Guojie Zhong

This paper presents a single-cell multiome atlas of the human basal ganglia, linking chromatin states and transcriptomes to cell-type identity and disease-associated variants.

The paper creates a single-cell atlas of transcription and chromatin states in the human brain, identifying regulatory programs and linking them to neuropsychiatric disorders.

PreMode predicts the mode-of-action of missense variants using graph neural networks, achieving state-of-the-art performance in mode-of-action classification.

The paper introduces MisFit, a probabilistic graphical model to estimate selection coefficients of missense variants using population sequence data, showing improved performance in prioritizing disease-associated variants.

Smoother integrates positional information into non-spatial models for spatial omics data, enabling accurate imputation, deconvolution, and dimensionality reduction in simulated and real datasets.

VBASS integrates single-cell expression and de novo variant data to enhance power in discovering disease risk genes, showing better performance than existing methods on simulated data and identifying more candidate risk genes.

The paper identifies novel risk genes associated with EA/TEF through whole genome sequencing and functional validation using CRISPR-Cas9 in Xenopus.

This paper introduces CSOmap, a computational method to infer spatial organization from scRNA-seq data, successfully reconstructing tumor microenvironments and simulating perturbations to study cellular interactions.