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

TL;DR

This paper reviews recent AI-based methods for identifying cell types and subtypes using single-cell RNA sequencing data in vision science, highlighting advancements that improve speed and accuracy over traditional approaches.

Contribution

It provides a comprehensive overview of recent AI techniques applied to cell-type identification in single-cell RNA sequencing data within vision science.

Findings

AI methods enhance speed and accuracy of cell-type identification

Recent advances enable more precise characterization of cell subtypes

Review highlights integration of AI in vision-related single-cell analysis

Abstract

Single-cell RNA sequencing (scRNA-seq) provides a high throughput, quantitative and unbiased framework for scientists in many research fields to identify and characterize cell types within heterogeneous cell populations from various tissues. However, scRNA-seq based identification of discrete cell-types is still labor intensive and depends on prior molecular knowledge. Artificial intelligence has provided faster, more accurate, and user-friendly approaches for cell-type identification. In this review, we discuss recent advances in cell-type identification methods using artificial intelligence techniques based on single-cell and single-nucleus RNA sequencing data in vision science.