Paul Hoffman

ANEVA-h improves detection of rare regulatory variants by analyzing haplotype-level ASE data, increasing testable genes and reducing false positives in GTEx and clinical cohorts.

Dictionary learning enables assumption-free cross-modality single-cell mapping via multi-omic bridge datasets and scales single-modality integration to millions of cells in under an hour via atomic sketching.

The paper uses long-read RNA-seq data to identify new transcripts and develop a tool to analyze genetic effects on transcript structure, revealing insights into splicing regulation and variant interpretation.

Weighted-nearest neighbor (WNN) analysis in Seurat v4 learns per-cell modality weights to define cell states, enabling a 228-antibody human PBMC CITE-seq reference for RNA-only query mapping.

EdiTyper analyzes targeted sequencing data from genome editing experiments to detect mutations and indels with high accuracy and scalability.

ANEVA quantifies genetic regulatory variation using allelic expression data, improving transcriptome analysis and aiding in rare disease diagnosis.

The paper identifies genetic loci associated with low temperature and drought tolerance in barley using environmental association and linkage disequilibrium analysis.

Seurat v3 finds scored mutual-nearest-neighbor anchors in canonical correlation space to integrate scRNA-seq batches and transfer labels, protein levels and transcriptome-wide expression across modalities.

The paper evaluates seven comparative genomic methods for predicting deleterious variants in Arabidopsis thaliana and finds their relative performance varies across species.

Seurat combines canonical correlation analysis and dynamic time warping to co-embed scRNA-seq datasets from different conditions, platforms, or species, outscoring ComBat and limma in alignment score.