A user-driven machine learning approach for RNA-based sample discrimination and hierarchical classification.

Journal: STAR protocols
Published Date: Oct 27, 2023

Abstract

RNA-based sample discrimination and classification can be used to provide biological insights and/or distinguish between clinical groups. However, finding informative differences between sample groups can be challenging due to the multidimensional and noisy nature of sequencing data. Here, we apply a machine learning approach for hierarchical discrimination and classification of samples with high-dimensional miRNA expression data. Our protocol comprises data preprocessing, unsupervised learning, feature selection, and machine-learning-based hierarchical classification, alongside open-source MATLAB code.

Authors

  • Tashifa Imtiaz
    Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen's University, 88 Stuart St, Kingston, ON K7L 3N6, Canada. Electronic address: 17ti6@queensu.ca.
  • Jina Nanayakkara
    Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen's University, 88 Stuart St, Kingston, ON K7L 3N6, Canada.
  • Alexis Fang
    Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen's University, 88 Stuart St, Kingston, ON K7L 3N6, Canada.
  • Danny Jomaa
    School of Medicine, Faculty of Health Sciences, Queen's University, 80 Barrie St, Kingston, ON K7L 3N6, Canada.
  • Harrison Mayotte
    Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen's University, 88 Stuart St, Kingston, ON K7L 3N6, Canada.
  • Simona Damiani
    Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen's University, 88 Stuart St, Kingston, ON K7L 3N6, Canada.
  • Fiza Javed
    Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen's University, 88 Stuart St, Kingston, ON K7L 3N6, Canada.
  • Tristan Jones
    Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen's University, 88 Stuart St, Kingston, ON K7L 3N6, Canada.
  • Emily Kaczmarek
    Medical Informatics Laboratory, School of Computing, Queen's University, 557 Goodwin Hall, Kingston, ON K7L 2N8, Canada.
  • Flourish Omolara Adebayo
    Medical Informatics Laboratory, School of Computing, Queen's University, 557 Goodwin Hall, Kingston, ON K7L 2N8, Canada.
  • Uroosa Imtiaz
    School of Computing, Queen's University, 557 Goodwin Hall, Kingston, ON K7L 2N8, Canada.
  • Yiheng Li
    School of Computing, Queen's University, 557 Goodwin Hall, Kingston, ON K7L 2N8, Canada.
  • Richard Zhang
    Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen's University, 88 Stuart St, Kingston, ON K7L 3N6, Canada.
  • Parvin Mousavi
    Medical Informatics Laboratory, School of Computing, Queen's University, 557 Goodwin Hall, Kingston, ON K7L 2N8, Canada.
  • Neil Renwick
    Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen's University, 88 Stuart St, Kingston, ON K7L 3N6, Canada.
  • Kathrin Tyryshkin
    Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen's University, 88 Stuart St, Kingston, ON K7L 3N6, Canada; School of Computing, Queen's University, 557 Goodwin Hall, Kingston, ON K7L 2N8, Canada. Electronic address: kt40@queensu.ca.

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