Fan Zhang

SPP1hi macrophages in RA synovial fibrin deposits promote tissue hyperplasia.

scLASER simulates and detects time-dependent cellular dynamics in longitudinal studies, showing higher sensitivity than traditional methods in rare cell types and non-linear patterns.

The paper identifies immune alterations preceding rheumatoid arthritis onset and provides tools for dataset exploration.

STEAM evaluates clustering performance in spatial transcriptomics by comparing results across methods and providing statistical metrics to assess consistency and reliability.

The paper identifies activated lymphocyte phenotypes in individuals at risk for rheumatoid arthritis using mass cytometry and develops an immunophenotype score to distinguish these individuals from controls.

The paper identifies shared transcriptional and cell communication signatures in RA patients and at-risk individuals, showing how multiple pathogenic mechanisms contribute to disease initiation and progression.

This study identifies granzyme K as a driver of a novel complement activation pathway independent of classical, lectin, or alternative pathways.

CellPhenoX identifies cell-specific phenotypes linked to clinical outcomes using single-cell multi-omics data, demonstrating its ability to detect relevant phenotypes in diseases like COVID-19 and ulcerative colitis.

BuDDI predicts cell-type-specific perturbations from bulk data by integrating single-cell and bulk profiles using domain adaptation in a variational autoencoder.

This study integrates GWAS and scRNA-seq to identify genetically relevant cell states in inflammatory diseases, finding scDRS outperforms scGWAS in capturing disease-relevant cellular heterogeneity.

The paper develops SCENT, a method to create cell-type-specific enhancer-gene maps from single-cell multimodal data, identifying causal variants and genes for diseases.

The paper identifies Oncostatin M as a driver of inflammation in knee osteoarthritis using network-based cytokine inference and pharmacological manipulation in aged mice.

The paper maps cell-type-specific eQTLs for HLA genes across multiple tissues and identifies dynamic regulation of HLA-DQ genes in immune cells.

STew identifies spatially informed clusters and disease-relevant niches in spatial transcriptomics data by jointly analyzing spatial information and gene expression.

The paper deconstructs RA synovium using single-cell data to identify six cell type abundance phenotypes associated with distinct inflammatory states.

Symphony maps query cells to large reference atlases efficiently, enabling rapid identification of cell states and phenotypes across diverse datasets.

The paper identifies a common inflammatory macrophage phenotype across severe COVID-19 and other inflammatory diseases, driven by IFN-γ and TNF-α, and suggests potential therapeutic targets.

Harmony alternates diversity-penalized soft clustering with cluster-specific linear correction of PCA embeddings, integrating single-cell datasets over multiple covariates and handling 500,000 cells in 7.2GB of memory.