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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 11, 2026

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

ScRDAVis: An R shiny application for single-cell transcriptome data analysis and visualization.

Sankarasubramanian Jagadesan¹, Chittibabu Guda^1,2

¹Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, Nebraska, United States of America.

Plos Computational Biology

|November 13, 2025

Summary

This summary is machine-generated.

ScRDAVis is a new R Shiny application that simplifies single-cell RNA sequencing (scRNA-seq) data analysis for biologists. This user-friendly tool offers advanced features without requiring programming knowledge, democratizing scRNA-seq data exploration.

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Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing

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Last Updated: Jan 11, 2026

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

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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Published on: October 19, 2021

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing

Published on: August 1, 2025

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) provides deep insights into cellular heterogeneity.
Data processing complexity and programming requirements create barriers for biologists using scRNA-seq data.

Purpose of the Study:

To develop an accessible, interactive, browser-based R Shiny application for scRNA-seq data analysis.
To empower biologists with no programming expertise to perform comprehensive scRNA-seq analyses.

Main Methods:

Developed ScRDAVis, an R Shiny application integrating Seurat, CellChat, Monocle3, clusterProfiler, and hdWGCNA.
Implemented user-friendly interface for single-sample, multiple-sample, and group-based analyses.
Included functionalities for marker discovery, cell type annotation, subclustering, cell-cell communication, trajectory inference, pathway enrichment, WGCNA, and TF regulatory network analysis.

Main Results:

ScRDAVis offers a GUI-based platform for scRNA-seq analysis, including novel hdWGCNA integration.
The application supports advanced functional studies and publication-ready visualizations.
Provides data download options in various formats for further research.

Conclusions:

ScRDAVis democratizes scRNA-seq data analysis by providing an intuitive graphical user interface.
Enables researchers to extract meaningful biological insights from complex scRNA-seq datasets.
Facilitates advanced analyses like co-expression and TF regulatory network analysis without coding.