A New Ensemble Method for Detecting Anomalies in Gene Expression Matrices

Laura Selicato, Flavia Esposito, Grazia Gargano, Maria Carmela Vegliante, Giuseppina Opinto, Gian Maria Zaccaria, Sabino Ciavarella, Attilio Guarini and Nicoletta Del Buono
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Laura Selicato: Department of Mathematics, University of Bari Aldo Moro, 70125 Bari, Italy
Flavia Esposito: Department of Mathematics, University of Bari Aldo Moro, 70125 Bari, Italy
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

Mathematics, 2021, vol. 9, issue 8, 1-26

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.

Keywords: anomaly; low rank decomposition; gene expression; clustering; outliers (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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