Benchmarking/Improving/Developing Computational Tools for Single-Cell and Spatial Genomics
Peng He, Professor
UC San Francisco
Applications for Spring 2026 are closed for this project.
This project provides a hands-on introduction to benchmarking or advancing state-of-the-art computational tools used in single-cell and spatial transcriptomics analysis. With the rapid growth of available software and datasets, there is a pressing need to evaluate, compare, and optimize tools across a wide range of biological applications.
Role: Students will conduct computational studies on specific categories of tools on a project-driven basis, aiming to produce publications. Areas include:
Foundation models for cell type annotation (e.g., GeneFormer, scSimilarity)
Trajectory inference algorithms for developmental lineage mapping
Knockout effect prediction tools from perturbation screens
Spatial transcriptomics analysis pipelines (e.g., for Visium and Xenium data)
Each student will be paired with an ongoing research project in the lab to apply their benchmarking insights to real biological datasets. The goal is to identify strengths, limitations, and best practices for each method category.
Students will:
Select a tool category and conduct systematic comparisons
Evaluate accuracy, scalability, interpretability, and runtime
Use benchmark results to assist lab members in active biological projects
Contribute to internal documentation and reproducible workflows
Qualifications: Strong interest in computational biology, molecular biology, or bioinformatics methods
Experience in Python or R preferred
Familiarity with single-cell or spatial omics is helpful but not required
Day-to-day supervisor for this project: Konstantinos Stasinos, Post-Doc
Hours: to be negotiated
Off-Campus Research Site: Hybrid/remote working is also allowed
Related website: https://peng-he-lab.github.io/
Related website: https://profiles.ucsf.edu/peng.he