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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 13, 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

scBubbletree: computational approach for visualization of single cell RNA-seq data.

Simo Kitanovski¹, Yingying Cao², Dimitris Ttoouli²

¹Bioinformatics and Computational Biophysics, Faculty of Biology and Centre for Medical Biotechnology (ZMB), University of Duisburg-Essen, 45141, Essen, Germany. simo.kitanovski@uni-due.de.

BMC Bioinformatics

|September 13, 2024

Summary

This summary is machine-generated.

scBubbletree offers a novel visualization method for single-cell RNA sequencing (scRNA-seq) data. This scalable approach addresses overplotting and quantitative assessment issues, enhancing biological interpretation of complex datasets.

Keywords:

Transcriptomics Visualization scRNA-seq

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Last Updated: Jun 13, 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

Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy

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Published on: January 19, 2024

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

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Published on: March 29, 2024

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Standard single-cell RNA sequencing (scRNA-seq) visualization methods often suffer from overplotting and lack quantitative information.
Existing approaches can distort the global and local properties of biological patterns from high-dimensional data.

Purpose of the Study:

To develop a scalable and quantitative visualization method for scRNA-seq data.
To improve the analysis of cell relationships and biological insights from scRNA-seq experiments.

Main Methods:

scBubbletree identifies cell clusters based on transcriptomes.
Clusters are visualized as "bubbles" on dendrograms, representing quantitative summaries.
Bubble trees are stacked to integrate additional cluster information.

Main Results:

scBubbletree provides a scalable method for scRNA-seq data visualization.
The method facilitates quantitative assessment and biological interpretation.
Demonstrated effectiveness on large datasets, including over 1.2 million cells.

Conclusions:

The R-package scBubbletree facilitates coherent quantification and visualization of scRNA-seq data.
scBubbletree is freely available via the Bioconductor repository.