Data infrastructure for single-cell multiplex imaging in Bioconductor

项目摘要/摘要 了解组织中基因表达的空间格局是关系到人类健康的一个基本问题。 应用范围从识别细胞类型的空间组织到空间依赖的失调 与疾病相关的基因表达。多路成像(MI)等技术的进步提供了一种 丰富的数据来研究这些问题。来自这些平台的图像都经过了预处理，以生成 每个组织样本的表格式数据集，其中每行都是单独的，每列都包含特征 包括空间坐标和蛋白质/细胞信号表达。尽管有这些相似之处，但这些 由于现有的挑战，新用户和软件开发人员往往没有充分利用数据的潜力， 包括缺乏跨MI平台的标准化数据基础架构，无法实现健壮和模块化 下游分析。为了解决这一问题，我们建议为单细胞开发标准化的数据基础设施 以及生物导体中的空间多路成像数据，可扩展用于分析大型地图集规模的数据。此数据 基础设施将允许用户和开发人员快速高效地从HuBMAP中提取更深层次的见解 多路传输成像数据。

Project Summary/Abstract Understanding the spatial landscape of gene expression in tissues is a fundamental question for human health. Applications range from identifying the spatial organization of cell types to dysregulation of spatial-dependent gene expression associated with disease. Advances in technologies, such as multiplex imaging (MI), provide a wealth of data to investigate these questions. Images from these platforms are all preprocessed to produce a tabular dataset for each tissue sample where each row is an individual and each column contains characteristics of that cell including spatial coordinates and protein / cell signaling expression. Despite these similarities, these data often are not used to their full potential by new users and software developers due to existing challenges, including a lack of a standardized data infrastructure across MI platforms to enable robust and modular downstream analyses. To address this, we propose to develop standardized data infrastructure for single-cell and spatial multiplex imaging data in Bioconductor scalable for the analysis of large atlas-scale data. This data infrastructure will allow users and developers to quickly and efficiently extract deeper insights from HuBMAP multiplex imaging data.