Qunhua Li

OT-knn aligns spatial transcriptomics data by integrating local neighborhood information within an optimal transport framework, achieving accurate alignment across diverse datasets and conditions.

RETROFIT deconvolves cell-type mixtures in spatial transcriptomics without requiring references, outperforming existing methods and matching reference-based approaches when references are imperfect.

The paper introduces a statistical model to measure reproducibility and replicability across replicate experiments in multi-source studies, using a nested copula mixture model.

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.

This paper introduces RCFGL, a fast method for joint estimation of condition-specific gene co-expression networks, showing improved computational efficiency and performance over existing methods.

This paper develops a regression model to assess how operational factors affect reproducibility in high-throughput experiments with missing data, showing improved accuracy over existing methods.

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 introduces a spatial modeling approach for detecting differentially interacting genomic regions in Hi-C data with false discovery control, demonstrating its ability to reveal biologically meaningful changes in chromatin architecture.

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.

The paper evaluates reproducibility and quality measures for Hi-C data using real and simulated datasets, showing that specialized methods outperform simple correlation analysis.

The paper introduces CFGL, a method for inferring condition-specific gene co-expression networks, showing improved accuracy and revealing biological insights in rat and cancer datasets.

HiCRep assesses Hi-C data reproducibility using a stratum-adjusted correlation coefficient, improving accuracy over existing methods.