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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: Aug 29, 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

Self-supervised contrastive learning for integrative single cell RNA-seq data analysis.

Wenkai Han¹, Yuqi Cheng^2,3, Jiayang Chen²

¹Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia.

Briefings in Bioinformatics

|September 11, 2022

Summary

This summary is machine-generated.

We developed CLEAR, a new self-supervised learning method for single-cell RNA sequencing data. CLEAR effectively addresses data challenges like batch effects and dropouts, improving downstream analysis and biological insights.

Keywords:

batch effect removal contrastive learning deep learning scRNA-seq

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Last Updated: Aug 29, 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

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

Published on: March 23, 2022

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) generates high-dimensional data with inherent technical challenges.
Existing methods struggle to simultaneously address data heterogeneity, batch effects, and dropout events in scRNA-seq analysis.

Purpose of the Study:

To introduce a novel self-supervised framework, Contrastive LEArning for scRNA-seq (CLEAR), for robust data representation.
To enhance downstream scRNA-seq analyses by overcoming common data limitations.

Main Methods:

Developed a self-supervised contrastive learning framework (CLEAR) tailored for scRNA-seq data.
Designed a representation learning task to handle data heterogeneity, batch effects, and dropout events concurrently.

Main Results:

CLEAR demonstrated superior performance in fundamental scRNA-seq tasks, including clustering, visualization, and pseudo-time inference.
The method effectively corrected batch effects and dropout events, improving data representation.
Successfully identified inflammatory mechanisms in a COVID-19 dataset comprising 43,695 peripheral blood mononuclear cells.

Conclusions:

CLEAR offers a powerful and versatile tool for scRNA-seq data representation and analysis.
The framework's ability to handle data imperfections makes it suitable for complex biological studies, such as COVID-19 research.