IEEE/ACM Trans Comput Biol Bioinform. 2023 Sep-Oct;20(5):2837-2852. doi: 10.1109/TCBB.2023.3284795. Epub 2023 Oct 9.
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. The main purpose of this review paper is to assist vision scientists not only to select suitable datasets 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.
单细胞 RNA 测序（scRNA-seq）为许多研究领域的科学家提供了一个高通量、定量和无偏的框架，用于从各种组织中的异质细胞群体中识别和表征细胞类型。然而，基于 scRNA-seq 的离散细胞类型识别仍然很费力，并且依赖于先前的分子知识。人工智能为细胞类型识别提供了更快、更准确和更用户友好的方法。在这篇综述中，我们讨论了基于单细胞和单核 RNA 测序数据的人工智能技术在视觉科学中细胞类型识别方法的最新进展。本文的主要目的是帮助视觉科学家不仅选择适合他们问题的数据集，而且还了解适当的计算工具来进行他们的分析。在未来的研究中仍然需要开发用于 scRNA-seq 数据分析的新方法。
