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A User-friendly and Powerful R Analysis of Large-scale Datasets
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Optimizing R with SparkR on a commodity cluster for biomedical research.

Martin Sedlmayr1, Tobias Würfl1, Christian Maier1

  • 1Friedrich-Alexander University Erlangen-Nürnberg, Wetterkreuz 13, 91058 Erlangen, Germany.

Computer Methods and Programs in Biomedicine
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Summary

Researchers can now perform computationally intensive genome-wide association studies (GWAS) faster using R. A new SparkR cluster solution significantly reduces analysis time on existing office computers, making big data analysis more accessible.

Keywords:
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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Healthcare generates vast amounts of data, posing challenges for analysis.
  • Genome-wide association studies (GWAS) are computationally intensive, requiring significant resources.
  • Dedicated clusters and public clouds have limitations like cost and data privacy concerns.

Purpose of the Study:

  • To explore building a private, flexible R cluster using non-dedicated hardware.
  • To compare the performance, scalability, quality, and simplicity of different R cluster setups for GWAS.

Main Methods:

  • Compared R script performance on a single desktop, Message Passing Interface (MPI)-cluster, and SparkR-cluster.
  • Evaluated computational time, scalability, and ease of implementation for each method.

Main Results:

  • Optimizing the R script reduced runtime from 3 years to 2 weeks.
  • Using R-MPI and SparkR reduced computation time to under 3 hours on standard office computers.
  • SparkR offers a dynamic, elastic environment with better scalability and failure handling than MPI.

Conclusions:

  • R is a key tool for clinical data analysis.
  • SparkR provides elastic resources for big data analysis in R on non-dedicated hardware with minimal code changes.
  • Further algorithm customization is recommended to optimize data distribution and fully leverage SparkR's potential.