基准资源，以标准化 存储和访问ST数据。这些提出的方法将导致对健康的了解，并 疾病机制。 该建议将提供培训，指导和专业发展，以实现我的 研究目标并过渡到具有独立的研究机构的任期教师职位 壁外资金。随着对ST技术的需求的增长，尤其是现在，它已被强调为 自然方法2020年度方法，这些急需的统计方法和开源方法 该项目中提出的软件将使ST技术能够通过新颖 与细胞群体的空间特性和健康和患病中基因表达有关的生物学见解 组织。该奖项完成后，我将成为新一代研究人员的一部分，熟练 空间统计，机器学习和空间转录组技术，使我能够与 生物医学研究人员在空间上分析了复杂组织的转录组。