CSENG Computer Science & Eng
College of Science & Engineering
Twin Cities
Project Title: 
High-Order Machine Learning Methods for Spatial Transcriptomics

Biological tissues are composed of different types of structurally organized cells which play distinct and cooperative functional roles in phenotypes. Recent spatial transcriptomics technologies have enabled spatially resolved RNA profiling of single cells with cell identities and localizations for understanding cells’ organizations and functions. This project will develop new machine learning methods for mining RNA profiles collected from single cells and their spatial locations. The research community will benefit from the collection of tools for the analysis of spatial and single-cell genomic data in studying molecular characteristics of cellular structures in tissue. The new methods will be applied to the study of spatial cell heterogeneity of ovarian cancer and circadian rhythms in Brassica rapa. The two applications will improve understanding of cellular structure and pathology of ovarian tissues and the association of cell-specific circadian gene expression patterns with crop improvement traits. 

Project Investigators

Thomas Atkins
Charles Broadbent
Shrijana Gurung
Yoshitaka Inoue
Professor Rui Kuang
Ethan Kulman
Tianci Song
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