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

项目总结/摘要 最近开发的空间分辨转录组学（ST）技术测量转录组范围的基因 在完整组织中以近单细胞、单细胞或亚细胞分辨率表达，保留了空间结构。 复杂组织的结构。这些技术建立在广泛采用的单细胞RNA测序的基础上 scRNA-seq技术通过在转录组范围的基因表达中添加空间坐标 测量，从而能够理解复杂组织中细胞的空间组织 影响人类健康和疾病中的功能、疾病起始、进展和治疗反应。 然而，这些技术也提出了新的统计和计算挑战，需要 准确解释这些复杂的数据。虽然应用这些工具的初步研究已经重新使用了 为scRNA-seq设计的数据分析方法和数据存储技术，不幸的是，这些方法 基本上忽略了空间信息。此外，ST数据的现有方法依赖于外部信息 例如标记基因或参考细胞类型，可能导致系统误差和偏倚结果， 预处理、特征选择、空间分辨细胞类型的分类和差异发现。那里 目前还不存在强大而准确的预处理和无监督的统计方法来调查 ST数据以数据驱动的方式。这个K99/R 00独立之路奖提案的总体目标 要求提供支持，以解决统计方法方面的这一根本差距，从而发展空间意识（1） 预处理方法，（2）空间分辨聚类和差分的无监督方法 条件之间的发现，以及（3）数据基础设施和基准资源，以标准化 ST数据存储和访问。这些建议的方法将导致更好地了解健康和 疾病机制。 本提案将提供培训、指导和专业发展，以完成我的 研究目标和过渡到终身教职的研究机构与独立的教师职位 校外资助随着对ST技术需求的增长，特别是现在它已被强调为 2020年自然方法年度方法，这些迫切需要的统计方法和开源 该项目中提出的软件将使ST技术能够通过新颖的方式改变精准医学。 与健康和患病的细胞群和基因表达的空间特性有关的生物学见解 组织中在完成这个奖项后，我将成为新一代研究人员的一部分，精通 空间统计，机器学习和空间转录组学技术，使我能够与 生物医学研究人员对复杂组织的转录组进行空间分析。

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.