Discovering Tissue Microenvironments Through Spatial Transcriptomics Analysis
Peng He, Professor
UC San Francisco
Applications for Spring 2025 are closed for this project.
This innovative research project aims to uncover the complex organization of tissues by analyzing spatial gene expression patterns. Using cutting-edge spatial transcriptomics technology, we can now measure gene expression while preserving information about where cells are located within a tissue. This project will adapt and apply advanced analytical methods to understand how a cell's location influences its identity and function.
The research leverages high-quality spatial transcriptomics data already generated in our collaborators' labs using state-of-the-art technology. By analyzing this data with sophisticated computational methods, we aim to discover how different microenvironments within tissues influence cell behavior and fate decisions. This work will provide crucial insights into tissue organization and cellular communication.
Qualifications: The undergraduate researcher will be involved in developing and implementing computational methods for spatial data analysis. Key responsibilities include:
Analysis Pipeline Development:
Process and analyze spatial transcriptomics data
Implement dimension reduction techniques for spatial data
Perform data integration across different samples and experiments
Develop and apply imputation methods for spatial transcriptomics
Identify and characterize distinct tissue microenvironments
Create visualizations that capture spatial relationships
Learning Outcomes:
Master cutting-edge spatial transcriptomics analysis methods
Gain expertise in computational biology and data science
Develop skills in handling and analyzing large-scale spatial data
Learn advanced statistical methods for biological data analysis
Understand tissue organization and cellular microenvironments
Acquire experience in scientific visualization and communication
Required Qualifications
The ideal candidate should have:
Strong programming abilities in R or Python(preferred)
Experience with statistical analysis and data visualization
Familiarity with machine learning concepts
Basic understanding of dimensional reduction techniques
Experience with computational data analysis
Day-to-day supervisor for this project: Jianwen Xie, Staff Researcher
Hours: to be negotiated
Off-Campus Research Site: on site/hybrid/off-campus all acceptable
Related website: https://profiles.ucsf.edu/peng.he
Related website: https://peng-he-lab.github.io/