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SCHNAPPs - Single Cell sHiNy APPlication(s).

Bernd Jagla1, Valentina Libri2, Claudia Chica3

  • 1Institut Pasteur, Université de Paris, Cytometry and Biomarkers UTechS, F-75015 Paris, France; Institut Pasteur, Université de Paris, Bioinformatics and Biostatistics Hub, F-75015 Paris, France.

Journal of Immunological Methods
|November 7, 2021
PubMed
Summary
This summary is machine-generated.

SCHNAPPs is a new GUI application that simplifies single-cell RNA sequencing (scRNAseq) data analysis for bench scientists. It enables autonomous exploration and interpretation of complex scRNAseq data, fostering collaboration and reproducible research.

Keywords:
CITE-SeqShiny applicationmulti-omics data analysisscRNA-seq

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Single-cell RNA sequencing (scRNAseq) generates complex data requiring specialized bioinformatics expertise.
  • Bench scientists need accessible tools to analyze scRNAseq data and focus on biological discovery.
  • Bridging the gap between bench scientists and bioinformaticians is crucial for advancing scRNAseq research.

Purpose of the Study:

  • To present SCHNAPPs, a Graphical User Interface (GUI) enabling bench scientists to autonomously analyze and interpret scRNAseq data.
  • To provide a user-friendly platform that abstracts technical complexities of scRNAseq analysis.
  • To facilitate reproducible science and collaboration between bench scientists and bioinformaticians.

Main Methods:

  • Development of an R/Shiny-based application, SCHNAPPs, with a modular design.
  • Integration of established scRNAseq analysis workflows from Seurat and Scran packages.
  • Implementation of diverse visualization tools (violin plots, 2D projections, etc.) and R-markdown reporting.

Main Results:

  • SCHNAPPs empowers bench scientists to perform quality control, normalization, integration, dimension reduction, clustering, and differential gene expression analysis.
  • The application supports autonomous data exploration and interpretation through interactive visualizations.
  • A case study on T cell characterization using scRNAseq and CITE-Seq data demonstrates SCHNAPPs' utility.

Conclusions:

  • SCHNAPPs effectively lowers the barrier for bench scientists to conduct sophisticated scRNAseq data analysis.
  • The tool promotes reproducible research practices and enhances collaboration in the field.
  • SCHNAPPs is a valuable resource for the broader scientific community seeking to leverage scRNAseq data.