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

Yeganeh Madadi, Aboozar Monavarfeshani, Hao Chen, W. Daniel Stamer, Robert W. Williams, Siamak Yousefi

Research output: Contribution to journalArticlepeer-review

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. 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.

Original languageEnglish (US)
Pages (from-to)2837-2852
Number of pages16
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume20
Issue number5
DOIs
StatePublished - Sep 1 2023
Externally publishedYes

Keywords

  • Artificial intelligence
  • review
  • single-cell RNA sequencing
  • vision science

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

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