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RNA-seq

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Updated: Oct 11, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Deep learning-based advances and applications for single-cell RNA-sequencing data analysis.

Siqi Bao^1,2,3, Ke Li², Congcong Yan²

¹School of Information and Communication Engineering, Hainan University, Haikou 570228, P. R. China.

Briefings in Bioinformatics

|December 1, 2021

Summary

This summary is machine-generated.

Deep learning methods address computational challenges in single-cell RNA sequencing (scRNA-seq) analysis. This review summarizes deep learning tools and future directions for scRNA-seq data interpretation in biomedical research.

Keywords:

bioinformatics deep learning single-cell RNA-sequencing

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Last Updated: Oct 11, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

Area of Science:

Computational biology
Bioinformatics
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) generates complex, high-dimensional data.
Significant computational and analytical challenges exist in scRNA-seq data processing.
Deep learning offers advanced solutions for these challenges.

Purpose of the Study:

To review recent advances in deep learning for scRNA-seq data analysis.
To summarize available deep learning tools for scRNA-seq.
To explore future perspectives and challenges of deep learning in this field.

Main Methods:

Literature review of deep learning applications in scRNA-seq.
Categorization of deep learning methods for upstream and downstream analysis.
Investigation of current tools and their functionalities.

Main Results:

Deep learning effectively addresses scRNA-seq challenges in quality control, normalization, and cell/gene/pathway-level analysis.
A range of deep learning tools are available for various scRNA-seq tasks.
Key future directions and potential hurdles were identified.

Conclusions:

Deep learning shows significant promise for advancing scRNA-seq data analysis.
Evidence supports the biomedical application of deep learning tools.
This review aids biologists and bioinformaticians in this rapidly evolving area.