Topics: spatial transcriptomics, spatial domain identification, representation learning, model robustness Skills: Programming Languages: Python; PyTorch experience preferred Machine Learning: representation learning, clustering, robustness and stability analysis Data Analysis: spatial transcriptomics preprocessing and evaluation (ARI, clustering metrics) Bioinformatics Knowledge (preferred): familiarity with spatial transcriptomics or scRNA-seq data Difficulty: Advanced Size: Large (350 hours) Mentors: Ziheng Duan (contact person) Project Idea Description Spatial domain identification is a fundamental task in spatial transcriptomics (ST), aiming to partition tissue sections into biologically meaningful regions based on spatially resolved gene expression profiles.
