Satwik Acharyya

The paper identifies conserved transcriptional programs in pancreatic cancer liver metastases and shows how they interact with spatial immune microenvironments to drive metastatic progression.

This paper introduces SCR, a Bayesian framework for estimating spatially-resolved gene co-expression networks to identify spatially co-expressed genes as potential biomarkers and therapeutic targets.

This paper introduces a Bayesian multi-resolution spatial graphical regression framework to infer spatially varying gene networks from hierarchical spatial transcriptomics data, showing improved network recovery and identifying key regulatory hubs in kidney cancer.

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

This paper introduces a spatial Graphical Regression model to analyze spatial transcriptomics data and identifies novel gene interactions in cancer.

CAFE is a web app for analyzing and visualizing high-dimensional spectral flow cytometry data, enabling interactive exploration without coding expertise.

This paper introduces GraphR, a Bayesian method for probabilistic graphical modeling under sample heterogeneity, showing improved performance in network estimation and computational efficiency.

The paper develops a semi-parametric Beta regression model for longitudinal proportion data with missing-at-random responses, using a monotone single index transform and Bayesian inference.

SpaceX identifies gene co-expression networks in spatial transcriptomics data, revealing spatially regulated genes in mouse hypothalamus and human breast cancer.

This paper introduces a Bayesian framework to improve drug response prediction in cancer by integrating pharmacogenomic data across patients and cell lines.