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Volume 9
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Article

A New Ensemble Method for Detecting Anomalies in Gene Expression Matrices

by
Laura Selicato
1,2,*,†,
Flavia Esposito
1,2,†,
Grazia Gargano
1,
Maria Carmela Vegliante
3,
Giuseppina Opinto
3,
Gian Maria Zaccaria
3,
Sabino Ciavarella
3,
Attilio Guarini
3 and
Nicoletta Del Buono
1,2
1
Department of Mathematics, University of Bari Aldo Moro, 70125 Bari, Italy
2
Member of GNCS, Istituto Nazionale di Alta Matematica, P.le Aldo Moro 5, 00185 Roma, Italy
3
Hematology and Cell Therapy Unit, IRCCS-Istituto Tumori ‘Giovanni Paolo II’, 70124 Bari, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Mathematics 2021, 9(8), 882; https://doi.org/10.3390/math9080882
Submission received: 1 March 2021 / Revised: 29 March 2021 / Accepted: 14 April 2021 / Published: 16 April 2021
(This article belongs to the Special Issue Computational Approaches for Data Inspection in Biomedicine)
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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 anomaly; low rank decomposition; gene expression; clustering; outliers

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MDPI and ACS Style

Selicato, L.; Esposito, F.; Gargano, G.; Vegliante, M.C.; Opinto, G.; Zaccaria, G.M.; Ciavarella, S.; Guarini, A.; Del Buono, N. A New Ensemble Method for Detecting Anomalies in Gene Expression Matrices. Mathematics 2021, 9, 882. https://doi.org/10.3390/math9080882

AMA Style

Selicato L, Esposito F, Gargano G, Vegliante MC, Opinto G, Zaccaria GM, Ciavarella S, Guarini A, Del Buono N. A New Ensemble Method for Detecting Anomalies in Gene Expression Matrices. Mathematics. 2021; 9(8):882. https://doi.org/10.3390/math9080882

Chicago/Turabian Style

Selicato, Laura, Flavia Esposito, Grazia Gargano, Maria Carmela Vegliante, Giuseppina Opinto, Gian Maria Zaccaria, Sabino Ciavarella, Attilio Guarini, and Nicoletta Del Buono. 2021. "A New Ensemble Method for Detecting Anomalies in Gene Expression Matrices" Mathematics 9, no. 8: 882. https://doi.org/10.3390/math9080882

APA Style

Selicato, L., Esposito, F., Gargano, G., Vegliante, M. C., Opinto, G., Zaccaria, G. M., Ciavarella, S., Guarini, A., & Del Buono, N. (2021). A New Ensemble Method for Detecting Anomalies in Gene Expression Matrices. Mathematics, 9(8), 882. https://doi.org/10.3390/math9080882

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