Artificial Intelligence Models for Cell Type and Subtype Identification Based on Single-Cell RNA Sequencing Data in Vision Science

Single-cell RNA sequencing (scRNA-seq) provides ahigh throughput, quantitative and unbiased framework for scientists in many research fields to identify and characterize cell types within heterogeneous cell populations from various tissues. How-ever, scRNA-seq based identification of discrete cell-types is still la-bor intensive and depends on prior molecular knowledge. Artificialintelligence has provided faster, more accurate, and user-friendlyapproaches for cell-type identification. In this review, we discussrecent advances in cell-type identification methods using artificialintelligence techniques based on single-cell and single-nucleus RNAsequencing data in vision science. The main purpose of this review paper is to assist vision scientists not only to select suitable data sets for their problems, but also to be aware of the appropriate computational tools to perform their analysis. Developing novel methods for scRNA-seq data analysis remains to be addressed in future studies.