Yuzhou Chang

Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering

The paper introduces SACCELERATOR, a framework for spatially aware clustering that improves benchmarking by incorporating expert feedback and consensus analysis across diverse datasets.

A Principled Statistical Framework for Analyzing Spatial Patterns in Spatially Resolved Multi-Omics

This paper introduces STORM, a statistical framework for analyzing spatial patterns in multi-omics data, demonstrating reliable performance in detecting spatial structures and supporting study design decisions.

Harnessing the Power of Single-Cell Large Language Models with Parameter Efficient Fine-Tuning using scPEFT

This paper introduces scPEFT, a parameter-efficient fine-tuning framework for scLLMs that improves task adaptation with reduced computational costs and enhances performance in disease-specific and cross-species analyses.

Defining transcriptomic niches in human fibrotic lung with multi-sample spatial transcriptomics analysis using the MAPLE algorithm

The paper uses spatial transcriptomics and the MAPLE algorithm to analyze fibrotic lung tissue, identifying spatial biomarkers and fibrotic programs in IPF.