Sumit Mukherjee

This paper shows that genome-wide association studies on ratio traits can be misleading, as associations may be driven by the denominator rather than the numerator, and proposes a method to correct for collider bias.

This study shows that machine learning-derived phenotypes for GWAS can lead to false discoveries depending on their causal relationship with the target phenotype and environment.

EmbedGEM evaluates embeddings for genetic discovery by assessing heritability and disease relevance using polygenic risk scores and genome-wide association statistics.

The paper estimates molecular pseudotime for Alzheimer's disease progression using RNA-Seq data from human brain samples, showing strong concordance with pathological markers and cognitive diagnosis.

UNCURL-App provides an interactive tool for scRNA-seq analysis, integrating cell type databases and allowing user-driven re-analysis of clusters and pathways.

The paper develops a multi-view evidence aggregation method to identify and rank potential driver genes of Alzheimer's Disease using RNA-Seq data and GWAS summary statistics.

Coexpression meta-analysis of 2114 human brain RNA-seq samples across the AMP-AD consortium identifies five consensus Alzheimer's disease transcriptional clusters, including two sex-heterogeneous clusters and a previously unreported proteostasis cluster.

This study investigates how microRNA-mediated incoherent feedforward loops suppress extrinsic noise, showing that translational inhibition expands noise rejection regimes and can buffer or amplify gene product fluctuations depending on time scales.

This paper introduces PIPER, a method to identify gene network perturbations in single-cell RNA-seq data, showing it outperforms existing methods on simulated data and identifies known regulators in real datasets.

UNCURL improves scRNA-seq preprocessing by incorporating prior knowledge and scaling to large datasets, enhancing clustering and lineage estimation performance.

The paper introduces SPLiT-seq, a scalable scRNA-seq method, and applies it to analyze 109,069 cells from a mouse brain, identifying 13 main cell populations and developmental substructures.