Chang Su

This study benchmarks 12 methods for differential composition analysis in scRNA-seq data, finding LOCOM performs best in controlling false discovery rate and maintaining power.

The paper introduces MTClass, a machine learning framework for eQTL identification that outperforms traditional methods by detecting more functionally relevant variants in the MHC region.

The paper performs single-cell transcriptomic and proteomic analysis of Parkinson's disease brains, identifying cell-type-specific changes and differences from Alzheimer's disease.

The paper introduces scMultiMap, a statistical method to map enhancer-gene associations in single-cell multimodal data, showing improved performance and efficiency in disease-related cell types.

The paper estimates cell-type-specific gene co-expression networks from bulk gene expression data and identifies novel co-expressions among Alzheimer's disease risk genes.

The paper introduces CS-CORE, a statistical method to infer cell-type-specific gene co-expressions from scRNA-seq data, showing improved accuracy and biological relevance compared to existing methods.

This study introduces a graph-matching based metric to quantify functional connectome differences between individuals and finds it correlates with age, cognitive performance, and familial relationships.