SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes
Abstract
Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of differentially expressed genes and permits us to understand how genes and associated molecular processes are spatially distributed within cellular niches. However, the expression activities of SVGs fail to encode all information inherent in the spatial distribution of cells. Here, we devised a deep learning model, Spatially Informed Artificial Intelligence (SPIN-AI), to identify spatially predictive genes (SPGs), whose expression can predict how cells are organized in space. We used SPIN-AI on spatial transcriptomic data from squamous cell carcinoma (SCC) as a proof of concept. Our results demonstrate that SPGs not only recapitulate the biology of SCC but also identify genes distinct from SVGs. Moreover, we found a substantial number of ribosomal genes that were SPGs but not SVGs. Since SPGs possess the capability to predict spatial cellular organization, we reason that SPGs capture more biologically relevant information for a given cellular niche than SVGs. Thus, SPIN-AI has broad applications for detecting SPGs and uncovering which biological processes play important roles in governing cellular organization.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 27, 2023
- Source ID
- 10.3390/biom13060895
Entities
People
- Cheng Zhang
- Choong-yong Ung
- Cristina Correia
- Hu Li
- Kevin Meng-lin
- Kok Siong Yeo
- Philip Wisniewski
- Shizhen Zhu
- Shyang-hong Tan
- Taylor M. Weiskittel
- Zhuofei Zhang
Organizations
- Glenn Foundation for Medical Research
- Mayo Clinic
- Mayo Medical School
- National Cancer Institute
- National Institutes of Health
- United States Department of Defense
- V Foundation for Cancer Research