Emmanuel Dollinger

The paper introduces a statistically principled feature selection method for scRNA-seq that improves accuracy in identifying rare cell types compared to existing tools.

The paper introduces BigSur, a method for identifying gene-gene correlations in scRNA-seq data without normalization, showing it captures significant correlation structures and aligns with known biological processes.

This paper introduces scFAN, a deep learning model that predicts genome-wide TF binding in single cells using single-cell ATAC-seq data and pre-trained on bulk ATAC-seq, DNA sequence, and ChIP-seq data.