Data infrastructure for single-cell multiplex imaging in Bioconductor

Johns Hopkins University, Baltimore MD

Investigators

Linked publications & trials

Abstract

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.