Qiwen Hu

The paper uses multiomic single-cell and spatial data to identify gene regulatory networks and transcription factors driving cortical regionality in the human brain.

This study uses snRNA-seq and a new method called GO-TEN to analyze mosaic genetic variants in focal cortical dysplasia, revealing cell-autonomous and non-cell-autonomous transcriptional changes associated with epilepsy.

The paper creates a high-resolution cellular atlas of healthy and injured kidney cell states and niches using single-cell and spatial technologies, identifying molecular signatures and pathways related to kidney injury.

The paper identifies selective vulnerability of supragranular layer neurons in schizophrenia through single nucleus RNA sequencing and histological analysis.

The paper compares cellular diversity in the primary motor cortex across human, marmoset, and mouse using single-nucleus transcriptomics and epigenomics, finding conserved cell types and species-specific adaptations.

The paper creates a comprehensive atlas of mouse primary motor cortex cell types using integrated transcriptomic and epigenomic data from over 600,000 samples.

The paper identifies histone modifications associated with alternative exon selection during mammalian development using machine learning.

The paper introduces scCoGAPS and projectR to decompose cell identity into shared and species-specific factors, enabling transfer learning across datasets and species.

MultiPLIER uses transfer learning from large public data to improve analysis of small rare disease datasets by capturing systemic biological features.

The paper evaluates the Tybalt VAE for scRNA-seq data, showing its performance depends heavily on parameter tuning and outperforms other methods when properly tuned.