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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Jun 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

scEGG: an exogenous gene-guided clustering method for single-cell transcriptomic data.

Dayu Hu¹, Renxiang Guan¹, Ke Liang¹

¹School of Computer, National University of Defense Technology, No. 109 Deya Road, 410073 Changsha, Hunan, China.

Briefings in Bioinformatics

|September 30, 2024

Summary

This summary is machine-generated.

This study introduces a novel deep clustering method for single-cell data analysis, integrating gene networks to improve cell representation and clinical relevance. The approach enhances disease diagnosis and treatment strategies.

Keywords:

Node2vec clustering deep learning exogenous gene information protein-protein interaction

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Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Single-cell data analysis and clustering methods have advanced significantly.
Current algorithms primarily analyze matrix data, often overlooking crucial exogenous information like gene networks.
Ignoring gene networks can lead to information loss and clinically irrelevant clustering outcomes.

Purpose of the Study:

To develop an innovative deep clustering method for single-cell data.
To leverage exogenous gene information for generating discriminative cell representations.
To improve the clinical relevance of single-cell data clustering.

Main Methods:

Developed an attention-enhanced graph autoencoder to capture topological signal patterns.
Utilized random walks on a protein-protein interaction network for gene embedding acquisition.
Integrated and reconstructed gene-cell cooperative embeddings for a discriminative representation.

Main Results:

The proposed deep clustering method effectively leverages exogenous gene information.
The attention-enhanced graph autoencoder successfully captured topological signals.
Gene embeddings acquired through random walks enhanced cell representation.
Extensive experiments validated the method's effectiveness in producing discriminative cell representations.

Conclusions:

The novel deep clustering method enhances insights into cellular characteristics by integrating gene networks.
This approach improves the clinical relevance of single-cell data analysis.
The findings lay the foundation for advancing early disease diagnosis and treatment strategies.