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A New Ensemble Method for Detecting Anomalies in Gene Expression Matrices

Author

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  • Laura Selicato

    (Department of Mathematics, University of Bari Aldo Moro, 70125 Bari, Italy
    Member of GNCS, Istituto Nazionale di Alta Matematica, P.le Aldo Moro 5, 00185 Roma, Italy
    These authors contributed equally to this work.)

  • Flavia Esposito

    (Department of Mathematics, University of Bari Aldo Moro, 70125 Bari, Italy
    Member of GNCS, Istituto Nazionale di Alta Matematica, P.le Aldo Moro 5, 00185 Roma, Italy
    These authors contributed equally to this work.)

  • Grazia Gargano

    (Department of Mathematics, University of Bari Aldo Moro, 70125 Bari, Italy)

  • Maria Carmela Vegliante

    (Hematology and Cell Therapy Unit, IRCCS-Istituto Tumori ‘Giovanni Paolo II’, 70124 Bari, Italy)

  • Giuseppina Opinto

    (Hematology and Cell Therapy Unit, IRCCS-Istituto Tumori ‘Giovanni Paolo II’, 70124 Bari, Italy)

  • Gian Maria Zaccaria

    (Hematology and Cell Therapy Unit, IRCCS-Istituto Tumori ‘Giovanni Paolo II’, 70124 Bari, Italy)

  • Sabino Ciavarella

    (Hematology and Cell Therapy Unit, IRCCS-Istituto Tumori ‘Giovanni Paolo II’, 70124 Bari, Italy)

  • Attilio Guarini

    (Hematology and Cell Therapy Unit, IRCCS-Istituto Tumori ‘Giovanni Paolo II’, 70124 Bari, Italy)

  • Nicoletta Del Buono

    (Department of Mathematics, University of Bari Aldo Moro, 70125 Bari, Italy
    Member of GNCS, Istituto Nazionale di Alta Matematica, P.le Aldo Moro 5, 00185 Roma, Italy)

Abstract

One of the main problems in the analysis of real data is often related to the presence of anomalies. Namely, anomalous cases can both spoil the resulting analysis and contain valuable information at the same time. In both cases, the ability to detect these occurrences is very important. In the biomedical field, a correct identification of outliers could allow the development of new biological hypotheses that are not considered when looking at experimental biological data. In this work, we address the problem of detecting outliers in gene expression data, focusing on microarray analysis. We propose an ensemble approach for detecting anomalies in gene expression matrices based on the use of Hierarchical Clustering and Robust Principal Component Analysis, which allows us to derive a novel pseudo-mathematical classification of anomalies.

Suggested Citation

  • Laura Selicato & Flavia Esposito & Grazia Gargano & Maria Carmela Vegliante & Giuseppina Opinto & Gian Maria Zaccaria & Sabino Ciavarella & Attilio Guarini & Nicoletta Del Buono, 2021. "A New Ensemble Method for Detecting Anomalies in Gene Expression Matrices," Mathematics, MDPI, vol. 9(8), pages 1-26, April.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:8:p:882-:d:537378
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    References listed on IDEAS

    as
    1. Laura Pasqualucci & David Dominguez-Sola & Annalisa Chiarenza & Giulia Fabbri & Adina Grunn & Vladimir Trifonov & Lawryn H. Kasper & Stephanie Lerach & Hongyan Tang & Jing Ma & Davide Rossi & Amy Chad, 2011. "Inactivating mutations of acetyltransferase genes in B-cell lymphoma," Nature, Nature, vol. 471(7337), pages 189-195, March.
    2. Shieh Albert D & Hung Yeung Sam, 2009. "Detecting Outlier Samples in Microarray Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-26, February.
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    Cited by:

    1. Sareer Ul Amin & Mohib Ullah & Muhammad Sajjad & Faouzi Alaya Cheikh & Mohammad Hijji & Abdulrahman Hijji & Khan Muhammad, 2022. "EADN: An Efficient Deep Learning Model for Anomaly Detection in Videos," Mathematics, MDPI, vol. 10(9), pages 1-15, May.

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