Guanjue Xiang

JOnTADS identifies TADs and stripes in Hi-C data across different experimental contexts, improving robustness and computational efficiency.

This study identifies conserved and species-specific cis-regulatory elements in human and mouse blood cells using integrative epigenomic analysis and clustering.

JMnorm improves normalization of epigenetic data by jointly normalizing multiple features across cell types and conditions, preserving biological relationships and enhancing downstream analysis.

MetaTiME identifies data-driven meta-components in tumor immune microenvironment scRNA-seq data to annotate cell states and reveal transcriptional regulators.

S3V2-IDEAS normalizes, denoises, and integrates epigenomic data across cell types to identify epigenetic states and signal intensity patterns.

This paper introduces CLIMB, a statistical method for detecting high-dimensional associations in genomic data, showing improved precision and biological relevance in hematopoietic datasets.

The paper integrates epigenomic and transcriptomic data from mouse hematopoiesis to identify regulatory elements and their dynamics across cell types.

S3norm improves normalization of epigenomic data by simultaneously adjusting sequencing depth and signal-to-noise ratio, enhancing the detection of biological variation.

OnTAD identifies hierarchical TAD structures from Hi-C data with improved accuracy and speed, revealing new insights into TAD organization and gene regulation.

The paper integrates GATA transcription factors and epigenomes in mouse hematopoietic cells using IDEAS to map regulatory landscapes and identify candidate cis-regulatory elements.

Snapshot generates hierarchical visualizations of epigenetic features along cell differentiation hierarchies using epigenomic data from the VISION project.