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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components.

Federico Marini1,2, Harald Binder3

  • 1Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg University Mainz, Obere Zahlbacher Str. 69, Mainz, 55131, Germany. marinif@uni-mainz.de.

BMC Bioinformatics
|June 15, 2019
PubMed
Summary

We developed pcaExplorer, an R package for interactive exploration of high-dimensional genomics data. This user-friendly tool enhances principal component analysis (PCA) for RNA sequencing (RNA-seq) data quality assessment and exploratory analysis.

Keywords:
BioconductorExploratory data analysisPrincipal component analysisRRNA-SeqReproducible researchShinyUser-friendly

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Principal Component Analysis (PCA) is crucial for analyzing high-dimensional genomics data, particularly RNA sequencing (RNA-seq) gene expression.
  • Existing software packages lack a comprehensive and interactive interface for these essential exploratory analysis steps.

Purpose of the Study:

  • To develop an interactive and user-friendly software package, pcaExplorer, for enhanced PCA in genomics data exploration.
  • To provide features for state saving and automated generation of reproducible reports.

Main Methods:

  • Developed in R using the Shiny framework.
  • Leverages data structures from the Bioconductor open-source project.
  • Offers interactive modules for data overview, dimension reduction (samples and genes), and functional interpretation of principal components.

Main Results:

  • pcaExplorer facilitates the generation of publication-ready graphs for assessing gene expression data.
  • The software provides an interactive environment for exploring principal components and their relationship to samples and genes.
  • Enables functional interpretation of principal components within the analysis workflow.

Conclusions:

  • pcaExplorer is an R package available through the Bioconductor project.
  • Designed to aid researchers in the critical process of interactive data exploration for genomics studies.
  • Enhances the utility of PCA for quality assessment and exploratory analysis of RNA-seq data.