Aurélie AG Gabriel

SuperCell2.0 constructs multimodal metacell atlases from blood and tumor samples, identifying interferon-primed monocytes and macrophages with improved inter-modality consistency.

This study shows that peripheral monocytes' epigenomic landscape is rewired by a distal tumor, shaping their transcriptional response upon tumor infiltration.

SuperSpot reduces spatial transcriptomic data size and noise by grouping similar spots into metaspots using graph-based clustering.

This paper develops EPIC-ATAC to estimate cancer and immune cell-type proportions from bulk tumor ATAC-Seq data, showing accurate prediction of immune contexture in breast cancer subtypes.

The paper reviews metacell applications in single-cell genomics, provides a tutorial, and introduces a pipeline for metacell construction and analysis.

This study investigates the relationship between genetically predicted leukocyte telomere length and lung cancer risk, finding a causal association and novel susceptibility loci.

The paper introduces metacells to address scalability issues in scRNA-seq data by merging similar cells, improving downstream analyses and accelerating atlas construction.

This study identifies eight novel lung cancer susceptibility loci and shows that germline variants related to smoking and DNA repair influence somatic mutation burden in lung tumors.

Needlestack is a variant caller that improves sensitivity for low abundance mutations by learning sequencing error rates from multi-sample data.