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

View ORCID ProfileAxel Theorell, Yenan Troi Bryceson, Jakob Theorell
doi: https://doi.org/10.1101/396135
Axel Theorell
1IBG-1: Biotechnology, Institute of Bio-and Geosciences, Forschungszentrum Jülich GmbH, Jülich, North Rhine-Westphalia, Germany
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  • ORCID record for Axel Theorell
Yenan Troi Bryceson
2Center for Hemotology and Regenerative Medicine, Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
3Broegelmann Research Laboratory, Department of Clinical Medicine, University of Bergen, Bergen, Norway
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Jakob Theorell
2Center for Hemotology and Regenerative Medicine, Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
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  • For correspondence: Jakob.Theorell@ki.se
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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 this 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 as the best performing state-of-the-art 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. An open source R implementation of DEPECHE is available at https://github.com/theorell/DepecheR.

Author summary DEPECHE-a data-mining algorithm for mega-variate data

Modern experimental technologies facilitate an array of single cells measurements, e.g. at the RNA-level, generating enormous datasets with thousands of annotated biological markers for each of thousands of cells. To analyze such datasets, researchers routinely apply automated or semi-automated techniques to order the cells into biologically relevant groups. However, even after such groups have been generated, it is often difficult to interpret the biological meaning of these groups since the definition of each group often dependends on thousands of biological markers. Therefore, in this article, we introduce DEPECHE, an algorithm designed to simultaneously group cells and enhance interpretability of the formed groups. DEPECHE defines groups only with respect to biological markers that contribute significantly to differentiate the cells in the group from the rest of the cells, yielding more succinct group definitions. Using the open source R software DepecheR on RNA sequencing data and mass cytometry data, the number of defining markers were reduced up to 1000-fold, thereby increasing interpretability vastly, while maintaining or improving the biological relevance of the groups formed compared to state-of-the-art algorithms.

Author contributions

A.T. drafted mathematical models, coded the software implementation and co-wrote the manuscript, Y.T.B. co-wrote the manuscript, J.T. drafted mathematical models, coded software implementation and wrote the manuscript.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted August 20, 2018.
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Determination of essential phenotypic elements of clusters in high-dimensional entities - DEPECHE
Axel Theorell, Yenan Troi Bryceson, Jakob Theorell
bioRxiv 396135; doi: https://doi.org/10.1101/396135
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Determination of essential phenotypic elements of clusters in high-dimensional entities - DEPECHE
Axel Theorell, Yenan Troi Bryceson, Jakob Theorell
bioRxiv 396135; doi: https://doi.org/10.1101/396135

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