Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) has become a routinely used technique to quantify the gene expression profile of thousands of single cells simultaneously. Analysis of scRNA-seq data plays an important role in the study of cell states and phenotypes, and has helped elucidate biological processes, such as those occurring during the development of complex organisms, and improved our understanding of disease states, such as cancer, diabetes, and coronavirus disease 2019 (COVID-19). Deep learning, a recent advance of artificial intelligence that has been used to address many problems involving large datasets, has also emerged as a promising tool for scRNA-seq data analysis, as it has a capacity to extract informative and compact features from noisy, heterogeneous, and high-dimensional sc

SUBMITTER: Brendel M 

PROVIDER: S-EPMC10025684 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature