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.

The Rise of Hypothesis-Driven Artificial Intelligence in Oncology

“…As such, we hypothesize the existence of a new class of genes named spatially predictive genes (SPGs), whose collective expression can predict where cells (or subpopulations of cells) are organized in space. This inspired us to develop a novel deep learning algorithm called Spatially Informed AI (SPIN-AI) (2023) to test this hypothesis [35]. SPIN-AI employs an unbiased approach and uses only the spatial gene expression, per patient per slide, as an input and is trained to predict the x and y spatial coordinates in a spatial transcriptomic slide.…”