Ruben Casanova

This paper introduces PCF-SiM, a framework for quantifying spatial organization in multiplexed imaging data, demonstrating its application in detecting tissue remodeling and spatial interactions in disease models.

This study shows SMAC mimetics overcome apoptotic resistance in ovarian cancer by targeting the MSLN-TNF alpha axis, enhancing cell death and synergizing with chemotherapy.

The paper develops statistical models for multiplexed imaging data to improve differential abundance testing and proposes a sampling strategy to enhance statistical power.

scROSHI identifies cell types without training data by using pre-existing gene sets and hierarchical relationships, outperforming methods in low-data and high-diversity scenarios.

SCIM matches single-cell measurements across technologies by learning a technology-invariant latent space and applying bipartite matching, achieving high accuracy in real-world datasets.