Alex C. Soupir

The paper introduces multiScaleAC, a method to analyze cell-cell interactions across spatial scales using a modified Moran's I with varying kernel bandwidths, showing improved statistical power and reduced bias.

The paper develops a functional data analysis framework to model spatial clustering in ovarian cancer, identifying prognostic T cell patterns beyond immune cell abundance.

The paper identifies a transcriptional transition state in ccRCC cells that drives sarcomatoid transformation and enhances immunotherapy response via macrophage and T-cell interactions.

The paper provides exact analytical solutions for the mean and variance of the Nearest Neighbor G(r) spatial metric under complete spatial randomness, avoiding computationally expensive permutations.

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

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 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 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.

curatedPCaData harmonizes prostate cancer datasets to enable meta-analysis and clinical insights across multiple cohorts.

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