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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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Related Experiment Video

Updated: Jun 25, 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

scVSC: Deep Variational Subspace Clustering for Single-Cell Transcriptome Data.

Zile Wang, Haiyun Wang, Jianping Zhao

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|May 27, 2024

Summary

This summary is machine-generated.

Single-cell RNA sequencing (scRNA-seq) data analysis is improved by scVSC, a novel deep learning algorithm. This unsupervised clustering tool enhances accuracy and efficiency for identifying cell types and biological pathways.

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Published on: April 25, 2011

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Last Updated: Jun 25, 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

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

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) enables gene expression analysis at the individual cell level, revealing cellular heterogeneity.
scRNA-seq data is often sparse and heterogeneous due to technical limitations, posing challenges for analysis.

Purpose of the Study:

To introduce scVSC, an unsupervised clustering algorithm designed to address the limitations of scRNA-seq data.
To improve the accuracy and efficiency of cell subpopulation identification from scRNA-seq data.

Main Methods:

Developed scVSC, an unsupervised clustering algorithm utilizing deep representation neural networks.
Integrated variational inference into a subspace model to regularize the latent space and prevent overfitting.

Main Results:

scVSC demonstrated superior clustering accuracy and running efficiency compared to state-of-the-art methods across multiple datasets.
The algorithm successfully identified differentially expressed genes and discovered critical biological pathways.
scVSC visually revealed cell differentiation trajectories.

Conclusions:

scVSC offers a robust and efficient solution for analyzing sparse and heterogeneous scRNA-seq data.
The method enhances the discovery of cellular heterogeneity, gene expression patterns, and biological insights.