A user-driven machine learning approach for RNA-based sample discrimination and hierarchical classification
Summary: 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,...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
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| Series: | STAR Protocols |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666166723006287 |
| _version_ | 1797647572049854464 |
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| author | Tashifa Imtiaz Jina Nanayakkara Alexis Fang Danny Jomaa Harrison Mayotte Simona Damiani Fiza Javed Tristan Jones Emily Kaczmarek Flourish Omolara Adebayo Uroosa Imtiaz Yiheng Li Richard Zhang Parvin Mousavi Neil Renwick Kathrin Tyryshkin |
| author_facet | Tashifa Imtiaz Jina Nanayakkara Alexis Fang Danny Jomaa Harrison Mayotte Simona Damiani Fiza Javed Tristan Jones Emily Kaczmarek Flourish Omolara Adebayo Uroosa Imtiaz Yiheng Li Richard Zhang Parvin Mousavi Neil Renwick Kathrin Tyryshkin |
| author_sort | Tashifa Imtiaz |
| collection | DOAJ |
| description | Summary: 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. : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. |
| first_indexed | 2024-03-11T15:19:26Z |
| format | Article |
| id | doaj.art-b19026798e34484280e1c315a445cfe6 |
| institution | Directory Open Access Journal |
| issn | 2666-1667 |
| language | English |
| last_indexed | 2024-03-11T15:19:26Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | STAR Protocols |
| spelling | doaj.art-b19026798e34484280e1c315a445cfe62023-10-29T04:20:16ZengElsevierSTAR Protocols2666-16672023-12-0144102661A user-driven machine learning approach for RNA-based sample discrimination and hierarchical classificationTashifa Imtiaz0Jina Nanayakkara1Alexis Fang2Danny Jomaa3Harrison Mayotte4Simona Damiani5Fiza Javed6Tristan Jones7Emily Kaczmarek8Flourish Omolara Adebayo9Uroosa Imtiaz10Yiheng Li11Richard Zhang12Parvin Mousavi13Neil Renwick14Kathrin Tyryshkin15Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen’s University, 88 Stuart St, Kingston, ON K7L 3N6, Canada; Corresponding authorLaboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen’s University, 88 Stuart St, Kingston, ON K7L 3N6, CanadaLaboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen’s University, 88 Stuart St, Kingston, ON K7L 3N6, CanadaSchool of Medicine, Faculty of Health Sciences, Queen’s University, 80 Barrie St, Kingston, ON K7L 3N6, CanadaLaboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen’s University, 88 Stuart St, Kingston, ON K7L 3N6, CanadaLaboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen’s University, 88 Stuart St, Kingston, ON K7L 3N6, CanadaLaboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen’s University, 88 Stuart St, Kingston, ON K7L 3N6, CanadaLaboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen’s University, 88 Stuart St, Kingston, ON K7L 3N6, CanadaMedical Informatics Laboratory, School of Computing, Queen’s University, 557 Goodwin Hall, Kingston, ON K7L 2N8, CanadaMedical Informatics Laboratory, School of Computing, Queen’s University, 557 Goodwin Hall, Kingston, ON K7L 2N8, CanadaSchool of Computing, Queen’s University, 557 Goodwin Hall, Kingston, ON K7L 2N8, CanadaSchool of Computing, Queen’s University, 557 Goodwin Hall, Kingston, ON K7L 2N8, CanadaLaboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen’s University, 88 Stuart St, Kingston, ON K7L 3N6, CanadaMedical Informatics Laboratory, School of Computing, Queen’s University, 557 Goodwin Hall, Kingston, ON K7L 2N8, CanadaLaboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen’s University, 88 Stuart St, Kingston, ON K7L 3N6, CanadaLaboratory 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; Corresponding authorSummary: 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. : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.http://www.sciencedirect.com/science/article/pii/S2666166723006287BioinformaticsGene ExpressionRNAseqSequence AnalysisSequencing |
| spellingShingle | Tashifa Imtiaz Jina Nanayakkara Alexis Fang Danny Jomaa Harrison Mayotte Simona Damiani Fiza Javed Tristan Jones Emily Kaczmarek Flourish Omolara Adebayo Uroosa Imtiaz Yiheng Li Richard Zhang Parvin Mousavi Neil Renwick Kathrin Tyryshkin A user-driven machine learning approach for RNA-based sample discrimination and hierarchical classification STAR Protocols Bioinformatics Gene Expression RNAseq Sequence Analysis Sequencing |
| title | A user-driven machine learning approach for RNA-based sample discrimination and hierarchical classification |
| title_full | A user-driven machine learning approach for RNA-based sample discrimination and hierarchical classification |
| title_fullStr | A user-driven machine learning approach for RNA-based sample discrimination and hierarchical classification |
| title_full_unstemmed | A user-driven machine learning approach for RNA-based sample discrimination and hierarchical classification |
| title_short | A user-driven machine learning approach for RNA-based sample discrimination and hierarchical classification |
| title_sort | user driven machine learning approach for rna based sample discrimination and hierarchical classification |
| topic | Bioinformatics Gene Expression RNAseq Sequence Analysis Sequencing |
| url | http://www.sciencedirect.com/science/article/pii/S2666166723006287 |
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