
Hourglass analytical framework emerges, offering an open-access solution for the visualization, exploration, and statistical evaluation of complex multiparametric datasets. With a focus on accommodating tissue and clinical diversity, Hourglass systematically categorizes observations at various levels, revealing intricate details within patient subgroups. Its application to a substantial bioimaging dataset revealed previously undisclosed insights, including a novel sexual dimorphism within the IL-6/STAT3-linked intratumoral T-cell response in human pancreatic cancer. Hourglass empowers users, regardless of computational proficiency, to extract valuable knowledge from intricate bioimaging datasets, unlocking hidden insights within heterogeneous tissues at both sample and patient levels.
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