Statistical analysis of multiple regions-of-interest in multiplexed spatial proteomics data.

ABSTRACT: Multiplexed spatial proteomics reveals the spatial organization of cells in tumors, which is associated with important clinical outcomes such as survival and treatment response. This spatial organization is often summarized using spatial summary statistics, including Ripley's K and Besag's L. However, if multiple regions of the same tumor are imaged, it is unclear how to synthesize the relationship with a single patient-level endpoint. We evaluate extant approaches for accommodating multiple images within the context of associating summary statistics with outcomes. First, we consider averaging-based approaches wherein multiple summaries for a single sample are combined in a weighted mean. We then propose a novel class of ensemble testing approaches in which we simulate random weights used

SUBMITTER: Samorodnitsky S 

PROVIDER: S-EPMC11491162 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature