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RNA-seq

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Updated: Oct 20, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Cell-specific gene association network construction from single-cell RNA sequence.

Riasat Azim¹, Shulin Wang¹

¹College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, P.R. China.

Cell Cycle (Georgetown, Tex.)

|September 17, 2021

Summary

This summary is machine-generated.

This study introduces a novel method for constructing cell-specific gene networks using single-cell RNA sequencing data. This approach aids in understanding gene interactions and identifying key genes for personalized cancer therapeutics.

Keywords:

Single-cell RNA embryonic stem cell gene association hub gene perturbed network significance analysis

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Published on: October 26, 2018

DamID-seq: Genome-wide Mapping of Protein-DNA Interactions by High Throughput Sequencing of Adenine-methylated DNA Fragments

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Published on: January 27, 2016

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Last Updated: Oct 20, 2025

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DamID-seq: Genome-wide Mapping of Protein-DNA Interactions by High Throughput Sequencing of Adenine-methylated DNA Fragments

DamID-seq: Genome-wide Mapping of Protein-DNA Interactions by High Throughput Sequencing of Adenine-methylated DNA Fragments

Published on: January 27, 2016

Area of Science:

Genomics
Computational Biology
Systems Biology

Background:

High-throughput single-cell RNA sequencing (scRNA-seq) enables detailed transcriptome analysis and cell characterization.
Cancer epigenetics involves complex gene interactions and system-level dysfunctions.
Existing scRNA-seq analysis tools often overlook single-cell network construction potential.

Purpose of the Study:

To develop a cell-specific gene network construction method using network perturbation theory.
To characterize individual cells by analyzing gene-gene associations at single-cell resolution.
To identify crucial hub genes within these cell-specific networks.

Main Methods:

Applied network perturbation theory and significant analysis to scRNA-seq data.
Constructed cell-specific gene networks to reveal gene-gene associations.
Utilized network degree theory to identify hub genes within each cell.
Performed pathway and gene enrichment analysis on identified hub genes.

Main Results:

Successfully developed a method for cell-specific network construction from scRNA-seq data.
Demonstrated the ability to characterize cells based on their unique gene network structures.
Identified significant hub genes through network degree analysis.
Pathway and gene enrichment analysis validated the biological relevance of identified genes.

Conclusions:

The developed method provides insights into gene-gene associations at single-cell resolution.
Cell-specific network analysis can effectively characterize individual cells and identify key genes.
This approach holds promise for advancing personalized drug design and therapeutic strategies in cancer.