Tusharkanti Ghosh

This paper introduces CytoKspace, a nonparametric method for detecting spatially variable genes, showing improved performance and scalability over existing approaches.

HetNetEX provides exact asymptotic inference for connectivity in heterogeneous biomedical knowledge graphs, outperforming XSwap in speed and accuracy.

cytoKernel assesses differential expression in single-cell data by using kernel embeddings to detect both aggregate and subtle variations, outperforming existing methods.

The paper identifies activated lymphocyte phenotypes in individuals at risk for rheumatoid arthritis using mass cytometry and develops an immunophenotype score to distinguish these individuals from controls.

The paper introduces FunSpace, a method using spatial entropy and functional principal component analysis to assess spatial heterogeneity in tumor microenvironments and its impact on survival.

DenVar analyzes multiplex imaging data to assess associations between functional marker densities and clinical outcomes without thresholding.

PaIRKAT improves testing power for pathway-phenotype associations by integrating graph structure into kernel regression, showing effectiveness in metabolomics data.