Zhijian Li

The paper maps chemokine landscapes in solid tumors using spatial transcriptomics, identifying conserved and tumor-specific patterns and revealing perivascular CCR7 dendritic cells as key sources of lymphocyte-attracting chemokines.

Spacelink integrates spatial transcriptomics with genetics to identify spatially variable genes linked to disease, showing improved detection power and disease informativeness.

This study shows that CREB knockout in Foxp3+ Tregs increases IL-10 and ST-2 expression, enhancing their suppressive function and preventing gut inflammation.

The paper maps spatially-resolved transcriptomes in systemic sclerosis to identify a fibrotic niche and disease-specific cell states, linking them to clinical outcomes.

The paper introduces ACCESS-ATAC, a method that improves TF binding site prediction resolution and reveals allele-specific co-occupancy patterns in HepG2 and K562 cells.

PHLOWER infers complex differentiation trees from multi-modal single-cell data using Hodge Laplacian decomposition, showing effectiveness in kidney organoid data.

This paper improves variant effect quantification in base editing screens by integrating genotypic and phenotypic data using a Bayesian network called BEAN.

This study benchmarks 14 computational methods for identifying spatially variable genes and peaks using simulated and real-world spatial transcriptomics and ATAC-seq data, finding spatialDE2 performs best and highlighting the need for specialized algorithms for peaks.

RGT is a Python package for integrative analysis of regulatory genomics data, offering tools for peak calling, binding site prediction, and regulatory factor association.

scMEGA infers gene regulatory networks from single-cell multi-omics data, demonstrating its application in studying myofibroblast activation in human myocardial infarction.

BIONIC integrates biological networks using deep learning to improve accuracy and scalability, identifying novel gene interactions in yeast.

This study generates a high-resolution spatial multi-omic map of human myocardial infarction by integrating single-cell gene expression, chromatin accessibility, and spatial transcriptomic data to reveal cellular zones of injury, repair, and remodeling.

MOJITOO integrates multimodal single cell data using canonical correlation analysis, outperforming existing methods in computational efficiency and clustering.

The paper develops YETI, a yeast library with inducible gene expression, and shows low native expression can bias promoter-perturbing screens.

scOpen improves downstream analysis of sparse scATAC-seq data by imputing open chromatin regions, revealing regulatory changes in kidney fibrosis.

The paper introduces HINT-ATAC, a method for identifying transcription factor binding sites in ATAC-seq data by addressing protocol artifacts and using probabilistic models.