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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: Jan 15, 2026

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

Network based simultaneous embedding of cells and marker genes from scRNA-seq studies.

Namrata Bhattacharya^1,2,3, Swagatam Chakraborti¹, Stuti Kumari¹

¹Department of Computer Science and Engineering, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), Okhla Industrial Estate, Phase III, New Delhi - 110020, Delhi, India.

Briefings in Bioinformatics

|October 6, 2025

Summary

Stardust is a new algorithm for visualizing single-cell RNA sequencing (scRNA-seq) data. It simultaneously embeds cells and marker genes, offering a unified 2D map for enhanced data exploration and cell type identification.

Keywords:

clustering embedding gene expression cartography scRNA-Seq

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Last Updated: Jan 15, 2026

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

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Published on: January 7, 2020

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

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Published on: March 12, 2021

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

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) generates complex datasets requiring advanced analytical methods.
Current clustering and visualization tools often struggle to effectively represent both cellular populations and their associated marker genes simultaneously.

Purpose of the Study:

To introduce Stardust, an iterative, force-directed graph layout algorithm for scRNA-seq data analysis.
To enable the simultaneous embedding and visualization of cells and marker genes on a single 2D map.
To provide a flexible visualization pipeline compatible with existing methods like UMAP and t-SNE.

Main Methods:

Development of Stardust, an iterative, force-directed graph layout algorithm.
Implementation of a novel visualization pipeline for simultaneous cell and marker gene embedding.
Benchmarking Stardust against established visualization and clustering tools using scRNA-seq and spatial transcriptomics datasets.

Main Results:

Stardust successfully integrates cells and marker genes into a single 2D visualization.
The algorithm demonstrates competitive performance in identifying and visualizing cell types accurately.
Spatial coherence of cell type visualization is maintained in transcriptomics datasets.

Conclusions:

Stardust offers a novel, unified approach for visualizing complex scRNA-seq and spatial transcriptomics data.
The algorithm enhances the ability to accurately identify and spatially visualize cell types.
Stardust provides a valuable tool for exploring cellular heterogeneity and gene expression patterns.