Jian Zou

The paper evaluates microRNA sequencing data harmonization by assessing its impact on sample clustering, showing that harmonization can partially restore clustering accuracy depending on method and settings.

The paper develops a novel mouse cell line model of invasive lobular breast carcinoma that recapitulates human tumor phenotype and immune microenvironment, offering a preclinical platform for therapy evaluation.

This study shows that chronic inflammation and hormonal changes in older women create a tumor-permissive environment for ER+ breast cancer by increasing local estradiol and immune dysfunction.

This study benchmarks 12 scRNA-seq CNV inference methods across 28 datasets, finding allele-aware tools perform better with high-quality allelic data, while expression-centric tools are reliable with raw data.

This study evaluates manual gating, unsupervised clustering, and auto-gating methods for high-dimensional cytometry data using six datasets and provides practical guidelines for selecting appropriate methods.

The paper introduces MICA, a method to detect multi-class biomarkers across multiple transcriptomic studies, showing improved accuracy and biological insights in both bulk and single-cell data.

CAMO evaluates omics congruence between model organisms and humans by analyzing differential analysis, concordance, pathway investigations, and subnetwork detection.