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Determination of essential phenotypic elements of clusters in high-dimensional entities—DEPECHE

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  • Axel Theorell
  • Yenan Troi Bryceson
  • Jakob Theorell

Abstract

Technological advances have facilitated an exponential increase in the amount of information that can be derived from single cells, necessitating new computational tools that can make such highly complex data interpretable. Here, we introduce DEPECHE, a rapid, parameter free, sparse k-means-based algorithm for clustering of multi- and megavariate single-cell data. In a number of computational benchmarks aimed at evaluating the capacity to form biologically relevant clusters, including flow/mass-cytometry and single cell RNA sequencing data sets with manually curated gold standard solutions, DEPECHE clusters as well or better than the currently available best performing clustering algorithms. However, the main advantage of DEPECHE, compared to the state-of-the-art, is its unique ability to enhance interpretability of the formed clusters, in that it only retains variables relevant for cluster separation, thereby facilitating computational efficient analyses as well as understanding of complex datasets. DEPECHE is implemented in the open source R package DepecheR currently available at github.com/Theorell/DepecheR.

Suggested Citation

  • Axel Theorell & Yenan Troi Bryceson & Jakob Theorell, 2019. "Determination of essential phenotypic elements of clusters in high-dimensional entities—DEPECHE," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-15, March.
    • Handle: RePEc:plo:pone00:0203247
    DOI: 10.1371/journal.pone.0203247
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    References listed on IDEAS

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    1. Amos Tanay & Aviv Regev, 2017. "Scaling single-cell genomics from phenomenology to mechanism," Nature, Nature, vol. 541(7637), pages 331-338, January.
    2. Witten, Daniela M. & Tibshirani, Robert, 2010. "A Framework for Feature Selection in Clustering," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 713-726.
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