Yun Zhang

The paper shows that differential expression methods like Wilcoxon rank-sum test produce unreliable cell-type markers due to cluster size dependence, and proposes a classification-based alternative with better specificity.

scFPC-DE identifies temporally differentially expressed genes along single-cell trajectories by modeling gene expression as functions of pseudotime and using functional principal component analysis to capture shared covariance structures.

This study shows that B cells in hepatitis C patients retain dysregulated transcriptional profiles after cure, with sustained TNF signaling driving chronic inflammation and loss of protective memory.

The paper creates a high-resolution multimodal lung atlas using single-nucleus RNA and chromatin data from 11 healthy adults, identifying 70 cell populations and 332,846 chromatin regions.

The paper benchmarks seven computational tools for matching cell types across cell atlases, finding variations in accuracy and proposing a framework for incremental growth of cell atlases.

This paper describes the construction and usage of the Human Reference Atlas (HRA) v2.0, which integrates multiscale data across 36 organs and provides tools for analysis and visualization.

NS-Forest v4.0 identifies optimal marker genes for cell type classification in scRNA-seq data, improving accuracy and selectivity for closely related cell types.

This study identifies the transcriptional response to dietary restriction in C. elegans and shows that MML-1::MXL-2 and PHA-4 have distinct roles in mediating the physiological benefits of DR.

This study evaluates six algorithms for cell type matching in spatial transcriptomics data and presents consensus strategies for improved analysis.

FR-Match matches cell type clusters from scRNA-seq data using the Friedman-Rafsky test, improving accuracy and identifying novel cell phenotypes.

This study examines how tissue composition affects gene co-expression estimates and shows how deconvolution methods can improve co-expression analysis.

The paper introduces efficient hypothesis testing methods for regression-type tests with correlated data and heterogeneous variance, outperforming existing methods in speed and accuracy.