Debashis Ghosh

RKMR identifies predictive marker panels in spatial omics data using a scalable framework that integrates kernel modeling and spatial dependence.

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

CBKMR improves marker detection in omics data by capturing nonlinear gene-outcome relationships and interactions, outperforming existing methods in accuracy and uncertainty quantification.

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

SpaceANOVA analyzes spatial co-occurrence of cell types in multiplex imaging data across tissue or disease groups using point process and functional ANOVA, revealing novel insights in colorectal cancer precursor lesions.

The paper describes the CCPM Biobank, a dual-purpose genomic resource linking health records and genomic data to support research and clinical applications.

SMASH identifies spatially variable genes in spatial transcriptomics data with improved statistical power and computational efficiency.

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.

The paper introduces a platform-independent preprocessing framework for multiplex tissue imaging data to improve cross-platform consistency and reliability.

The paper introduces MIAMI, a method using mutual information to analyze protein co-expression in cancer datasets, finding associations between marker co-expression and clinical outcomes.

The paper introduces a spatial and functional analytic approach to model the impact of tumor-stromal cell interactions on survival in cancer patients using imaging data.

The paper introduces CODAK, a method for analyzing compositional mass cytometry data to test associations with predictors, showing good performance on small samples and applied to SLE data.

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.

The paper introduces a kernel method to dissect genetic signals in high-dimensional phenotypes, showing it controls type I error and identifies direct effects in GWAS data.

This paper introduces a new rare-variant association test that incorporates family history information, showing improved performance over standard methods in simulated data and a study of cleft lip and palate.

This paper introduces a method to improve genetic association testing for psychiatric symptoms by analyzing multivariate questionnaire data instead of collapsing it into a single score, showing increased power and efficiency.

This paper introduces a kernel-based regression method using resistance perturbation distance to analyze fMRI connectivity maps and their association with biological phenotypes.

TCIApathfinder provides an R client for accessing TCIA's REST API to explore and download cancer imaging data.

The paper introduces SASE-mapper to detect accelerated somatic evolution in cancer genomes and finds recurrent SASE in regulatory regions across multiple cohorts.