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M3C: Monte Carlo reference-based consensus clustering.

Christopher R John1, David Watson2, Dominic Russ3

  • 1Experimental Medicine and Rheumatology, William Harvey Research Institute, Bart's and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, United Kingdom. christopher.john@qmul.ac.uk.

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|February 6, 2020
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Summary
This summary is machine-generated.

We developed Monte Carlo reference-based consensus clustering (M3C) to accurately determine the number of patient clusters (K) for precision medicine. M3C corrects bias in existing methods, improving patient stratification using genome-wide data.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Patient stratification using genome-wide data is crucial for precision medicine.
  • Clustering algorithms are employed to identify patient subgroups.
  • Estimating the optimal number of clusters (K) remains a significant challenge.

Purpose of the Study:

  • To address the bias and false positives associated with the Monti consensus clustering algorithm for selecting K.
  • To introduce Monte Carlo reference-based consensus clustering (M3C) as a novel solution.
  • To enable statistically robust determination of patient clusters for improved precision medicine.

Main Methods:

  • Developed Monte Carlo reference-based consensus clustering (M3C).
  • M3C simulates null distributions of stability scores for bias correction.
  • Utilized simulated multivariate Gaussian clusters (clusterlab) and The Cancer Genome Atlas (TCGA) expression data for validation.

Main Results:

  • M3C effectively corrects the inherent bias of consensus clustering methods.
  • Demonstrated improved accuracy in estimating K on both simulated and real genomic data.
  • M3C provides a statistically sound approach to identify patient subgroups.

Conclusions:

  • M3C offers a more reliable method for determining the number of clusters in genomic data analysis.
  • This advancement facilitates more accurate patient stratification for precision medicine.
  • M3C enhances the reliability of clustering in bioinformatics applications.