Oscar E. Ospina

iModMix identifies novel multi-omics associations by constructing data-driven modules without relying on biological annotations.

scSpatialSIM simulates spatial single-cell molecular data to evaluate spatial clustering methods, finding Ripleys K(r) best detects clustering across scenarios.

The paper uses spatial transcriptomics to identify increased spatial coupling of integrin and collagen IV in immunoresistant clear cell renal cell carcinoma tumors, suggesting integrin as a therapeutic target.

spatialGE simplifies spatial transcriptomics analysis by providing a user-friendly web interface for non-programmers, enabling tasks like quality control, domain detection, and spatial analysis.

This study uses spatial transcriptomics to identify a tumor-promoting role of meningeal stroma in melanoma leptomeningeal disease and shows that targeting SERPINA3 or its downstream pathway can restore drug sensitivity.

This study applies spatial mixed models to differential gene expression analysis in spatial transcriptomics, showing they reduce type-I error and improve model fit compared to non-spatial methods.

The paper develops the first turtle-derived hepatic organoids from multiple species, enabling in vitro studies of unique adaptations and biomedical applications.

This study identifies branched-chain keto acid accumulation in leptomeningeal disease CSF, showing immune suppression and neurodegeneration, and shows that reducing BCKA improves outcomes in mouse models.

This study cultivates and characterizes new canine organoid lines from endometrium and pancreas, comparing them with existing lines using bulk RNA-seq to identify cell types and differences between two genetically related canines.

The paper introduces spatialGE, a software tool for quantifying and visualizing tumor microenvironment heterogeneity using spatial transcriptomics data.