Gang Li

The paper develops a high-throughput Visual Cell Sorting workflow to link nuclear compartment morphology to single-nucleus transcriptomes, identifying a conserved transcriptional signature of nucleolar size linked to proteostasis.

The paper investigates how sex chromosome aneuploidy affects gene expression and DNA methylation in different cell types, revealing cell-type specific dosage compensation mechanisms.

CellCycleNet predicts cell cycle phases from 3D nuclear-stained images, outperforming SVM models and showing strong generalization across microscopes.

This paper introduces a method using local gradients of functional connectivity to identify infant brains with high accuracy, achieving 99% individual identification rates across ages.

Capricorn enhances low-coverage Hi-C contact matrices using a multi-view diffusion model to improve loop detection accuracy across cell lines and chromosomes.

This paper introduces Sunbear, a framework for integrating multi-condition and multi-modal single-cell profiles across time to infer temporal profiles and predict dynamic relationships in mouse embryonic development.

The paper introduces Sceptic, a supervised pseudotime analysis model that improves prediction accuracy over existing methods for scRNA-seq and single-nucleus image data.

LSMMD-MA scales multimodal data integration for single-cell genomics to handle up to a million cells per modality, improving on existing methods by two orders of magnitude.

CRISPRpath enables parallel characterization of cis-regulatory elements for multiple genes in a pathway, identifying functional enhancers and distinguishing their impact on gene expression.

iSMNN improves batch effect correction in scRNA-seq by iteratively refining mutual nearest neighbors, enhancing cell type mixing and differential gene identification across batches.

CUE imputes DNA methylation from HM450 to HM850 platforms, achieving lower RMSE and higher accuracy than existing methods across two cohorts.

SMNN improves batch effect correction in scRNA-seq by using supervised mutual nearest neighbor detection within cell types, leading to better merging and retention of cell-type specific features.