Zemin Zhang

This paper develops a pan-cancer tumor classification system based on tumor microenvironment cellular components, identifying immune-activation patterns and therapeutic response stratifications.

The paper identifies STEAP4+ neutrophils as a circulating biomarker for lung metastasis, validated in both human and mouse models.

This study identifies MMP1+ fibroblasts as a cellular determinant of resistance to PD-1 pathway blockade in NSCLC by analyzing single-cell RNA sequencing data and spatial architecture.

Redeconve deconvolutes spatial transcriptomics data at single-cell resolution, outperforming existing methods in accuracy and speed across multiple datasets.

This study directly compares 10X Genomics Chromium and Smart-seq2 scRNA-seq platforms, finding differences in gene detection, noise, dropout rates, and differential expression, with recommendations for platform selection based on study goals.

This study uses single-cell RNA sequencing to analyze immune responses in COVID-19 patients, identifying key immune cell changes and molecular pathways linked to disease severity.

ROGUE quantifies cell cluster purity in scRNA-seq data, identifying subtypes and improving analysis of specific subpopulations across datasets.

This paper introduces CSOmap, a computational method to infer spatial organization from scRNA-seq data, successfully reconstructing tumor microenvironments and simulating perturbations to study cellular interactions.

SciBet is a Bayesian classifier for single-cell RNA sequencing data that predicts cell identity and can be deployed locally via a web client.

The paper identifies hundreds of genes deregulated by SVs in cancer, showing increased expression linked to SV breakpoints and enhancer elements.

The paper analyzes immuno-genomic data across 31 cancer types to identify immune escape mechanisms and immuno-editing histories.

SSCC improves clustering accuracy and speed for single-cell RNA-seq data while reducing memory usage compared to existing methods.

The paper analyzes RNA alterations across 27 cancer types using whole-genome sequencing data, identifying gene expression associations with DNA variants and structural fusions.

This study analyzes whole genome and RNA-Seq data from 1,188 cancer patients to map the effects of somatic and germline genetic variation on gene expression in cancer.

The paper systematically characterizes gene fusions in cancer using RNA and whole genome DNA sequencing data from 1188 donors across 27 cancer types, identifying 3297 high-confidence fusions and revealing novel mechanisms.