Yifan Zhao

The paper investigates somatic copy number variants (CNVs) in human neurons across development, finding that perinatal elimination of neurons with aberrant genomes reduces CNV prevalence and targets dosage-sensitive genes.

scE2TM improves single-cell embedding interpretability by integrating external knowledge, achieving better clustering and revealing biological perturbation signatures across datasets.

The paper uses single-cell DNA sequencing to analyze somatic mosaicism in postmortem lung and colon tissues, revealing mutation heterogeneity, chromosomal abnormalities, and cellular ancestry.

HiScanner detects high-resolution copy number alterations in single cells, outperforming existing methods and revealing distinct CNA patterns in neurons and oligodendrocytes.

This paper introduces scETM, a model that learns interpretable gene and cell type embeddings from scRNA-seq data, showing cross-tissue and cross-species transferability.