Tapabrata Maiti

This paper introduces a Bayesian Hierarchical Hypothesis Testing method for GWAS that improves power and fine-mapping resolution, leading to more discoveries in serum urate risk variant mapping.

This study benchmarks Bayesian and frequentist methods for differential gene expression analysis in dose-response scRNA-seq studies, finding that multi-group methods outperform two-group approaches.

The paper proposes a multiview graph learning method to reconstruct gene regulatory networks from single-cell RNA sequencing data, showing improved performance on synthetic and real datasets.

scSGL infers gene regulatory networks from single-cell RNA sequencing data by learning signed graphs that capture both activating and inhibitory gene relationships, while handling cell-cycle heterogeneity and dropouts.