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

Author:

Ouyang John F1ORCID,Kamaraj Uma S1,Cao Elaine Y1ORCID,Rackham Owen J L1ORCID

Affiliation:

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

Abstract

Abstract Motivation As the generation of complex single-cell RNA sequencing datasets becomes more commonplace it is the responsibility of researchers to provide access to these data in a way that can be easily explored and shared. Whilst it is often the case that data is deposited for future bioinformatic analysis many studies do not release their data in a way that is easy to explore by non-computational researchers. Results In order to help address this we have developed ShinyCell, an R package that converts single-cell RNA sequencing datasets into explorable and shareable interactive interfaces. These interfaces can be easily customized in order to maximize their usability and can be easily uploaded to online platforms to facilitate wider access to published data. Availability and implementation ShinyCell is available at https://github.com/SGDDNB/ShinyCell and https://figshare.com/projects/ShinyCell/100439. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

Singapore National Research Foundation

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference12 articles.

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2. Reprogramming roadmap reveals route to human induced trophoblast stem cells;Liu;Nature,2020

3. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R;McCarthy;Bioinformatics,2017

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