Qihuang Zhang

MOLA identifies multiomic loops in datasets, showing better capture of epigenetic alterations in Influenza A virus data and associations with progression in breast cancer.

The paper develops a statistical method to detect locus-specific escape from X-chromosome inactivation in DNA methylation data, applied to rheumatoid arthritis and venous thromboembolism datasets.

DenMark identifies genes correlated with cell density in spatial transcriptomics data using a Bayesian hierarchical model, revealing density-related biological programs across tissues.

InSTaPath integrates spatial transcriptomics and histopathology images using multimodal topic learning to improve spatial domain identification and uncover gene-morphology relationships.

SpaTM is a topic modeling framework for spatial transcriptomics data that supports both annotation-guided and annotation-free analysis, showing competitiveness in spatial label prediction and clustering.

MuSiC2 improves cell type proportion estimation in bulk RNA-seq data from different clinical conditions using an iterative method, outperforming traditional approaches.