A Gene Ontology-Driven Wide and Deep Learning Architecture for Cell-Type Classification from Single-Cell RNA-seq Data

Abstract

Recent advances in single-cell RNA-sequencing in order to study cells in biology, and the increasing amount of data available, led to the development of algorithms for analyzing single cells from gene expression data. In this work, we propose an artificial intelligence architecture that classifies cell types of human tissue. This architecture combines a deep learning model based on the convolutional neural network (CNN) with a wide model. The classification model integrates the concept of functional genes neighbourhood, based on Gene Ontology, in the CNN model (deep part) and the information on biologically relevant marker genes for each cell type in the underlying human tissue (wide part). This approach leads to a gene ontology-driven wide and deep learning model. We tested the proposed architecture with seven human tissue datasets and compared achieved results against three reference literature algorithms. Although the cell-type classification problem is heavily data-dependent, our model performed equal or better than the other models within each tissue.