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cola: an R/Bioconductor package for consensus partitioning through a general framework.

Zuguang Gu1,2, Matthias Schlesner3, Daniel Hübschmann1,4,5,6

  • 1Computational Oncology, Molecular Diagnostics Program, National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.

Nucleic Acids Research
|December 4, 2020
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Summary
This summary is machine-generated.

The new R/Bioconductor package, cola, enhances consensus partitioning for high-throughput genomic data. It offers improved feature selection and subgroup classification, automating analysis and reporting for better biological insights.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput genomic data classification assigns samples to molecular subgroups.
  • Consensus partitioning is a key method for stable subgroup discovery.
  • Existing methods lack flexibility and comprehensive benchmarking.

Purpose of the Study:

  • Introduce the R/Bioconductor package 'cola' for flexible consensus partitioning.
  • Provide a general framework for user-defined analysis steps like feature selection and classification.
  • Benchmark new methods and parameters for improved performance.

Main Methods:

  • Implemented the 'cola' package in R/Bioconductor.
  • Developed the 'ATC' (ability to correlate to other rows) method for feature extraction.
  • Recommended spherical k-means (skmeans) for subgroup classification.
  • Conducted comprehensive benchmarking on public genomic datasets.

Main Results:

  • 'cola' offers a flexible framework for consensus partitioning.
  • The ATC method and skmeans show superior performance compared to existing methods.
  • Benchmarking identified optimal parameters for consensus partitioning.
  • The package provides parallel analysis, visualization, and automated HTML reporting.

Conclusions:

  • 'cola' provides a robust and user-friendly platform for genomic data analysis.
  • The package facilitates reproducible and efficient discovery of molecular subgroups.
  • ATC and skmeans represent advancements in feature selection and classification for genomics.