Chen Yang (Caffery) 🧬


About Me

I'm a PhD Student in Statistics at Texas A&M University, co-advised by Prof. Xianyang Zhang and Prof. Jun Chen at Mayo Clinic.

My research focuses on: - LLM4omics: Using Large Language Models for omics data analysis - Single-cell RNA-seq Analysis: Computational methods for scRNA-seq data - Microbiome Analysis: Statistical methods for microbiome data - LLMs for Productivity: Building tools to enhance daily workflows

Featured Projects

mLLMCelltype · New Release

An iterative multi-LLM consensus framework for cell type annotation in single-cell RNA sequencing data. By leveraging the complementary strengths of multiple large language models (including GPT-4o/4.1, Claude-3.7/3.5, Gemini-2.0, Grok-3, and others), this framework significantly improves annotation accuracy while providing transparent uncertainty quantification.

• Paper: Large Language Model Consensus Substantially Improves the Cell Type Annotation Accuracy for scRNA-seq Data
• Key Achievement: Outperforms state-of-the-art methods by nearly 15% in mean accuracy (77.3% vs 61.3%) across 50 diverse datasets

MicrobiomeStat · 29K+ Downloads

Comprehensive R package for microbiome and multi-omics analysis

ggpicrust2 · 100+ Citations

R package for PICRUSt2 visualization and analysis

LLMCal

LLM-powered tool that converts text into calendar events

Recent Highlights

• 2025: Published mLLMCelltype in bioRxiv, a novel multi-LLM consensus framework for scRNA-seq cell type annotation
• 2025: ggpicrust2 paper reached 100 citations in Google Scholar
• 2024: Awarded the Su Binghua Distinguished Biostatistics Scholarship
• Published in Bioinformatics (2023)

Google Scholar Stats

Top Cited Papers

• ggpicrust2: an R package for PICRUSt2 predicted functional profile analysis and visualization. (2023, N/A) - 180 citations
• The microbiome at the interface between environmental stress and animal health: an example from the most threatened vertebrate group (2024, N/A) - 3 citations
• Large Language Model Consensus Substantially Improves the Cell Type Annotation Accuracy for scRNA-seq Data (2025, N/A) - 0 citations

Top Cited Papers

• ggpicrust2: an R package for PICRUSt2 predicted functional profile analysis and visualization. (2023, N/A) - 156 citations
• The microbiome at the interface between environmental stress and animal health: an example from the most threatened vertebrate group (2024, N/A) - 2 citations
• Large Language Model Consensus Substantially Improves the Cell Type Annotation Accuracy for scRNA-seq Data (2025, N/A) - 0 citations

Top Cited Papers

• ggpicrust2: an R package for PICRUSt2 predicted functional profile analysis and visualization. (2023, Bioinformatics) - 132 citations
• Large Language Model Consensus Substantially Improves the Cell Type Annotation Accuracy for scRNA-seq Data (2025, bioRxiv) - New publication
• The microbiome at the interface between environmental stress and animal health: an example from the most threatened vertebrate group (2024, Proceedings of the Royal Society B) - 1 citations

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