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R.JIVE for exploration of multi-source molecular data.

Michael J O'Connell1, Eric F Lock1

  • 1Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA.

Bioinformatics (Oxford, England)
|June 9, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces R.JIVE, an R package for analyzing multi-source biomedical data. It enhances the Joint and Individual Variation Explained (JIVE) method for better data integration and visualization.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Integrative analysis of multi-source high-throughput data is crucial in biomedical research.
  • Joint and Individual Variation Explained (JIVE) is a dimension reduction tool for multi-source data.
  • Previous JIVE implementations had limited accessibility.

Purpose of the Study:

  • To introduce R.JIVE, an R package for performing JIVE analysis.
  • To provide an intuitive tool for exploring joint and individual variation in multi-source datasets.
  • To enhance the JIVE methodology with new features and extensions.

Main Methods:

  • Development of an R package implementing the JIVE methodology.
  • Incorporation of improvements and extensions to the JIVE algorithm.
  • Visualization tools for interpreting JIVE results.

Main Results:

  • R.JIVE offers an accessible implementation of the JIVE method.
  • The package facilitates the integrative analysis of multi-source data.
  • Demonstrated application on The Cancer Genome Atlas breast tumor data.

Conclusions:

  • R.JIVE democratizes the use of JIVE for biomedical researchers.
  • The package aids in uncovering complex patterns in multi-source omics data.
  • Facilitates exploratory data analysis for biological insights.