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.
@article{arxiv.2209.13022,
title = {Artificial Intelligence Models for Cell Type and Subtype Identification Based on Single-Cell RNA Sequencing Data in Vision Science},
author = {Yeganeh Madadi and Aboozar Monavarfeshani and Hao Chen and W. Daniel Stamer and Robert W. Williams and Siamak Yousefi},
journal= {arXiv preprint arXiv:2209.13022},
year = {2022}
}