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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: Jan 2, 2026

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

schex avoids overplotting for large single-cell RNA-sequencing datasets.

Saskia Freytag¹, Ryan Lister^1,2

¹Molecular Medicine, Harry Perkins Institute of Medical Research, Perth, 6009, WA, Australia.

Bioinformatics (Oxford, England)

|December 4, 2019

Summary

This summary is machine-generated.

The schex R package uses hexagonal binning to visualize large single-cell RNA sequencing datasets, improving clarity and efficiency for researchers.

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Last Updated: Jan 2, 2026

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

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Published on: March 23, 2022

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) data presents challenges due to its large scale and sparsity.
Traditional plotting methods can obscure important biological insights within scRNA-seq data.

Purpose of the Study:

To introduce schex, an R package designed to enhance the visualization of scRNA-seq data.
To address the limitations of conventional plotting techniques for high-dimensional single-cell data.

Main Methods:

Implementation of hexagonal binning within the schex R package.
Development of a novel approach to handle the scale and sparsity inherent in scRNA-seq datasets.

Main Results:

Hexagonal binning effectively visualizes complex scRNA-seq data, overcoming limitations of traditional plots.
The schex package demonstrates improved plotting speed and reduced storage requirements.

Conclusions:

schex provides a powerful and efficient tool for the analysis and visualization of single-cell RNA sequencing data.
This approach enhances data interpretability and facilitates biological discovery from large-scale single-cell experiments.