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Regression plane concept for analysing continuous cellular processes with machine learning

Author

Listed:
  • Abel Szkalisity

    (Synthetic and Systems Biology Unit, Biological Research Centre (BRC)
    University of Helsinki)

  • Filippo Piccinini

    (Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS)

  • Attila Beleon

    (Synthetic and Systems Biology Unit, Biological Research Centre (BRC))

  • Tamas Balassa

    (Synthetic and Systems Biology Unit, Biological Research Centre (BRC))

  • Istvan Gergely Varga

    (Institute of Genetics, Biological Research Center (BRC))

  • Ede Migh

    (Synthetic and Systems Biology Unit, Biological Research Centre (BRC))

  • Csaba Molnar

    (Synthetic and Systems Biology Unit, Biological Research Centre (BRC))

  • Lassi Paavolainen

    (University of Helsinki)

  • Sanna Timonen

    (University of Helsinki)

  • Indranil Banerjee

    (Indian Institute of Science Education and Research (IISER))

  • Elina Ikonen

    (University of Helsinki)

  • Yohei Yamauchi

    (University of Bristol, BS8 1TD University Walk)

  • Istvan Ando

    (Institute of Genetics, Biological Research Center (BRC))

  • Jaakko Peltonen

    (Tampere University, FI-33014 Tampere University
    Aalto University)

  • Vilja Pietiäinen

    (University of Helsinki)

  • Viktor Honti

    (Institute of Genetics, Biological Research Center (BRC))

  • Peter Horvath

    (Synthetic and Systems Biology Unit, Biological Research Centre (BRC)
    University of Helsinki
    Single-Cell Technologies Ltd.)

Abstract

Biological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them. Using multi-parametric active regression we introduce the Regression Plane (RP), a user-friendly discovery tool enabling class-free phenotypic supervised machine learning, to describe and explore biological data in a continuous manner. First, we compare traditional classification with regression in a simulated experimental setup. Second, we use our framework to identify genes involved in regulating triglyceride levels in human cells. Subsequently, we analyse a time-lapse dataset on mitosis to demonstrate that the proposed methodology is capable of modelling complex processes at infinite resolution. Finally, we show that hemocyte differentiation in Drosophila melanogaster has continuous characteristics.

Suggested Citation

  • Abel Szkalisity & Filippo Piccinini & Attila Beleon & Tamas Balassa & Istvan Gergely Varga & Ede Migh & Csaba Molnar & Lassi Paavolainen & Sanna Timonen & Indranil Banerjee & Elina Ikonen & Yohei Yama, 2021. "Regression plane concept for analysing continuous cellular processes with machine learning," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22866-x
    DOI: 10.1038/s41467-021-22866-x
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