Barbara Elizabeth Engelhardt

This paper introduces a robust nonlinear manifold model for scRNA-seq data to enable visualization and analysis of raw, unfiltered gene counts.

netNMF-sc improves scRNA-seq analysis by imputing dropout events and reducing dimensionality using gene-gene interactions, outperforming existing methods at high dropout rates.

This paper introduces a Bayesian nonparametric semi-supervised model for integrating multiple single-cell experiments, showing its effectiveness on simulated and real data.

GTEx v8 maps expression and splicing QTLs in 49 tissues from 838 donors: cis-eQTLs cover 94.7% of protein-coding genes, and cross-tissue QTL sharing mirrors cell-type composition.

GT-TS is a computational method that optimizes cell type discovery in multi-tissue scRNA-seq by iteratively planning data collection.

The paper develops S-PrediXcan to compute gene expression associations from GWAS summary data and applies it to 44 GTEx tissues and 100+ phenotypes, finding tissue-specific effects and colocalized associations.

This paper introduces a nonparametric model, DPGP, to cluster gene expression time series data and demonstrates its effectiveness using simulated and real datasets.

The paper constructs co-expression networks that integrate transcription and splicing regulation across tissues, identifying shared and tissue-specific regulatory hubs and genetic variants impacting gene expression.

The paper identifies trans-eQTLs in multiple human tissues and shows their tissue-specific effects and association with disease.