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Related Experiment Video

Updated: Nov 11, 2025

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

ShinyCell: simple and sharable visualization of single-cell gene expression data.

John F Ouyang¹, Uma S Kamaraj¹, Elaine Y Cao¹

¹Program in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore 169857, Singapore.

Bioinformatics (Oxford, England)

|March 28, 2021

Summary

Researchers can now easily explore and share complex single-cell RNA sequencing data using ShinyCell, an R package creating interactive interfaces for non-computational users.

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Last Updated: Nov 11, 2025

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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

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Published on: October 25, 2018

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) generates complex datasets requiring accessible sharing methods.
Current data sharing practices often lack user-friendly interfaces for non-computational researchers.
Facilitating data exploration is crucial for broader scientific understanding and collaboration.

Purpose of the Study:

To develop an accessible tool for exploring and sharing scRNA-seq data.
To bridge the gap between complex biological data and non-computational researchers.
To enhance the usability and shareability of scRNA-seq datasets.

Main Methods:

Developed ShinyCell, an R package for creating interactive data interfaces.
Designed interfaces for easy customization and enhanced usability.
Enabled straightforward uploading of interfaces to online platforms.

Main Results:

ShinyCell converts scRNA-seq datasets into interactive and explorable formats.
The package facilitates data sharing for a wider audience.
Customizable interfaces improve user experience and data accessibility.

Conclusions:

ShinyCell provides a valuable solution for democratizing scRNA-seq data exploration.
The R package promotes wider access and understanding of complex biological datasets.
Interactive interfaces enhance the utility of published scRNA-seq data.