Abhishek Sarkar

This paper introduces GoM DE, a differential expression analysis method that allows cells to have partial membership in multiple groups, improving the interpretation of structure in single-cell sequencing data.

The paper introduces a flexible empirical Bayes method for multivariate multiple regression to improve prediction of multi-tissue gene expression from genotypes.

The paper clarifies confusion in scRNA-seq analysis by distinguishing between measurement error and true gene expression, advocating for a Poisson measurement model.

The paper identifies dynamic eQTLs in cardiomyocytes under hypoxic stress, showing how genetic variation affects gene expression differently across conditions.

This study compares gene expression variability and eQTLs in humans and chimpanzees, finding shared genetic and non-genetic determinants of inter-individual expression differences.

SuSiE writes sparse regression coefficients as a sum of single-effect vectors; its IBSS algorithm returns a credible set per effect and outperformed existing fine-mapping methods in numerical experiments.

This study identifies variance QTLs (vQTLs) in human iPSCs using scRNA-seq, finding that genetic variants affect gene expression variance but with smaller phenotypic impact than mean expression QTLs.

This paper introduces a Bayesian method to identify causal genes in Alzheimer’s disease by distinguishing mediation from pleiotropy using summary statistics, finding 206 causal genes.

This paper improves TWAS power by incorporating prediction uncertainty, finding 45 TWAS genes with 17 novel associations and replicating only 2 of 40 previously reported genes.

The paper introduces fQTL, a multi-tissue model for eQTL mapping and gene expression prediction, improving TWAS results for Alzheimer's and schizophrenia.